JNIT295PPL.pdf

Intelligent Load Management And Optimal Operation of a Wind-Diesel Hybrid Power System

Maged N. F. Nashed, Mona N. Eskander

Power Electronics and Energy Conversion Department Electronics Research Institute, Cairo, Egypt [email protected], [email protected]

Abstract Wind-Diesel hybrid power systems are particularly suited for locations where wind resource

availability is high and the cost of diesel fuel and generator are reasonable. In this paper, a control technique is designed to adjust the hybrid system performance aiming at minimum fuel consumption and continuous supply of critical loads. The research investigates optimizing the operation of a single diesel engine–single wind turbine hybrid energy generation system. Strategies for reducing fuel consumption are presented. Equations relating fuel consumption with load are derived. Fuzzy Logic-based decision-making framework is implemented for energy management to prevent diesel generator over-sizing, ensure continuous power supply to critical loads, allow optimum utilization of wind energy, and improve system stability. Two case studies are presented, the first represent a day of minimum wind energy and the second represent a day of abundant wind energy.

Keyword: Wind Energy, Diesel Engine, Fuzzy Logic Control, Microgrid

1. Introduction

The remote microgrid and mini-grid projects aim to investigate autonomous operation of the isolated power generation and distribution systems which use multiple energy sources to supply a community load. The immediate applications are for electrification of non-integrated areas based on alternative energy generation technologies, particularly with high penetration of renewable resources. The principal issues to consider are: optimal sizing of the power generation plant, control strategies, short-term power balancing, long term energy management, power quality, and reliability of the power supply system, [1-3].

Integration of renewable energy (RE) sources with fossil fuel based power generation systems for remote areas can offer attractive economical and environmental merits including considerable fuel savings and carbon dioxide emission reductions. In the case of autonomous wind-diesel systems that operate based on continuous utilization of one or more diesel generators without energy storage, various operating limits are imposed on the wind energy import to maintain adequate level of loading on the diesel generators for safe and reliable operation, [4-5].

Previous research on stand-alone hybrid wind-diesel system focused on different types of controllers to adjust the voltage and frequency of the grid [6]. Other researches dealt with the problems faced during the operation of the system in definite areas [7]. Neural network were designed to maintain good power quality under varying wind and load conditions [8]. In this paper the control technique is designed to adjust the hybrid system performance aiming at minimum fuel consumption and continuous supply of critical loads.

These goals are attained by developing a management plan using fuzzy logic (FL) aiming at maximum exploitation of wind energy and minimum consumption of fuel. This intelligent control system leads to creating a balance between the power generated and the load demand. Two case studies are presented to test the proposed system performance; during a day of minimum wind energy and a day of abundant wind energy. Figure 1 shows the investigated microgrid system. It consists of wind turbine equipped with induction generator, one diesel generator, and three load groups (sensitive load, adjustable load, and shedable load).

Tables showing the proposed generation system management scheme for the two case studies are given. These tables show the available wind energy, the corresponding consumed diesel fuel needed to supply the critical load, and the managed loads. FL rules are designed to execute the scheme of there tables. The detailed FL management scheme which aims at lower diesel fuel consumption, and better

Intelligent Load Management And Optimal Operation of a Wind-Diesel Hybrid Power System Maged N. F. Nashed, Mona N. Eskander

Journal of Next Generation Information Technology(JNIT) Volume 4, Number 9, November 2013

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utilization of wind energy is presented. Saving diesel engine fuel leads to lower cost of the generation system.

Figure 1. Layout of the Wind-Diesel Generation System.

2. Single Wind- Diesel Hybrid System

The selection and sizing of diesel generators in an autonomous power system depends on the nature of the load, [9-11]. Many remote villages are supplied with power for only a few hours of the day, mostly during evening hours or when there is sufficient demand. Many small power systems operate with a single diesel generator, due to simplicity in operation. 2.1. Diesel power plant

A 50 Kw Cummins B3.3 diesel engine is the main power generation source of the remote wind-

diesel power plant. It is a 4 cylinders engine with electric starting system and operation speed of 1800 rpm. The relation between the diesel engine output power and the coresponding fuel consumption is shown in Table I. It reveals that in actual application the diesel fuel consumption (liter/hour) is not in exact proportion to the load. For example, at a loading of 50%, the fuel consumption is 57% of consumption at full load, while at 10% load the fuel consumption is 18% of full load consumption. This shows that the efficiency of diesel generator always decreases with the decrease in load.

Table I. The consumption of diesel engine. Consumption at 1/10 load 1.9342 liter/hour

Consumption at 1/5 load 3.16832 liter/hour










Consumption at full load 10.599 liter/hour

Curve fitting is used to derive a mathematical formula describing the relation between the fuel consumption and the diesel output power as a polynomial equation as follows:

y = -0.017141143 + 17.741057 x + -16.054697 x2 + 8.9126400 x3 Where Y is the consumed fuel and X is the output power in per unit

This polynomial is shown in Fig. 2, while, Fig. 3 shows the equivalent energy consumed per liter versus the diesel engine loading.


