Post on 03-Apr-2018
transcript
7/28/2019 ANN and Drives IEEE Papers
1/26
Toma, S.; Capocchi, L.; Capolino, G.-A., "Wound-Rotor Induction Generator Inter-Turn Short-Circuits
Diagnosis Using a New Digital Neural Network," Industrial Electronics, IEEE Transactions on , vol.60,
no.9, pp.4043,4052, Sept. 2013
doi: 10.1109/TIE.2012.2229675
Abstract: This paper deals with a new transformation and fusion of digital input patterns used to train
and test feedforward neural network for a wound-rotor three-phase induction machine windings
short-circuit diagnosis. The single type of short-circuits tested by the proposed approach is based on
turn-to-turn fault which is known as the first stage of insulation degradation. Used input/output data
have been binary coded in order to reduce the computation complexity. A new procedure, namely
addition and mean of the set of same rank, has been implemented to eliminate the redundancy due to
the periodic character of input signals. However, this approach has a great impact on the statistical
properties on the processed data in terms of richness and of statistical distribution. The proposed
neural network has been trained and tested with experimental signals coming from six current sensors
implemented around a setup with a prime mover and a 5.5 kW wound-rotor three-phase induction
generator. Both stator and rotor windings have been modified in order to sort out first and last turns
in each phase. The experimental results highlight the superiority of using this new procedure in bothtraining and testing modes.
keywords: {asynchronous generators;electric machine analysis computing;fault diagnosis;machine
insulation;neural nets;power generation faults;binary code;current sensor;digital input pattern;digital
neural network;insulation degradation;interturn short circuit diagnosis;power 5.5 kW;prime
mover;turn-to-turn fault;wound rotor induction generator;wound rotor three phase induction
generator;Artificial neural networks;Neurons;Rotors;Sensors;Stator
windings;Training;Backpropagation;data preprocessing;digital measurements;fault
diagnosis;feedforward neural network;induction generators;rotor current;stator current;winding
short-circuits},
URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6359911&isnumber=6512583
Opathella, C.; Singh, B; Cheng, D.; Venkatesh, B., "Intelligent Wind Generator Models for Power Flow
Studies in PSSE and PSSSINCAL," Power Systems, IEEE Transactions on , vol.28, no.2, pp.1149,1159,
May 2013
doi: 10.1109/TPWRS.2012.2211043
Abstract: Wind generator (WG) output is a function of wind speed and three-phase terminal voltage.
Distribution systems are predominantly unbalanced. A WG model that is purely a function of windspeed is simple to use with unbalanced three-phase power flow analysis but the solution is inaccurate.
These errors add up and become pronounced when a single three-phase feeder connects several WGs.
Complete nonlinear three-phase WG models are accurate but are slow and unsuitable for power flow
applications. This paper proposes artificial neural network (ANN) models to represent type-3 doubly-
fed induction generator and type-4 permanent magnet synchronous generator. The proposed
approach can be readily applied to any other type of WGs. The main advantages of these ANN models
are their mathematical simplicity, high accuracy with unbalanced systems and computational speed.
These models were tested with the IEEE 37-bus test system. The results show that the ANN WG
models are computationally ten times faster than nonlinear accurate models. In addition, simplicity of
the proposed ANN WG models allow easy integration into commercial software packages such as
PSSE and PSSSINCAL and implementations are also shown in this paper.
keywords: {Artificial neural networks;Biological system modeling;Computational
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6359911&isnumber=6512583http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6359911&isnumber=6512583http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6359911&isnumber=6512583http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6359911&isnumber=65125837/28/2019 ANN and Drives IEEE Papers
2/26
modeling;Generators;Mathematical model;Neurons;Wind speed;Artificial neural networks;power
distribution systems;power flow;wind power generators},
URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6302218&isnumber=6504806
Gastli, A.; Ahmed, M.M., "ANN-Based Soft Starting of Voltage-Controlled-Fed IM Drive System," Energy
Conversion, IEEE Transactions on , vol.20, no.3, pp.497,503, Sept. 2005
doi: 10.1109/TEC.2004.841522
Abstract: Soft starters are used as induction motor controllers in compressors, blowers, fans, pumps,
mixers, crushers and grinders, and many other applications. Soft starters use ac voltage controllers to
start the induction motor and to adjust its speed. This paper presents a novel artifical neural network
(ANN)-based ac voltage controller which generates the appropriate thyristors' firing angle for any
given operating torque and speed of the motor and the load. An ANN model was designed for that
purpose. The results obtained are very satisfactory and promising. The advantage of such a controller
are its simplicity, stability, and high accuracy compared to conventional mathematical calculation of
the firing angle which is a very complex and time consuming task especially in online control
applications.keywords: {induction motor drives;machine control;neurocontrollers;starting;thyristors;torque;voltage
control;ANN-based soft starting;artificial neural
networks;blowers;compressors;crushers;fans;grinders;induction motor;induction motor
controllers;mixers;online control applications;pumps;thyristor firing angle;torque;voltage controlled-
fed IM drive system;AC generators;Compressors;Fans;Grinding machines;Induction
generators;Induction motors;Neural networks;Thyristors;Torque control;Voltage control;AC voltage
controller;artificial neural network (ANN);firing angle;induction motor;soft starter;thyristor},
URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1495520&isnumber=32134
Malerba, D.; Esposito, F.; Ceci, M.; Appice, A., "Top-down induction of model trees with regression and
splitting nodes," Pattern Analysis and Machine Intelligence, IEEE Transactions on , vol.26, no.5,
pp.612,625, May 2004
doi: 10.1109/TPAMI.2004.1273937
Abstract: Model trees are an extension of regression trees that associate leaves with multiple
regression models. In this paper, a method for the data-driven construction of model trees is
presented, namely, the stepwise model tree induction (SMOTI) method. Its main characteristic is the
induction of trees with two types of nodes: regression nodes, which perform only straight-lineregression, and splitting nodes, which partition the feature space. The multiple linear model associated
with each leaf is then built stepwise by combining straight-line regressions reported along the path
from the root to the leaf. In this way, internal regression nodes contribute to the definition of multiple
models and have a "global" effect, while straight-line regressions at leaves have only "local" effects.
Experimental results on artificially generated data sets show that SMOTI outperforms two model tree
induction systems, M5' and RETIS, in accuracy. Results on benchmark data sets used for studies on
both regression and model trees show that SMOTI performs better than RETIS in accuracy, while it is
not possible to draw statistically significant conclusions on the comparison with M5'. Model trees
induced by SMOTI are generally simple and easily interpretable and their analysis often reveals
interesting patterns.
keywords: {learning by example;regression analysis;trees (mathematics);benchmark data sets;data
driven construction;global effect;internal regression nodes;learning by example;multiple linear
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6302218&isnumber=6504806http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6302218&isnumber=6504806http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6302218&isnumber=6504806http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1495520&isnumber=32134http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1495520&isnumber=32134http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1495520&isnumber=32134http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1495520&isnumber=32134http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6302218&isnumber=65048067/28/2019 ANN and Drives IEEE Papers
3/26
model;multiple regression models;regression trees;splitting nodes;stepwise model tree induction
method;straight line regression;top down induction;Induction generators;Linear regression;Machine
learning;Neural networks;Pattern analysis;Piecewise linear approximation;Piecewise linear
techniques;Regression tree analysis;Statistics;Tree data structures;Algorithms;Artificial
Intelligence;Cluster Analysis;Computer Simulation;Decision Support Techniques;Information Storage
and Retrieval;Numerical Analysis, Computer-Assisted;Pattern Recognition, Automated;Reproducibility
of Results;Sensitivity and Specificity},
URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1273937&isnumber=28505
Tsang, E. C C; Yeung, D.S.; Lee, J.W.T.; Huang, D. M.; Wang, X. Z., "Refinement of generated fuzzy
production rules by using a fuzzy neural network," Systems, Man, and Cybernetics, Part B: Cybernetics,
IEEE Transactions on , vol.34, no.1, pp.409,418, Feb. 2004
doi: 10.1109/TSMCB.2003.817033
Abstract: Fuzzy production rules (FPRs) have been used for years to capture and represent fuzzy,
vague, imprecise and uncertain domain knowledge in many fuzzy systems. There have been a lot of
researches on how to generate or obtain FPRs. There exist two methods to obtain FPRs. One is bypainstakingly, repeatedly and time-consuming interviewing domain experts to extract the domain
knowledge. The other is by using some machine learning techniques to generate and extract FPRs from
some training samples. These extracted rules, however, are found to be nonoptimal and sometimes
redundant. Furthermore, these generated rules suffer from the problem of low accuracy of classifying
or recognizing unseen examples. The reasons for having these problems are: 1) the FPRs generated are
not powerful enough to represent the domain knowledge, 2) the techniques used to generate FPRs are
pre-matured, ad-hoc or may not be suitable for the problem, and 3) further refinement of the
extracted rules has not been done. In this paper we look into the solutions of the above problems by
1) enhancing the representation power of FPRs by including local and global weights, 2) developing a
fuzzy neural network (FNN) with enhanced learning algorithm, and 3) using this FNN to refine the local
and global weights of FPRs. By experimenting our method with some existing benchmark examples,
the proposed method is found to have high accuracy in classifying unseen samples without increasing
the number of the FPRs extracted and the time required to consult with domain experts is greatly
reduced.
