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Suman Sahoo Roll No : 97/ELM/124005By

Sumanta Kundu Roll No: 97/ELM/124013 Agniv Mukherjee Roll No : 97/ELM/124016Raju Ray Roll No : 97/ELM/124017

Under Guidance ofProf. Jitendra Nath Bera

Department of Applied Physics,University College of Science & Technology,

University of Calcutta92, A.P.C. Road, Kolkata-700009

West Bengal, India Date: 26-05-2015

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Introduction

Solutions

Objectives

Theoretical Background

Experimentation

Hardware Implementation

Conclusion

Future Scope

Reference

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Information regarding real time specific data for energy consumption and corresponding tariff

Remote control of different Home Appliances

Remote notification of usage of energy consumption

Managing and storing vast quantities of metering data

Ensuring the security of metering data

Extra energy (if generated) can be sent back to Grid

Today’s Demands

Performance degradation analysis of a particular appliance

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Decarbonise electricity

Greater visibility to distribution network

Participation of the consumers into power system operation through Smart Meter, Smart Plug.

Improved ICT (Information & Communication Technology) offers greater monitoring, control, flexibility and low cost operation of power system.

Effective management of loads and reduction of losses and wasted energy needs accurate information about the loads.

Performance comparison for same appliance of different make

Local display of information on smart plug itself

Contd.

Provides some basic information of a particular appliance

Includes an embedded ICT unit so that power usage information it collects about the appliance can then be transmitted

Access electricity consumption data and determine the best time to use an individual appliance

Smart PlugContd.

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Smart Plug

Essential part of the Smart GridIt can provide detailed load flow on real time basisHelps in effective management of the grid operationHelps consumer to realize the energy usage and the corresponding tariffTwo way communication

- Automatic Meter Reading- Restriction of supply

Smart Energy Meter

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Contd.

Smart Energy Meter

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Smart Home•A home equipped with lighting, heating, and electronic devices that can be controlled remotely by smart phone or computer

• Smart home relate to the development of some major aspects:

(a)Capabilities of home infrastructure and controlled device

(b) Usability of mobile and stationary user interfaces

Contd.

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Contd.

Pictorial Representation of a Typical Smart Home

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Contd.

Involvement of Information & Communication Technology

Rapid development of Wireless Communication Systems like 3G, 4G, Wi-FiIntroductory involvement of communication interfaces like Bluetooth, ZigBee, Wi-FiPower Line Communication (PLC)

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Power Line Communication (PLC) as Communication Channel

The Home Automation System Using Power Line Communication (PLC) at home is user friendly and cost efficient. It requires only electricity to run the system.Fundamental parts of the smart meter as well as the Smart Grid.Communication through power line by the Utilities to Consumers if possible results to breakthrough in communications.Every household would be connected at any time and services being provided at real-time.Based on electrical signals, carrying information, propagating over the power-line.

Contd.

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ClassifiersAn algorithm implementing classification, especially concrete implementation A mathematical function , implemented by a classification algorithm, that maps input data to a category

Bayesian

Support Vector machine Classifier

Fuzzy Ruled Based Classifier

Artificial Neural Network

Types of Classifiers

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In our project we have used the Artificial Neural Network (ANN) as the classifier has some benefits from others which are mentioned below :

(a) Adaptive Learning

(b) Self-Organization

(c) Real Time Operation

(d) Fault Tolerance via Redundant Information Coding

(e) Implementation Ability

Contd.

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Artificial Neural Network (ANN)A computational system inspired by the structure, processing method and learning ability of a biological brain

Contd.

(a) A large number of very simple processing neuron-like processing elements.

(b) A large number of weighted connections between the elements. (c) Distributed representation of knowledge over the connections. (d) Knowledge is acquired by network through a learning process.

Elements of ANN

(a) Processing unit

(b) Activation function

(c) Learning paradigm

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Contd.

Learning Algorithms

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Contd.

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Design of ANNContd.

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Back Propagataion AlgorithmContd.

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Schematic Overview

Ardiuno UNO Board

Signal Conditioning

Signal Conditioning

ADC Atmega 328 µC

Communication unit

(PLC)

Local Display

Ph N

CT

1-ph Elec. Load

PT

Smart Plug

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Smart Meter

Procedural Steps

Training And Testing of Neural Network

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Contd.

Data Acquisition & Signature Extraction

•Voltage & current data of diff. load captured by DSO

•The sample data of voltage & current data taken into a spreadsheet using software webstar

• Formation of load signature (by mathematical calculation) using captured sampled data of voltage & current of diff. load

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•The data obtained in the measurement is stored into a computer for further study; the appliances include:

i) Fan ii) Bulb ; iii) Tube Light iv) Heater v)1 Ph Induction Motor

Contd.