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0 20 40 60 80 1000

2

4

6

8

10

Fuel

Con

sum

ptio

n (L

iter/h

r)

Diesel Output Power (%)

Figure 2. Fuel Consumption As Function of Diesel Engine Output Power.

0 20 40 60 80 1000

1

2

3

4

5

KW

h/Li

ter

Diesel Load %

Figure 3. Energy Consumed Per Liter Versus The Diesel Engine Loading.

2.2. Wind power plant A 600 KW horizontal-axis wind turbine is coupled to a squirrel cage induction generator is

employed with the diesel engine. The present study is based on wind data from El Hamam city in the West Egyptian desert. Figure 4 gives the average wind speed profile for a typical day on May where maximum wind speed occurs. Fig. 5 gives the average wind speed profile for a typical day on May where minimum wind speed occurs.

2.3. Load Profile The load supplied by the wind-diesel hybrid system consists of 3 types, i.e. 40 kW sensitive load

(important lighting, heat or air-condition, refrigeration, control system. communication…etc.), a 30 kW second priority load adjustable load (water pumping, lighting garden), and a 30 kW least priority load (warehouse lighting, washing Machine, pump of Swimming pool).

3. Operation of Wind-Diesel Hybrid System The control scheme proposed for managing the performance of the wind-diesel hybrid system is described in the flowchart shown in Fig. 6. Applying the proposed control scheme for the two case studies is described in the following sections.


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0 2 4 6 8 10 12 14 16 18 20 22 240

1

2

3

4

5

6

7

8

9

Spe

ed m

/sec

Hr.

Figure 4. High Wind Speed Profile.

0 2 4 6 8 10 12 14 16 18 20 22 240

1

2

3

4

5

6

7

8

9

Spe

ed m

/sec

Hr.

Figure 5. Low Wind Speed Profile.

3.1 First Case Study

The first case study is for a day of abundant wind energy. Figure 7 shows the wind power on the day of maximum available wind energy, the diesel engine power required to supply the suggested loads that cannot be supplied by the wind, and the total power. Table II details the share of each generator in supplying the load considering loads' priorities. The introduction of wind power reduces the load to be served by the diesel generators. When the load matches the wind power the diesel generator could be made idle.

3.2 Second Case Study

This case study is for a day of minimum wind energy. Figure 8 shows the wind power on the day of minimum available wind energy, the diesel power required to supply the suggested load that cannot be supplied by the wind, and the total power. Table III details the share of each generator in supplying the load considering loads' priorities


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Figure 6. The Flowchart of the System Control Scheme

0 2 4 6 8 10 12 14 16 18 20 22 24

0

20

40

60

80

100

120

Diesel

Wind

Load

Kw

Hour

Figure 7. Wind Power, Diesel Engine Power, & Load Demand

On The Day of Maximum Available Wind Energy.

Enter - Observed wind speed data - Hourly consumed energy CE

Calculate the Total Generation TG

If TG>CE

Start

If TG>CE Increase

Diesel

No

Power Diesel is

Max.

Yes

Decrease

No

Yes

If Diesel 0perates

No No

Yes

If Diesel 0perates

No

Decrease Diesel

Yes

Yes

Calculate the Total Generation TG


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Table II. The share of each generator in supplying the load considering loads' priorities on the day of maximum available wind energy

Load 3 30 Kw

Load 230 Kw

Load 140 Kw

Fuel Precent

Load Power

KW

Diesel Power

KW

Action Wind PowerKW

Wind speed m/sec

Time at 01-05

Full Full Full 0 100

0 Diesel Stop and resistance load 132 8.78, 0:00

Full Full Full 47% 100 20 Diesel work 40% 85 7.835,1:00 Full Full Full 57.1%100 25 Diesel Work 50% 76 7.575,2:00

96.67%Full Full 57.1%99 25 Diesel Work 50% 74 7.406,3:00 93.3% Full Full 67.25%98 30 Diesel Work 60% 68 6.852,4:00 93.3% Full Full 57.1%98 25 Diesel Work 50% 73 7.315,5:00 Full Full Full 29.9%100 10 Diesel Work 20% 90 7.931,6:00

98.3% Full Full 79.6%99.5 40 Diesel Work 80% 59.2 5.852,7:00 Full Full Full 0

100 0 Diesel stop and

resistance load 124 8.62, 8:00 Full Full Full 79.6%100 40 Diesel Work 80% 65 6.548,9:00 Full Full Full 79.6%100 40 Diesel Work 80% 66.5 6.700,10:00 Full Full Full 79.6%100 40 Diesel Work 80% 57 5.517,11:00 Full Full Full 79.6%100 40 Diesel Work 80% 62.5 6.283,12:00 Full Full Full 67.25%100 30 Diesel Work 60% 73.2 7.360,13:00 Full Full Full 57.1%100 25 Diesel Work 50% 77.5 7.713,14:00 Full Full Full 29.9%100 10 Diesel Work 20% 91 7.950,15:00 Full Full Full 79.6%100 40 Diesel Work 80% 60 6.040,16:00