keywords: {fuzzy neural nets;fuzzy systems;knowledge acquisition;knowledge based
systems;knowledge representation;learning (artificial intelligence);fuzzy neural network;fuzzy
production rules;fuzzy systems;machine learning techniques;uncertain domain knowledge;Automaticcontrol;Fuzzy control;Fuzzy logic;Fuzzy neural networks;Fuzzy systems;Induction generators;Machine
learning;Power generation;Production;Refining},
URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1262513&isnumber=28229
Karanayil, B.; Rahman, M.F.; Grantham, C., "An implementation of a programmable cascaded low-pass
filter for a rotor flux synthesizer for an induction motor drive,"Power Electronics, IEEE Transactions on ,
vol.19, no.2, pp.257,263, March 2004
doi: 10.1109/TPEL.2003.823181
Abstract: This paper investigates a programmable cascaded low pass filter for the estimation of rotor
flux of an induction motor, with a view to estimate the rotor time constant of an indirect field
orientation controlled induction motor drive. Programmable cascaded low pass filters have been
traditionally used in stator flux oriented vector control of the induction motor. This paper extends the
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1273937&isnumber=28505http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1273937&isnumber=28505http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1273937&isnumber=28505http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1262513&isnumber=28229http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1262513&isnumber=28229http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1262513&isnumber=28229http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1262513&isnumber=28229http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1273937&isnumber=285057/28/2019 ANN and Drives IEEE Papers
4/26
use of this filter to estimate the rotor flux for the indirect field orientation control by generating rotor
flux estimates from stator flux estimates. This is achieved by using a three-stage programmable
cascaded low pass filter. The three-stage programmable cascaded low-pass filter investigated in this
paper has resulted in excellent estimation of rotor flux in the steady-state and transient operation of
an indirect field oriented drive. The estimated rotor flux data have also been used for the on-line rotor
resistance identification with artificial neural network. Modeling and experiment results presented in
this paper demonstrate this method of estimating rotor flux clearly.
keywords: {cascade networks;induction motor drives;low-pass filters;machine vector control;magnetic
flux;neural nets;power engineering computing;programmable filters;rotors;artificial neural
networks;indirect field orientation control;induction motor drive;online rotor resistance
identification;programmable cascaded low-pass filter;rotor flux synthesizer;stator flux oriented vector
control;Induction generators;Induction motor drives;Induction motors;Low pass filters;Machine vector
control;Position control;Rotors;Stators;Steady-state;Synthesizers},
URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1271307&isnumber=28467
Mohamadian, M.; Nowicki, E.; Ashrafzadeh, F.; Chu, A.; Sachdeva, R.; Evanik, E., "A novel neuralnetwork controller and its efficient DSP implementation for vector-controlled induction motor
drives," Industry Applications, IEEE Transactions on , vol.39, no.6, pp.1622,1629, Nov.-Dec. 2003
doi: 10.1109/TIA.2003.819441
Abstract: An artificial neural network controller is experimentally implemented on the Texas
Instruments TMS320C30 digital signal processor (DSP). The controller emulates indirect field-oriented
control for an induction motor, generating direct and quadrature current command signals in the
stationary frame. In this way, the neural network performs the critical functions of slip estimation and
matrix rotation internally. There are five input signals to the neural network controller, namely, a shaft
speed signal, the synchronous frame present and delayed values of the quadrature axis stator current,
as well as two neural network output signals fed back after a delay of one sample period. The
proposed three-layer neural network controller contains only 17 neurons in an attempt to minimize
computational requirements of the digital signal processor. This allows DSP resources to be used for
other control purposes and system functions. For experimental investigation, a sampling period of 1
ms is employed. Operating at 33.3 MHz (16.7 MIPS), the digital signal processor is able to perform all
neural network calculations in a total time of only 280 s or only 4700 machine instructions. Torque
pulsations are initially observed, but are reduced by iterative re-training of the neural network using
experimental data. The resulting motor speed step response (for several forward and reverse stepcommands) quickly tracks the expected response, with negligible error under steady-state conditions.
keywords: {digital control;digital signal processing chips;induction motor drives;learning (artificial
intelligence);machine vector control;multilayer perceptrons;neurocontrollers;stators;1 ms;280
mus;33.3 MHz;DSP implementation;DSP resources;Texas Instruments TMS320C30 digital signal
processor;computational requirements minimisation;direct current command signals;forward step
commands;indirect field-oriented control;induction motor;iterative re-training;matrix rotation;motor
speed step response;neural network controller;neural network output signals;quadrature axis stator
current;quadrature current command signals;reverse step commands;shaft speed signal;slip
estimation;stationary frame;steady-state conditions;synchronous frame;three-layer neural network
controller;torque pulsations;vector-controlled induction motor drives;Artificial neural networks;DC
generators;Digital signal processing;Digital signal processors;Induction generators;Induction motor
drives;Induction motors;Instruments;Neural networks;Signal generators},
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1271307&isnumber=28467http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1271307&isnumber=28467http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1271307&isnumber=28467http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1271307&isnumber=284677/28/2019 ANN and Drives IEEE Papers
5/26
URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1248245&isnumber=27952
Karayaka, H.; Keyhani, A.; Heydt, G.; Agrawal, B.; Selin, D., "Neural Network-Based Modeling of a Large
Steam Turbine-Generator Rotor Body Parameters from Online Disturbance Data," Power Engineering
Review, IEEE, vol.21, no.9, pp.62,62, Sept. 2001
doi: 10.1109/MPER.2001.4311621
Abstract: A novel technique to estimate and model rotor-body parameters of a large steam turbine
generator from real time disturbance data is presented. For each set of disturbance data collected at
different operating conditions, the rotor body parameters of the generator are estimated using an
output error method (OEM). Artificial neural network (ANN)-based estimators are later used to model
the nonlinearities in the estimated parameters based on the generator operating conditions. The
developed ANN models are then validated with measurements not used in the training procedure. The
performance of estimated parameters is also validated with extensive simulations and compared
against the manufacturer values.
keywords: {Artificial neural networks;Circuit simulation;Equivalent circuits;Fault detection;Induction
generators;Induction motors;Neural networks;Parameter estimation;Rotors;Voltage;Parameteridentification;artificial neural networks;large utility generators;rotor body parameters},
URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4311621&isnumber=20535
Tsang, E. C C; Wang, X.Z.; Yeung, D.S., "Improving learning accuracy of fuzzy decision trees by hybrid
neural networks," Fuzzy Systems, IEEE Transactions on , vol.8, no.5, pp.601,614, Oct 2000
doi: 10.1109/91.873583
Abstract: Although the induction of fuzzy decision tree (FDT) has been a very popular learning
methodology due to its advantage of comprehensibility, it is often criticized to result in poor learning
accuracy. Thus, one fundamental problem is how to improve the learning accuracy while the
comprehensibility is kept. This paper focuses on this problem and proposes using a hybrid neural
network (HNN) to refine the FDT. This HNN, designed according to the generated FDT and trained by
an algorithm derived in this paper, results in a FDT with parameters, called weighted FDT. The
weighted FDT is equivalent to a set of fuzzy production rules with local weights (LW) and global
weights (GW) introduced in our previous work (1998). Moreover, the weighted FDT, in which the
reasoning mechanism incorporates the trained LW and GW, significantly improves the FDTs' learning
accuracy while keeping the FDT comprehensibility. The improvements are verified on several selected
databases. Furthermore, a brief comparison of our method with two benchmark learning algorithms,namely, fuzzy ID3 and traditional backpropagation, is made. The synergy between FDT induction and
HNN training offers new insight into the construction of hybrid intelligent systems with higher learning
accuracy
keywords: {decision trees;fuzzy set theory;learning (artificial intelligence);neural nets;FDT;FDT
induction;GW;HNN;HNN training;LW;backpropagation;comprehensibility;fuzzy ID3;fuzzy decision tree
induction;fuzzy production rules;global weights;hybrid intelligent systems;hybrid neural
networks;learning accuracy;local weights;Algorithm design and analysis;Databases;Decision
trees;Entropy;Fuzzy neural networks;Fuzzy sets;Induction generators;Knowledge acquisition;Neural
networks;Production},
URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=873583&isnumber=18902
Lavrac, N.; Ganberger, D.; Turney, P., "A relevancy filter for constructive induction," Intelligent Systems
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1248245&isnumber=27952http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1248245&isnumber=27952http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1248245&isnumber=27952http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4311621&isnumber=20535http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4311621&isnumber=20535http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4311621&isnumber=20535http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=873583&isnumber=18902http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=873583&isnumber=18902http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=873583&isnumber=18902http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=873583&isnumber=18902http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4311621&isnumber=20535http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1248245&isnumber=279527/28/2019 ANN and Drives IEEE Papers
6/26
and their Applications, IEEE, vol.13, no.2, pp.50,56, Mar/Apr 1998
doi: 10.1109/5254.671092
Abstract: Some machine-learning algorithms enable the learner to extend its vocabulary with new
terms if, for a given a set of training examples, the learner's vocabulary is too restricted to solve the
learning task. We propose a filter, called the Reduce algorithm, that selects potentially relevant terms
from the set of constructed terms and eliminates terms that are irrelevant for the learning task.