Captured Data for Signature Extraction

current

TimeTime

voltage

Current nature obtained for 200 Watt bulb switching phenomenon .

Voltage nature obtained for 200 Watt bulb switching phenomenon

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Contd.

AmplitudePower spectrum

Time Frequency

Simulated MATLAB output of 200W bulb current switching phenomenon

FFT Analysis using MatLab

Simulated MATLAB FFT analysis of 200W bulb current data

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Contd.

ANN Start up Window

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NN toolbox can be open by entering the command on command window>>nnstart

Contd.

>> nprtool or Pattern Recognition Application from Neural Network Start Window

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Neural Pattern Recognition Application Contd.

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Neural Network Training and Target Data InputContd.

Training and Target Data are Browse from Data Bank

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Contd.

Neural Network Architecture

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Contd.

Neural Network Training• Training Algorithm• Data Partition

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Contd.

The Figure shows the changes between the validation, Training and testing where the Mean Square Error is minimum.

NN Training PerformanceContd.

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NN Training Performance

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Contd.

In evaluation of the network , if the learning process fails then retraining is required to achieve the goal

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Contd.

Generation of NN Program Code

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Contd.

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Performance Analysis of ANNContd.

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Overview of Hardware

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Technical Specifications of Arduino UNO Microcontroller : ATmega328Operating Voltage : 5VSupply Voltage (recommended) : 7-12VMaximum supply voltage : 20VDigital I/O Pins : 14 (of which 6 provide PWM output)Analog Input Pins : 6DC Current per I/O Pin :40 mA DC Current for 3.3V Pin :50 mAFlash Memory : 32 KB (ATmega328) of which 0.5 KB used by boot loader SRAM : 2 KB (ATmega328) EEPROM : 1 KB (ATmega328)Clock Speed :16 MHz

Matlab Hardware Support Package for Ardiuno UNO BoardMatlab to communicate with Arduino UNO Board over a USB cable

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Start MATLAB

Start Support Package Installer

Select Arduino UNO from a list of support packages

Math Works Account

Continue and Complete the Installation

Contd.

Matlab Simulink Model of Load Identification

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Contd.

Procedure to run the Model on Arduino UNO HardwareLoad the voltage and current samples into constants v1 & i1in the command window of Matlab

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Contd.

Configuration parameter setting to run the model on Arduino UNO hardware

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Procedure to run the Model on Arduino UNO HardwareContd.

Target hardware :Arduino UNO Host COM port : Automatically

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Contd.Procedure to run the Model on Arduino UNO Hardware

Deploy the Model to run on Hardware

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Contd.

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Load Identification IndicationContd.

Simulink Model run on Arduino UNO Hardware

Read analog signals using Arduino UNO ADC

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Contd.

Arduino IDE 1.0.6

Run Matlab code to read serial data Run Simulink Model for Load Identification automatically

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Contd.

Simulink model of Load Identification

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Contd.

Test set up to read analog signal through Arduino UNO ADC

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Contd.

Arduino UNO ADC data plotted on Matlab

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• Convenient and efficient use of electric appliances

• Remote access of the home electrical appliances

• Energy management strategies of Utility

• Utility to control the energy supply to a particular appliance

• Brief cost estimation of development of a Smart Plug

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1. Smart Grid Technology and Applications by Janaka Ekanayake(Cardiff University, UK), Kithsiri Liyanage(University of Peradeniya, Sri Lanka), Jianzhong Wu (Cardiff University, UK), Akihiko Yokoyama (University of Tokyo, Japan), Nick Jenkins (Cardiff University, UK). 2. Simulator for Smart Load Management in Home Appliances by Michael Rathmair and Jan Haase (Vienna University of Technology, Institute of Computer Technology). 3. Smart Power Grids 2011 by Professor Ali Keyhani Department of Electrical and Computer Engineering. 4. Experimental Study and Design of Smart Energy Meter for the Smart Grid by Anmar Arif, Muhannad AI-Hussain, Nawaf AI-Mutairi, Essam AI-Ammar Yasin Khan and Nazar Malik Saudi Aramco Chair in Electrical Power, Department of Electrical Engineering, College of Engineering (King Saud University).5. A model for generating household electricity load profiles by Jukka V. Paatero and Peter D. Lund Advanced Energy Systems, (Helsinki University of Technology Finland).6. Analysis and Application of Artificial Neural Network by L.P.J Veelenturf.7. ANN Based Load Identification And Forecasting System For The Built Environment by Hosen Hasna (University of Nebraska-Lincoln, hhasna@unomaha.ed).8. Principles Of Artificial Neural Networks 2nd Edition by Wai-Kai Chen (Univ. Illinois, Chicago, USA)9. Neural Networks by M. Hajek

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