Restricting constructive induction (or predicate invention) to relevant terms allows a much larger
explored space of constructed terms. The elimination of irrelevant terms is especially well-suited for
learners of large time or space complexity, such as genetic algorithms and artificial neural networks. To
illustrate our approach to feature construction and irrelevant feature elimination, we applied our
proposed relevancy filter to the 20- and 24-train East-West Challenge problems. The experiments
show that the performance of a hybrid genetic algorithm, RL-ICET (Relational Learning with ICET),
improved significantly when we applied the relevancy filter while pre-processing the data set
keywords: {computational complexity;feature extraction;filtering theory;genetic algorithms;learning
by example;relevance feedback;vocabulary;East-West Challenge problems;RL-ICET;Reduce
algorithm;constructed term space;constructive induction;data set pre-processing;extendablevocabulary;feature construction;hybrid genetic algorithm;irrelevant feature elimination;machine-
learning algorithm;performance;predicate invention;relational learning;relevancy filter;relevant
terms;space complexity;time complexity;training examples;Artificial neural networks;Computer aided
software engineering;Councils;Filters;Genetic algorithms;Induction generators;Machine learning;Space
exploration;Switches;Vocabulary},
URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=671092&isnumber=14787
Simoes, M.G.; Bose, B.K.; Spiegel, Ronald J., "Design and performance evaluation of a fuzzy-logic-based
variable-speed wind generation system," Industry Applications, IEEE Transactions on , vol.33, no.4,
pp.956,965, Jul/Aug 1997
doi: 10.1109/28.605737
Abstract: Artificial intelligence techniques, such as fuzzy logic, neural networks and genetic algorithms,
have recently shown promise in the application of power electronic systems. The paper describes the
control strategy development, design and experimental performance evaluation of a fuzzy logic-based
variable-speed wind generation system that uses a cage-type induction generator and double-sided
PWM power converters. The system can feed a utility grid maintaining unity power factor at all
conditions or can supply an autonomous load. The fuzzy logic-based control of the system helps tooptimize efficiency and enhance performance. A complete 3.5 kW generation system has been
developed, designed and thoroughly evaluated by laboratory tests in order to validate the predicted
performance improvements. The system gives excellent performance and can easily be translated to a
larger size in the field
keywords: {AC-AC power convertors;PWM power convertors;asynchronous generators;control system
synthesis;fuzzy control;machine control;machine testing;power station control;wind power
plants;wind turbines;3.5 kW;PWM AC-AC power conversion;cage-type induction generator;control
performance;control strategy development;double-sided PWM power converter;fuzzy logic
control;performance evaluation;unity power factor;utility grid;variable-speed wind generation
system;Artificial intelligence;Artificial neural networks;Control systems;Electric variables control;Fuzzy
control;Fuzzy logic;Fuzzy systems;Genetic algorithms;Induction generators;Power electronics},
URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=605737&isnumber=13302
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=671092&isnumber=14787http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=671092&isnumber=14787http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=671092&isnumber=14787http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=605737&isnumber=13302http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=605737&isnumber=13302http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=605737&isnumber=13302http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=605737&isnumber=13302http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=671092&isnumber=147877/28/2019 ANN and Drives IEEE Papers
7/26
Angeline, P.J.; Saunders, G.M.; Pollack, J.B., "An evolutionary algorithm that constructs recurrent
neural networks," Neural Networks, IEEE Transactions on , vol.5, no.1, pp.54,65, Jan 1994
doi: 10.1109/72.265960
Abstract: Standard methods for simultaneously inducing the structure and weights of recurrent neural
networks limit every task to an assumed class of architectures. Such a simplification is necessary since
the interactions between network structure and function are not well understood. Evolutionary
computations, which include genetic algorithms and evolutionary programming, are population-based
search methods that have shown promise in many similarly complex tasks. This paper argues that
genetic algorithms are inappropriate for network acquisition and describes an evolutionary program,
called GNARL, that simultaneously acquires both the structure and weights for recurrent networks.
GNARL's empirical acquisition method allows for the emergence of complex behaviors and topologies
that are potentially excluded by the artificial architectural constraints imposed in standard network
induction methods
keywords: {optimisation;recurrent neural nets;GNARL;evolutionary algorithm;evolutionary
programming;genetic algorithms;population-based search methods;recurrent neuralnetworks;Artificial intelligence;Ash;Computer architecture;Evolutionary computation;Genetic
algorithms;Genetic programming;Induction generators;Network topology;Recurrent neural
networks;Search methods},
URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=265960&isnumber=6672
Umbaugh, S.E.; Moss, R.H.; Stoecker, W.V., "Applying artificial intelligence to the identification of
variegated coloring in skin tumors," Engineering in Medicine and Biology Magazine, IEEE, vol.10, no.4,
pp.57,62, Dec. 1991
doi: 10.1109/51.107171
Abstract: The importance of color information for the automatic diagnosis of skin tumors by computer
vision is demonstrated. The utility of the relative color concept is proved by the results in identifying
variegated coloring. A feature file paradigm is shown to provide an effective methodology for the
independent development of software modules for expert system/computer vision research. An
automatic induction tool is used effectively to generate rules for identifying variegated coloring.
Variegated coloring can be identified at rates as high as 92% when using the automatic induction
technique in conjunction with the color segmentation method.
keywords: {artificial intelligence;computer vision;expert systems;medical diagnosticcomputing;skin;artificial intelligence;automatic diagnosis;automatic induction tool;color
information;color segmentation method;computer vision;expert system;feature file paradigm;skin
tumors;software modules;variegated coloring identification;Artificial intelligence;Cancer;Classification
algorithms;Decision trees;Expert systems;Humans;Induction generators;Neural networks;Skin
neoplasms;Virtual colonoscopy},
URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=107171&isnumber=3269
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=265960&isnumber=6672http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=265960&isnumber=6672http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=265960&isnumber=6672http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=107171&isnumber=3269http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=107171&isnumber=3269http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=107171&isnumber=3269http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=107171&isnumber=3269http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=265960&isnumber=66727/28/2019 ANN and Drives IEEE Papers
8/26
Mesemanolis, Athanasios; Mademlis, Christos, "A Neural Network based MPPT controller for variable
speed Wind Energy Conversion Systems," Power Generation, Transmission, Distribution and Energy
Conversion (MEDPOWER 2012), 8th Mediterranean Conference on , vol., no., pp.1,6, 1-3 Oct. 2012
doi: 10.1049/cp.2012.2034
Abstract: In this paper, an Artificial Neural Network (ANN) based Maximum Power Point Tracking
(MPPT) controller for Wind Energy Conversion Systems (WECS) is proposed, that achieves fast and
reliable tracking of the optimum rotational speed of the turbine and accomplishes maximum power
harvesting from the incident wind. The proposed control system can be implemented on any WECS
and requires minimum training for the ANN as well as a small number of artificial neurons. During the
training of the ANN, the WECS needs to operate simultaneously with a wind measurement system,
until a sufficient amount of data is collected on all operating regions of the wind turbine and the wind
turbine characteristics are determined. Next, the ANN is trained, having the rotational speed of the
shaft and the power output of the generator as input signals. As a result, the wind turbine can be
driven to the optimum rotor speed very fast and with high precision so as the MPPT controller can
follow the fast dynamics of the wind speed. Several simulation results are presented for the validation
of the effectiveness of the suggested MPPT control scheme and demonstrate the operationalimprovements.
keywords: {Artificial neural network;Induction generator;Maximum power point tracking;Variable
speed drive;Wind energy conversion system},
URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6521875&isnumber=6521842
Brahmi, J.; Krichen, L.; Ouali, A., "Sensorless control of PMSG in WECS using artificial neural
network," Systems, Signals and Devices, 2009. SSD '09. 6th International Multi-Conference on , vol.,
no., pp.1,8, 23-26 March 2009
doi: 10.1109/SSD.2009.4956689
Abstract: This paper presents an artificial neural network (ANN) observer for a speed sensorless
permanent magnet synchronous generator (PMSG) in wind energy conversion system (WECS). In order
to perform maximum power point tracking control of the wind generation system, it is necessary to
drive wind turbine at an optimal rotor speed. From the aspect of reliability and increase in cost, wind
velocity sensor is not preferred too. Wind and rotor speeds sensorless operating methods for wind
generation system using observer are proposed only by measuring phase voltages and currents.
Maximum wind energy extraction is achieved by running the wind turbine generator in variable-speed
mode. The robustness of the ANN against stator resistance variation is studied.keywords: {artificial intelligence;control engineering computing;direct energy conversion;electric
current measurement;machine vector control;neural nets;observers;optimal control;permanent
magnet generators;power control;synchronous generators;voltage measurement;wind power
plants;wind turbines;artificial neural network;maximum power point tracking control;observer;phase
current measurement;phase voltage measurement;sensorless control;speed sensorless permanent
magnet synchronous generator;variable-speed mode;wind energy conversion system;wind generation
system;wind turbine generator;wind velocity sensor;Artificial neural networks;Optimal
control;Permanent magnets;Rotors;Sensorless control;Synchronous generators;Wind energy;Wind
energy generation;Wind speed;Wind turbines;MPPT;Sensorless control;WECS;artificial neural network
observer},
URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4956689&isnumber=4956638
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6521875&isnumber=6521842http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6521875&isnumber=6521842http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6521875&isnumber=6521842http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4956689&isnumber=4956638http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4956689&isnumber=4956638http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4956689&isnumber=4956638http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4956689&isnumber=4956638http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6521875&isnumber=65218427/28/2019 ANN and Drives IEEE Papers
9/26
Yanjun Yan; Kamath, G.; Osadciw, L.A.; Benson, G.; Legac, P.; Johnson, P.; White, E., "Fusion for
modeling wake effects on wind turbines," Information Fusion, 2009. FUSION '09. 12th International
Conference on , vol., no., pp.1489,1496, 6-9 July 2009
Abstract: Wind turbine wakes cause power reduction and structural loading. A data-driven approach is
proposed for wake modeling to provide the azimuth, angular spread, and intensity of wakes. We
introduce three wind speed difference definitions for wake analysis. The wake identification is
automated using morphological imaging operators. The wake pattern is complicated by multiple
neighboring turbines. Four fusion schemes are proposed to draw a complete picture. A similarity based
clustering enhances the final fusion result by treating clusters, each with specific features, equally.
keywords: {aerodynamics;image processing;mechanical engineering computing;wakes;wind
turbines;data-driven approach;morphological imaging operators;multiple neighboring turbines;power
reduction;similarity based clustering;structural loading;wake analysis;wake effects modeling;wake
identification;wake pattern;wind turbines;Automation;Azimuth;Image processing;Potential
energy;Renewable energy resources;Wind energy;Wind farms;Wind power generation;Wind
speed;Wind turbines;Automation;Fusion;Image Processing;Wake;Wind Turbine},
URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5203854&isnumber=5203583
Fernandez, E.; Mabel, M.C., "Analysis of the Influence of Control Parameters on Wind Farm Output: a
Sensitivity Analysis using ANN Modelling," Power Electronics, Drives and Energy Systems, 2006. PEDES
'06. International Conference on , vol., no., pp.1,4, 12-15 Dec. 2006
doi: 10.1109/PEDES.2006.344289
Abstract: Wind energy planners are interested in studies that highlight the impact of control input
parameters on the output of wind farms. Yet, there are few studies highlighting such investigations. It
has been observed that wind energy programs are being actively pursued in most developing
countries. In India, one of the states that is actively involved in wind energy power generation
programs is Tamil Nadu. Within this state, Muppandal area is one of the identified regions where wind
farms concentration is being encouraged.
keywords: {neurocontrollers;power generation control;sensitivity analysis;wind power;wind power
plants;ANN modelling;India;Muppandal area;Tamil Nadu;power generation;sensitivity analysis;wind
energy planner;wind farm control;Artificial neural networks;Government;Helium;Sensitivity
analysis;Wind energy;Wind energy generation;Wind farms;Wind power generation;Wind speed;Wind
turbines;ANN models;Impact Assessment;Sensitivity Analysis;Wind Power Generation;Wind farms},
URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4147996&isnumber=4147830
Phan Quoc Dzung; Anh Nguyen Bao; Hong-Hee Lee, "New artificial neural network based direct virtual
torque control and direct power control for DFIG in wind energy systems," Power Electronics and Drive
Systems (PEDS), 2011 IEEE Ninth International Conference on , vol., no., pp.219,227, 5-8 Dec. 2011
doi: 10.1109/PEDS.2011.6147250
Abstract: This paper presents direct power control (DPC) strategy for controlling power flow, direct
virtual torque control (DVTC) strategy for synchronizing double-fed induction generator (DFIG) with
grid and voltage oriented control (VOC) for controlling voltage of link capacitor. All strategies are
implemented on artificial neural network (ANN) controller to decrease the time of calculation in
comparison with the conventional DSP control system. The essence of three strategies is selection
appropriate voltage vectors on the rotor side converter. The network is divided in two types: fixed
weight and supervised models. The simulation results on a 4-kW machine are explained using
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5203854&isnumber=5203583http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5203854&isnumber=5203583http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5203854&isnumber=5203583http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4147996&isnumber=4147830http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4147996&isnumber=4147830http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4147996&isnumber=4147830http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4147996&isnumber=4147830http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5203854&isnumber=52035837/28/2019 ANN and Drives IEEE Papers
10/26
MATLAB/SIMULINK together with the Neural Network Toolbox.
keywords: {asynchronous generators;load flow control;machine
control;microcontrollers;neurocontrollers;power control;power convertors;power generation
control;torque control;voltage control;wind power plants;ANN controller;DFIG;DPC strategy;DSP
control system;DVTC strategy;Matlab-Simulink;VOC;artificial neural network controller;direct power
control;direct virtual torque control;double-fed induction generator;link capacitor voltage;neural
network toolbox;power 4 kW;power flow control;rotor side converter;voltage oriented control;wind
energy systems;Artificial neural networks;Hysteresis;Neurons;Rotors;Stators;Torque;Training;Artificial
Neural Network (ANN);Direct Power Control (DPC);Direct Virtual Torque Control (DVTC);Doubly-Fed
Induction Generator (DFIG);Grid-side converter (GSC);Rotor-side converter (RSC)},
URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6147250&isnumber=6146804
Mohseni, M.; Niassati, N.; Tajik, S.; Afjei, E., "A novel method of maximum power point tracking for a
SRG based wind power generation system using AI," Power Electronics and Drive Systems Technology
(PEDSTC), 2012 3rd, vol., no., pp.330,335, 15-16 Feb. 2012
doi: 10.1109/PEDSTC.2012.6183350Abstract: A novel maximum power point tracking technique is introduced in this study, for a wind
power generation system, based on switched reluctance generator. This method is based on the rotor
speed control of the SRG, by adjusting the excitation current with respect to the wind speed, using an
artificial neural network (ANN). In order to achieve best performance, considering the non-linear
nature of the SRG wind power generation system, processes of optimization are performed, using the
genetic algorithm (GA). Results obtained by the optimizations were used to train the ANN. The
presented MPPT method is then modeled and simulated, in MATLAB/SIMULINK environment, in
order to investigate and verify its performance.
keywords: {genetic algorithms;maximum power point trackers;neural nets;power engineering
computing;reluctance generators;rotors;velocity control;wind power
plants;AI;ANN;GA;MATLAB/SIMULINK;MPPT method;SRG wind power generation system;artificial
neural network;genetic algorithm;maximum power point tracking method;nonlinear
nature;optimization processes;rotor speed control;switched reluctance generator;Energy
loss;Optimized production technology;Switches;Wind power generation;Artificial Neural
Network;Genetic Algorithm;Maximum Power Point Tracking;Switched Reluctance Generator;Wind
Power Generation},
URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6183350&isnumber=6183296
Ren, Y. F.; Bao, G. Q., "Control Strategy of Maximum Wind Energy Capture of Direct-Drive Wind
Turbine Generator Based on Neural-Network," Power and Energy Engineering Conference (APPEEC),
2010 Asia-Pacific , vol., no., pp.1,4, 28-31 March 2010
doi: 10.1109/APPEEC.2010.5448343
Abstract: The wind power varies mainly depending on the wind speed. Many methods have been
proposed to track the maximum power point (MPPT) of the wind, such as the fuzzy logic (FL), artificial
neural network (ANN) and Neuro-Fuzzy. In this paper, a variable speed wind generator MPPT based on
artificial neural network (ANN) is presented. It is designed as a combination of the generator speed
forecasting model and neural network. The ANN is used to predict the optimal speed rotation using
the variation of the wind speed and the generator speed as the inputs. The wind energy control
system employs a permanent magnet synchronous generator connected to a DC bus using a power
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6147250&isnumber=6146804http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6147250&isnumber=6146804http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6147250&isnumber=6146804http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6183350&isnumber=6183296http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6183350&isnumber=6183296http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6183350&isnumber=6183296http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6183350&isnumber=6183296http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6147250&isnumber=61468047/28/2019 ANN and Drives IEEE Papers
11/26
converter is presented. The performance of the control system with the proposed ANN controller is
tested for wind speed variation. System simulation results have confirmed the functionality and
performance of this method.
keywords: {fuzzy neural nets;maximum power point trackers;neurocontrollers;permanent magnet
generators;power convertors;power generation control;synchronous generators;wind turbines;DC
bus;artificial neural network;direct-drive wind turbine generator;fuzzy logic;generator speed
forecasting model;maximum power point trackers;neuro-fuzzy;permanent magnet synchronous
generator;power converter;wind energy capture;wind energy control;wind speed;Artificial neural
networks;Control systems;Energy capture;Fuzzy logic;Predictive models;Wind energy;Wind energy
generation;Wind forecasting;Wind speed;Wind turbines},
URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5448343&isnumber=5448125
Vale, Z.A.; Morais, H.; Faria, P.; Soares, J.; Sousa, T., "LMP based bid formation for virtual power
players operating in smart grids," Power and Energy Society General Meeting, 2011 IEEE, vol., no.,
pp.1,8, 24-29 July 2011
doi: 10.1109/PES.2011.6039853Abstract: Power system organization has gone through huge changes in the recent years. Significant
increase in distributed generation (DG) and operation in the scope of liberalized markets are two
relevant driving forces for these changes. More recently, the smart grid (SG) concept gained increased
importance, and is being seen as a paradigm able to support power system requirements for the
future. This paper proposes a computational architecture to support day-ahead Virtual Power Player
(VPP) bid formation in the smart grid context. This architecture includes a forecasting module, a
resource optimization and Locational Marginal Price (LMP) computation module, and a bid formation
module. Due to the involved problems characteristics, the implementation of this architecture requires
the use of Artificial Intelligence (AI) techniques. Artificial Neural Networks (ANN) are used for resource
and load forecasting and Evolutionary Particle Swarm Optimization (EPSO) is used for energy resource
scheduling. The paper presents a case study that considers a 33 bus distribution network that includes
67 distributed generators, 32 loads and 9 storage units.
keywords: {energy resources;load forecasting;particle swarm optimisation;power engineering
computing;power markets;smart power grids;LMP based bid formation;artificial intelligence
techniques;artificial neural networks;bid formation module;distributed generation;energy resource
scheduling;evolutionary particle swarm optimization;forecasting module;liberalized markets;load
forecasting;locational marginal price;resource optimization;smart grids;virtual power players;Artificialneural networks;Contracts;Electricity supply industry;Energy resources;Smart grids;Wind
forecasting;Artificial Intelligence;Artificial Neural Networks;Energy Resources Management;Intelligent
Power Systems;Locational Marginal Prices (LMP);Particle Swarm Optimization},
URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6039853&isnumber=6038815
Abdel-Khalik, A.S.; Elserougi, A.; Massoud, A.; Ahmed, S., "Control of doubly-fed induction machine
storage system for constant charging/discharging grid power using artificial neural network," Power
Electronics, Machines and Drives (PEMD 2012), 6th IET International Conference on , vol., no., pp.1,6,
27-29 March 2012
doi: 10.1049/cp.2012.0177
Abstract: A large-capacity low-speed flywheel energy storage system based on a doubly-fed induction
machine (DFIM) basically consists of a wound-rotor induction machine, and a back-to-back converter
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5448343&isnumber=5448125http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5448343&isnumber=5448125http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5448343&isnumber=5448125http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6039853&isnumber=6038815http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6039853&isnumber=6038815http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6039853&isnumber=6038815http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6039853&isnumber=6038815http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5448343&isnumber=54481257/28/2019 ANN and Drives IEEE Papers
12/26
for rotor excitation. It has been promoted as a challenging storage system for power system
applications such as grid frequency support/control, power conditioning, and voltage sag mitigation.
This paper presents a power control strategy to charge/discharge a flywheel doubly-fed induction
machine storage system (FW-DFIM) to obtain a constant power delivered to the grid. The proposed
controller is based on conventional vector control, where an artificial neural network (ANN) is used to
develop the required rotor current component based on the required grid power level and the
flywheel instantaneous speed. This technique is proposed for power levelling and frequency support
to improve the quality of the electric power delivered by wind generators, where a constant power
level can be delivered to the grid for a predetermined time depending on the required power level and
the storage system inertia. The controller is designed to avoid overloading stator as well as rotor
circuits while the flywheel charges/discharges. The validity of the developed concept in this paper,
along with the effectiveness and viability of the control strategy, is confirmed by computer simulation
using Matlab/Simulink for a medium voltage 10MJ/1000hp FW-DFIM example.
keywords: {Doubly-fed induction machine;ac machines;flywheel storage system;neural network;vector
control},
URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6242026&isnumber=6241991
Dong Lei; Wang Lijie; Hu Shi; Gao Shuang; Liao Xiaozhong, "Prediction of Wind Power Generation
based on Chaotic Phase Space Reconstruction Models," Power Electronics and Drive Systems, 2007.
PEDS '07. 7th International Conference on , vol., no., pp.744,748, 27-30 Nov. 2007
doi: 10.1109/PEDS.2007.4487786
Abstract: The development of wind generation has rapidly progressed over the last decade, but it must
be integrated into power grids and electric utility systems. However, it cannot be dispatched like
conventional generators because the power generated by the wind changes rapidly because of the
continuous fluctuation of wind speed and direction. So it is very important to predict the wind power
generation. This paper discusses why the wind power generation can be predicted in short-term, and
how to setup the construction of an ANN (artificial neural network) prediction model of wind power
based on chaotic time series. The analysis of modeling with low dimensions nonlinear dynamics
indicates that time series of wind power generation have chaotic characteristics, and wind power can
be predicted in short-term. Phase space reconstruction method can be used for ANN model design.
The data from the wind farm located in the Saihanba China are used for this study.
keywords: {chaos;load forecasting;neural nets;nonlinear dynamical systems;phase space
methods;power system simulation;time series;wind power plants;ANN prediction model;artificialneural network;chaotic phase space reconstruction models;chaotic time series;electric utility
systems;nonlinear dynamics;power grids;wind farm;wind power generation;Artificial neural
networks;Chaos;Mesh generation;Power generation;Power grids;Power system modeling;Predictive
models;Wind energy;Wind energy generation;Wind power generation;chaotic dynamic
system;forecast;neural network;wind power prediction},
URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4487786&isnumber=4487657
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6242026&isnumber=6241991http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6242026&isnumber=6241991http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6242026&isnumber=6241991http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4487786&isnumber=4487657http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4487786&isnumber=4487657http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4487786&isnumber=4487657http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4487786&isnumber=4487657http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6242026&isnumber=62419917/28/2019 ANN and Drives IEEE Papers
13/26
Muyeen, S. M.; Hasanien, H.M.; Tamura, J., "Reduction of frequency fluctuation for wind farm
connected power systems by an adaptive artificial neural network controlled energy capacitor
system,"Renewable Power Generation, IET, vol.6, no.4, pp.226,235, July 2012
doi: 10.1049/iet-rpg.2010.0126
Abstract: Frequency fluctuations are a major concern for transmission system operators and power
grid companies from the beginning of power system operation due to their adverse effects on modern
computer-controlled industrial systems. Because of the huge integration of wind power into the power
grid, frequency fluctuations are becoming a serious problem, where randomly varying wind power
causes the grid frequency fluctuations of the power system. Therefore, in this paper, the minimisation
of the frequency fluctuation of a power system, including a wind farm, is proposed using an energy
capacitor system (ECS). A scaled-down, multi-machine power system model from Hokkaido prefecture,
Japan, is considered for the analysis. A novel adaptive artificial neural network (ANN) controller is
considered for controlling the DC-bus connected ECS. The control objective is to standardise the line
power of the wind farm, taking into consideration the frequency deviation. The effects of wind power
penetration levels, as well as load variations, are also analysed. The proposed control method is
verified by simulation analysis, which is performed with PSCAD/EMTDC using real wind speed data.The adaptive ANN-controlled ECS was found to be an effective means of diminishing the frequency
fluctuation of multi-machine power systems with connected wind farms.
keywords: {adaptive control;neurocontrollers;power capacitors;power generation control;power
grids;wind power plants;ANN controller;DC-bus connected ECS;PSCAD-EMTDC;adaptive ANN-
controlled ECS;adaptive artificial neural network controlled energy capacitor system;adaptive artificial
neural network controller;computer-controlled industrial systems;grid frequency fluctuation
reduction;load variations;power grid companies;power system operation;scaled-down multimachine
power system model;transmission system operators;wind farm connected power systems;wind power
penetration levels;wind speed data},
URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6291071&isnumber=6291068
Alexiadis, M.C.; Dokopoulos, P.S.; Sahsamanoglou, H. S., "Wind speed and power forecasting based on
spatial correlation models," Energy Conversion, IEEE Transactions on , vol.14, no.3, pp.836,842, Sep
1999
doi: 10.1109/60.790962
Abstract: Wind energy conversion systems (WECS) cannot be dispatched like conventional generators.
This can pose problems for power system schedulers and dispatchers, especially if the schedule ofwind power availability is not known in advance. However, if the wind speed can be reliably forecasted
up to several hours ahead, the generating schedule can efficiently accommodate the wind generation.
This paper illustrates a technique for forecasting wind speed and power output up to several hours
ahead, based on cross correlation at neighboring sites. The authors develop an artificial neural
network (ANN) that significantly improves forecasting accuracy comparing to the persistence
forecasting model. The method is tested at different sites over a year
keywords: {correlation methods;forecasting theory;neural nets;power generation planning;power
generation scheduling;power system analysis computing;wind;wind power;wind power plants;artificial
neural network;cross correlation;forecasting accuracy;power system dispatchers;power system
schedulers;spatial correlation models;wind power availability schedule;wind power forecasting;wind
power generation;wind speed forecasting;Artificial neural networks;Job shop scheduling;Power system
modeling;Power system stability;Predictive models;Weather forecasting;Wind energy;Wind energy
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6291071&isnumber=6291068http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6291071&isnumber=6291068http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6291071&isnumber=6291068http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6291071&isnumber=62910687/28/2019 ANN and Drives IEEE Papers
14/26
generation;Wind forecasting;Wind speed},
URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=790962&isnumber=17195
Walker, R. C.; Early, H. C., "HalfMegampere MagneticEnergyStorage Pulse Generator,"Review of
Scientific Instruments , vol.29, no.11, pp.1020,1022, Nov 1958
doi: 10.1063/1.1716044
Abstract: Energy is stored in the magnetic field of a large aircore transformer having a very low
impedance, tightly coupled secondary winding. The energy can be effectively delivered in less than 5
msec to a noninductive load, having a resistance of less than 10-4
ohm.
URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5049249&isnumber=5049224
Methaprayoon, K.; Yingvivatanapong, C.; Wei-jen Lee; Liao, J.R., "An Integration of ANN Wind Power
Estimation Into Unit Commitment Considering the Forecasting Uncertainty," Industry Applications,
IEEE Transactions on , vol.43, no.6, pp.1441,1448, Nov.-dec. 2007
doi: 10.1109/TIA.2007.908203
Abstract: The development of wind power generation has rapidly progressed over the last decade.With the advancement in wind turbine technology, wind energy has become competitive with other
fuel-based resources. The fluctuation of wind, however, makes it difficult to optimize the usage of
wind power. The current practice ignores wind generation capacity in the unit commitment (UC),
which discounts its usable capacity and may cause operational issues when the installation of wind
generation equipment increases. To ensure system reliability, the forecasting uncertainty must be
considered in the incorporation of wind power capacity into generation planning. This paper discusses
the development of an artificial-neural-network-based wind power forecaster and the integration of
wind forecast results into UC scheduling considering forecasting uncertainty by the probabilistic
concept of confidence interval. The data from a wind farm located in Lawton City, OK, is used in this
paper.
keywords: {neural nets;power engineering computing;power generation planning;power generation
scheduling;wind power plants;ANN model;Lawton City;artificial-neural-network;forecasting
uncertainty;power generation planning;unit commitment;unit commitment scheduling;wind
energy;wind power estimation;wind power forecaster;wind power generation;wind turbine
technology;Capacity planning;Fluctuations;Power generation;Reliability;Uncertainty;Wind
energy;Wind energy generation;Wind forecasting;Wind power generation;Wind turbines;Artificial
neural network (ANN);confidence interval;short-term wind power forecast;wind forecast uncertainty},URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4385004&isnumber=4384978
Gershman, Daniel J.; Zurbuchen, T.H., "Modeling extreme ultraviolet suppression of electrostatic
analyzers," Review of Scientific Instruments , vol.81, no.4, pp.045111,045111-8, Apr 2010
doi: 10.1063/1.3378685
Abstract: In addition to analyzing energy-per-charge ratios of incident ions, electrostatic analyzers
(ESAs) for spaceborne time-of-flight mass spectrometers must also protect detectors from extreme
ultraviolet (EUV) photons from the Sun. The required suppression rate often exceeds 1:107
and is
generally established in tests upon instrument design and integration. This paper describes a novel
technique to model the EUV suppression of ESAs using photon ray tracing integrated into SIMION, the
most commonly used ion optics design software for such instruments. The paper compares simulation
results with measurements taken from the ESA of the Mass instrument flying onboard the Wind
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=790962&isnumber=17195http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=790962&isnumber=17195http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=790962&isnumber=17195http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5049249&isnumber=5049224http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5049249&isnumber=5049224http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5049249&isnumber=5049224http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4385004&isnumber=4384978http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4385004&isnumber=4384978http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4385004&isnumber=4384978http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4385004&isnumber=4384978http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5049249&isnumber=5049224http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=790962&isnumber=171957/28/2019 ANN and Drives IEEE Papers
15/26
spacecraft. This novel technique enables an active inclusion of EUV suppression requirements in the
ESA design process. Furthermore, the simulation results also motivate design rules for such
instruments.
keywords: {mass spectrometers;space vehicle electronics;ultraviolet
spectrometers;0760Rd;0775+h;0787+v},
URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5457935&isnumber=5442927
Fidalgo, J.N.; Peas Lopes, J.A.; Miranda, V., "Neural networks applied to preventive control measures
for the dynamic security of isolated power systems with renewables," Power Systems, IEEE
Transactions on , vol.11, no.4, pp.1811,1816, Nov 1996
doi: 10.1109/59.544647
Abstract: This paper presents an artificial neural network (ANN) based approach for the definition of
preventive control strategies of autonomous power systems with a large renewable power
penetration. For a given operating point, a fast dynamic security evaluation for a specified wind
perturbation is performed using an ANN. If insecurity is detected, new alternative stable operating
points are suggested, using a hybrid ANN-optimization approach that checks several feasiblepossibilities, resulting from changes in power produced by diesel and wind generators, and other
combinations of diesel units in operation. Results obtained from computer simulations of the real
power system of Lemnos (Greece) support the validity of the developed approach
keywords: {control system analysis computing;control system synthesis;diesel-electric power
stations;neurocontrollers;optimal control;power system analysis computing;power system
control;power system security;power system stability;wind power plants;artificial neural
network;autonomous power systems;computer simulation;control design;control simulation;dynamic
security evaluation;isolated power systems;optimization approach;preventive neurocontrol
strategy;renewable energy resources;wind-diesel hybrid power systems;Artificial neural
networks;Control systems;Hybrid power systems;Neural networks;Performance evaluation;Power
system control;Power system dynamics;Power system measurements;Power system security;Power
systems},
URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=544647&isnumber=11903
Karayaka, H.B.; Keyhani, A.; Agrawal, B.L.; Selin, Douglas A.; Heydt, G.T., "Identification of armature,
field, and saturated parameters of a large steam turbine-generator from operating data," Energy
Conversion, IEEE Transactions on , vol.15, no.2, pp.181,187, Jun 2000doi: 10.1109/60.866997
Abstract: This paper presents a step by step identification procedure of armature, field and saturated
parameters of a large steam turbine-generator from real time operating data. First, data from a small
excitation disturbance is utilized to estimate armature circuit parameters of the machine.
Subsequently, for each set of steady state operating data, saturable mutual inductances Lads and
Laqs are estimated. The recursive maximum likelihood estimation technique is employed for
identification in these first two stages. An artificial neural network (ANN) based estimator is used to
model these saturated inductances based on the generator operating conditions. Finally, using the
estimates of the armature circuit parameters, the field winding and some damper winding parameters
are estimated using an output error method (OEM) of estimation. The developed models are validated
with measurements not used in the training of ANN and with large disturbance responses
keywords: {damping;inductance;machine windings;maximum likelihood estimation;neural nets;power
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5457935&isnumber=5442927http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5457935&isnumber=5442927http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5457935&isnumber=5442927http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=544647&isnumber=11903http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=544647&isnumber=11903http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=544647&isnumber=11903http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=544647&isnumber=11903http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5457935&isnumber=54429277/28/2019 ANN and Drives IEEE Papers
16/26
system analysis computing;recursive estimation;steam turbines;turbogenerators;armature
parameters;artificial neural network;damper winding parameters;excitation disturbance;field
parameters;generator operating conditions;output error method;parameters identification;recursive
maximum likelihood estimation;saturable mutual inductances estimation;saturated parameters;steady
state operating data;steam turbine-generator;training;Artificial neural
networks;Circuits;Damping;Maximum likelihood estimation;Parameter estimation;Shape;Shock
absorbers;State estimation;Steady-state;Voltage},
URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=866997&isnumber=18774
Fraile-Ardanuy, J.; Wilhelmi, J.R.; Fraile-Mora, J.J.; Perez, J.I., "Variable-speed hydro generation:
operational aspects and control," Energy Conversion, IEEE Transactions on , vol.21, no.2, pp.569,574,
June 2006
doi: 10.1109/TEC.2005.858084
Abstract: The potential advantages of variable-speed hydroelectric generation are discussed in this
article. Some general aspects concerning the efficiency gains in turbines and the improvements in
plant operation are analyzed. The main results of measurements on a test loop with an axial-flowturbine are reported. Also, we describe the control scheme implemented, which is based on artificial
neural networks. To confirm the practical interest of this technology, the operation of a run-of-the-
river small hydro plant has been simulated for several years. Substantial increases in production with
respect a fixed-speed plant have been found.
keywords: {hydraulic turbines;hydroelectric power stations;neurocontrollers;power generation
control;artificial neural networks;axial-flow turbines;hydroplants;plant operation improvements;test
loop;variable-speed hydrogeneration;Artificial neural networks;Costs;Hydraulic turbines;Hydroelectric
power generation;Power generation;Production;Propellers;Synchronous generators;Testing;Wind
energy generation;Artificial neural network (ANN);operation limits of hydroturbines;regenerative
frequency converters;variable-speed hydro generation},
URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1634606&isnumber=34276
Lepri, S.T.; Nikzad, S.; Jones, T.; Blacksberg, J.; Zurbuchen, T.H., "Response of a delta-doped charge-
coupled device to low energy protons and nitrogen ions," Review of Scientific Instruments , vol.77,
no.5, pp.053301,053301-9, May 2006
doi: 10.1063/1.2198829
Abstract: We present the results of a study of the response of a delta-doped charge-coupled device(CCD) exposed to ions with energies less than 10 keV. The study of ions in the solar wind, the majority
having energies in the 15 keV range, has proven to be vital in understanding the solar atmosphere
and the near Earth space environment. Delta-doped CCD technology has essentially removed the dead
layer of the silicon detector. Using the delta-doped detector, we are able to detect H+
and N+
ions with
energies ranging from 1 to 10 keV in the laboratory. This is a remarkable improvement in the low
energy detection threshold over conventional solid-state detectors, such as those used in space
sensors, one example being the solar wind ion composition spectrometer (SWICS) on the Advanced
Composition Explorer spacecraft, which can only detect ions with energies greater
than 30 keV because of the solid-state detectors minimum energy threshold. Because this threshold is
much higher than the average energy of the solar wind ions, the SWICS instrument employs a bulky
high voltage postacceleration stage that accelerates ions above the 30 keV detection threshold. This
stage is massive, exposes the instrument to hazardous high voltages, and is therefore problematic
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=866997&isnumber=18774http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=866997&isnumber=18774http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=866997&isnumber=18774http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1634606&isnumber=34276http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1634606&isnumber=34276http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1634606&isnumber=34276http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1634606&isnumber=34276http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=866997&isnumber=187747/28/2019 ANN and Drives IEEE Papers
17/26
both in terms of price and its impact on spacecraft resources. Adaptation of delta-doping technology in
future space missions may be successful in reducing the need for heavy postacceleration stages
allowing for miniaturization of space-borne ion detectors.
keywords: {charge-coupled devices;cosmic ray apparatus;position sensitive particle detectors;solar
atmosphere;solar cosmic ray particles;solar radiation;solar wind;2940Gx;9660Vg},
URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5002805&isnumber=5002795
Pena, F.L.; Duro, R.J., "A virtual instrument for automatic anemometer calibration with ANN based
supervision," Instrumentation and Measurement, IEEE Transactions on , vol.52, no.3, pp.654,661, June
2003
doi: 10.1109/TIM.2003.814703
Abstract: A fully automatic anemometer calibrator intended for performing fast and accurate
calibrations has been developed to fulfill the increasing demand and strict requirements of the wind
energy industry. Different sensors are connected to a computer where a virtual environment acquires
and processes the incoming signals and controls a wind tunnel, allowing the calibration of the
anemometer at the pre-selected air speed values. An important part of the resulting complex virtualenvironment is a supervising system, based on artificial neural networks and able to check and handle
the possible malfunctions and deviations within the calibration process.
keywords: {anemometers;calibration;geophysics computing;neural nets;virtual instrumentation;wind
tunnels;artificial neural network supervision;automatic anemometer calibration;computerised
sensor;virtual instrument;wind energy;wind tunnel;AC motors;Artificial neural networks;Associate
members;Calibration;Electrical equipment industry;Fluid flow
measurement;Hardware;Instruments;Virtual environment;Wind energy},
URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1213644&isnumber=27281
Maene, N.; Cornelis, J.; Biermans, F.; Van den Bosch, A., "Quench experiments on superconductive
coils," Magnetics, IEEE Transactions on , vol.21, no.2, pp.702,705, Mar 1985
doi: 10.1109/TMAG.1985.1063746
Abstract: A set of four similar superconductive coils of 166 mm inner winding diameter were wet-
wound with NbTi wire for subsequent application in various configurations: one single coil, a pair of
coils or all four coils stacked on top of each other and connected in series. The height of the winding
was 41 mm and the thickness 9 mm. The detailed observation of the time-dependence of the current
and voltage with a data processing system yielded information on the time scale of the quenchpropagation. In the single coil and the pair of coils the time dependence of the quench resistance with
time was derived from an analysis of the current and the coil voltages during the transient. With the
four coils the time required for the current to decrease from 90 % to 10 % gets shorter with increasing
quench current. Variations of 1.6 s to 0.3 s were observed in this configuration. At the maximum
current a magnetic induction of at least 2.5 T was reached in a volume of over 2.5 litres. The self-
inductance of this system was 1.28 Henry and the stored energy attained 22 kJ.
keywords: {Superconducting coils;Boring;Magnetic flux;Niobium compounds;Superconducting
coils;Superconducting magnets;Superconductivity;Testing;Titanium compounds;Voltage;Wire},
URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1063746&isnumber=22874
Fouad, R. H.; Ashhab, M. S.; Mukattash, A.; Idwan, S., "Simulation and energy management of an
experimental solar system through adaptive neural networks," Science, Measurement & Technology,
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5002805&isnumber=5002795http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5002805&isnumber=5002795http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5002805&isnumber=5002795http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1213644&isnumber=27281http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1213644&isnumber=27281http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1213644&isnumber=27281http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1063746&isnumber=22874http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1063746&isnumber=22874http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1063746&isnumber=22874http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1063746&isnumber=22874http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1213644&isnumber=27281http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5002805&isnumber=50027957/28/2019 ANN and Drives IEEE Papers
18/26
IET, vol.6, no.6, pp.427,431, November 2012
doi: 10.1049/iet-smt.2011.0201
Abstract: In this study, the authors consider a solar system which consists of a solar trainer that
contains a photovoltaic panel, a DC centrifugal pump, flat plate collectors, storage tank, a flowmeter
for measuring the water mass flow rate, pipes, pyranometer for measuring the solar intensity,
thermocouples for measuring various system temperatures and wind speed meter. The various
efficiencies of the solar system have been predicted by an artificial neural network (ANN) which was
trained with historical data. The ANN fails to predict the efficiencies accurately over the long-time
horizon because of system parts degradation, environmental variations, date changes within the year
from the modelling date and presence of modelling errors. Therefore the ANN is adapted using the
error between the ANN-predicted efficiency and the efficiency measurement from the appropriately
selected sensors and efficiency laws to update the network's parameters recursively. The adaptation
scheme can be performed online or occasionally and is based on the Kaczmarz's algorithm. The
adaptive ANN capability is demonstrated through computer simulation.
URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6356016&isnumber=6355496
Opathella, C.; Singh, B; Cheng, D.; Venkatesh, B., "Intelligent Wind Generator Models for Power Flow
Studies in PSSE and PSSSINCAL," Power Systems, IEEE Transactions on , vol.28, no.2, pp.1149,1159,
May 2013
doi: 10.1109/TPWRS.2012.2211043
Abstract: Wind generator (WG) output is a function of wind speed and three-phase terminal voltage.
Distribution systems are predominantly unbalanced. A WG model that is purely a function of wind
speed is simple to use with unbalanced three-phase power flow analysis but the solution is inaccurate.
These errors add up and become pronounced when a single three-phase feeder connects several WGs.
Complete nonlinear three-phase WG models are accurate but are slow and unsuitable for power flow
applications. This paper proposes artificial neural network (ANN) models to represent type-3 doubly-
fed induction generator and type-4 permanent magnet synchronous generator. The proposed
approach can be readily applied to any other type of WGs. The main advantages of these ANN models
are their mathematical simplicity, high accuracy with unbalanced systems and computational speed.
These models were tested with the IEEE 37-bus test system. The results show that the ANN WG
models are computationally ten times faster than nonlinear accurate models. In addition, simplicity of
the proposed ANN WG models allow easy integration into commercial software packages such as
PSSE and PSSSINCAL and implementations are also shown in this paper.keywords: {Artificial neural networks;Biological system modeling;Computational
modeling;Generators;Mathematical model;Neurons;Wind speed;Artificial neural networks;power
distribution systems;power flow;wind power generators},
URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6302218&isnumber=6504806
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6356016&isnumber=6355496http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6356016&isnumber=6355496http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6356016&isnumber=6355496http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6302218&isnumber=6504806http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6302218&isnumber=6504806http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6302218&isnumber=6504806http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6302218&isnumber=6504806http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6356016&isnumber=63554967/28/2019 ANN and Drives IEEE Papers
19/26
Pinto, J. O P; Bose, B.K.; da Silva, L.E.B., "A stator-flux-oriented vector-controlled induction motor drive
with space-vector PWM and flux-vector synthesis by neural networks," Industry Applications, IEEE
Transactions on , vol.37, no.5, pp.1308,1318, Sep/Oct 2001
doi: 10.1109/28.952506
Abstract: A stator-flux-oriented vector-controlled induction motor drive is described where the space-
vector pulsewidth modulation (SVM) and stator-flux-vector estimation are implemented by artificial
neural networks (ANNs). ANNs, when implemented by dedicated hardware application-specific
integrated circuit chips, provide extreme simplification and fast execution for control and feedback
signal processing functions in high-performance AC drives. In the proposed project, a feedforward
ANN-based SVM, operating at 20 kHz sampling frequency, generates symmetrical pulsewidth
modulation (PWM) pulses in both undermodulation and overmodulation regions covering the range
from DC (zero frequency) up to square-wave mode at 60 Hz. In addition, a programmable cascaded
low-pass filter (PCLPF), that permits DC offset-free stator-flux-vector synthesis at very low frequency
using the voltage model, has been implemented by a hybrid neural network which consists of a
recurrent neural network (RNN) and a feedforward neural network (FFANN). The RNN-FFANN-based
flux estimation is simple, permits faster implementation, and gives superior transient performancewhen compared with a standard digital-signal-processor-based PCLPF. A 5 HP open-loop volts/Hz-
controlled drive incorporating the proposed ANN-based SVM and RNN-FFANN-based flux estimator
was initially evaluated in the frequency range of 1.0-58 Hz to validate the performance of SVM and the
flux estimator. Next, the complete 5 HP drive with stator-flux-oriented vector control was evaluated
extensively using the PWM modulator and flux estimator
keywords: {PWM invertors;feedforward neural nets;frequency control;induction motor drives;low-
pass filters;machine vector control;magnetic flux;neurocontrollers;recurrent neural
nets;stators;voltage control;1 to 58 Hz;20 kHz;5 hp;60 Hz;ANN;DC offset-free stator-flux-vector
synthesis;application-specific integrated circuit chips;artificial neural networks;digital-signal-
processor;feedback signal processing functions;feedforward ANN-based SVM;feedforward neural
network;flux estimator;flux-vector synthesis;frequency control;hybrid neural network;neural
networks;overmodulation region;programmable cascaded low-pass filter;recurrent neural
network;sampling frequency;space-vector PWM;space-vector pulsewidth modulation;square-wave
mode;stator-flux-oriented vector control;stator-flux-oriented vector-controlled induction motor
drive;stator-flux-vector estimation;symmetrical pulsewidth modulation pulses generation;transient
performance;undermodulation region;very low frequency;voltage control;voltage model;Feedforward
neural networks;Frequency estimation;Frequency synthesizers;Induction motor drives;Neuralnetworks;Pulse width modulation;Recurrent neural networks;Space vector pulse width
modulation;Stators;Support vector machines},
URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=952506&isnumber=20592
Rahman, M.A.; Ashraful Hoque, M., "Online self-tuning ANN-based speed control of a PM DC
motor," Mechatronics, IEEE/ASME Transactions on , vol.2, no.3, pp.169,178, Sep 1997
doi: 10.1109/3516.622969
Abstract: This paper presents an online self-tuning artificial-neural-network (ANN)-based speed control
scheme of a permanent magnet (PM) DC motor. For precise speed control, an online training
algorithm with an adaptive learning rate is introduced, rather than using fixed weights and biases of
the ANN. The complete system is implemented in real time using a digital signal processor controller
board (DS1102) on a laboratory PM DC motor. To validate its efficacy, the performances of the
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=952506&isnumber=20592http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=952506&isnumber=20592http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=952506&isnumber=20592http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=952506&isnumber=205927/28/2019 ANN and Drives IEEE Papers
20/26
proposed ANN-based scheme are compared with a proportional-integral controller-based PM DC
motor drive system under different operating conditions. The comparative results show that the ANN-
based speed control scheme is robust, accurate, and insensitive to parameter variations and load
disturbances
keywords: {DC motors;adaptive control;angular velocity control;feedforward neural
nets;neurocontrollers;permanent magnet motors;real-time systems;self-adjusting systems;tuning;DC
motors;adaptive learning;biases;digital signal processor controller;feedback;feedforward neural-
network;online self-tuning;permanent magnet motors;real time system;speed control;Adaptive
control;Artificial neural networks;Control systems;DC motors;Digital control;Digital signal
processors;Programmable control;Real time systems;Signal processing algorithms;Velocity control},
URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=622969&isnumber=13554
Nguyen, C.T.-C.; Howe, R.T., "An integrated CMOS micromechanical resonator high-Q oscillator," Solid-
State Circuits, IEEE Journal of, vol.34, no.4, pp.440,455, Apr 1999
doi: 10.1109/4.753677
Abstract: A completely monolithic high-Q oscillator, fabricated via a combined CMOS plus