93.3% Full Full 79.6%98 40 Diesel Work 80% 58 5.692,17:00 88.3% Full Full 79.6%96.5 40 Diesel Work 80% 56.8 5.504,18:00 97.67%Full Full 79.6%99.3 40 Diesel Work 80% 59.3 5.860,19:00

Full Full Full 79.6%100 40 Diesel Work 80% 61.8 6.181,20:00 99.3% Full Full 90.7%99.8 45 Diesel Work 90% 54.8 5.196,21:00 93.3% Full Full 90.7%98 45 Diesel Work 90% 53 4.713,22:00 97% Full Full 90.7%99.1 45 Diesel Work 90% 54.1 5.102,23:00 95.3 Full Full 90.7%98.6 45 Diesel Work 90% 53.6 5.023,24:00

0 2 4 6 8 10 12 14 16 18 20 22 240

10

20

30

40

50

60

70

80

90

100

Kw

Hour

Wind

Diesel

Load

Figure 8. Wind Power, Diesel Engine Power, & Load Demand

On the Day of Minimum Available Wind Energy.


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Table III. The share of each generator in supplying the load considering loads' priorities on the day of minimum available wind energy

Load 3 30 Kw

Load 2 30 Kw

Load 140 Kw

Fuel Precent

Load Power KW

Diesel Power KW

Action Wind Power KW

Wind speed m/sec

Time at 16-

05 No 33.3% Full

100%50

50 Diesel work full load and load 1 and important load 0 1.481, 0:00

No 33.3% Full 100%50

50

Diesel work full load and load 1 and important load 0 2.381, 1:00

No 33.3% Full 100%50


No 33.3% Full 100%50


93.3% Full Full 100%98 50 Diesel Work full load 48 2.404, 4:00 No 33.3% Full 100%

50 50 Diesel work full load and

load 1 and important load 0 2.081, 5:00 Full Full Full 100%100 50 Diesel Work full load 50 2.852, 6:00 Full Full Full 100%100 50 Diesel Work full load 51.4 3.156, 7:00 No 33.3% Full 100%

50 50 Diesel work full load and

load 1 and important load 0 2.356, 8:00 Full Full Full 100%100 50 Diesel Work full load 50 2.850, 9:00 Full Full Full 100%100 50 Diesel Work full load 50.2 2.908, 10:00 Full Full Full 100%100 50 Diesel Work full load 52.4 4.242, 11:00 Full Full Full 90.7%100 45 Diesel Work 90% 55.1 5.285, 12:00 94% Full Full 90.7%98.2 45 Diesel Work 90% 53.2 4.944, 13:00

93.3% Full Full 90.7%98 45 Diesel Work 90% 53 4.723, 14:00 95.3% Full Full 90.7%98.6 45 Diesel Work 90% 53.6 5.023, 15:00 99.67% Full Full 90.7%99.9 45 Diesel Work 90% 54.9 5.248, 16:00 99.67% Full Full 90.7%99.9 45 Diesel Work 90% 54.9 5.242, 17:00 92% Full Full 90.7%97.6 45 Diesel Work 90% 52.6 4.069, 18:00 97.67% Full Full 90.7%97 45 Diesel Work 90% 52 3.540, 19:00

No 33.3% Full 100%50


NO 33.3% Full 100%50


No 33.3% Full 100%50


NO 33.3% Full 100%50


Full Full Full 100%100 50 Diesel Work full load 51 3.056, 24:00

4. Operation Strategies Using Fuzzy Logic

Tables II and III are executed using FL rules. These rules are designed to match the load with

available power from wind and diesel generators. The loads are categorized to three types; critical loads that have to be supplied continuously, loads of medium priority, and loads of lower priority which are the first to be disconnected if a shortage in power supply occurs. Thus the proposed of operation strategy is as follows, [12-13]:

(1) The wind generator supplies the total load demand if the wind power equals or exceeds the load requirement.

(2) The diesel generator supplies the unsupplied load when the wind power is less than the load requirement.

(3) If the wind generator power and diesel generator power are less than the total load. The output power of diesel engine is increased. However if the diesel generator output power is at its maximum rating, the load is decreased starting with the lower priority loads.


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4.1. Inputs and Outputs of Fuzzy Logic Controller

Three variables are monitored and act as the inputs of the fuzzy controller. These variables are the wind generator power, the diesel engine power, and the load power. The two output variables of the controller are the change in the diesel power and the change in the load. Load change may be adding previously disconnected loads, or shedding non-critical loads, according to the available wind power and diesel engine power. FL control is chosen due to its high capacity to interpret linguistically the variables present in the system. Hence, starting from a group of measured data, the best conclusion regarding the operation of the system can be obtained.

For the parameter ‘Wind_power’, five membership functions are used, defined as: L_power (at low wind speed), soft, medium, hard and V_high (at maximum speed of wind).

For the parameter ‘Load_power’, three membership functions are used, with the definitions: Min_L (the critical load that cannot be disconnected), Med_L, and Full_L (all loads are connected).

For the parameter ‘Diesel’, five membership functions are used, with the definitions: low (load is low or medium but the wind power not enough), .2full, half, .8full, and full. The membership functions of the inputs are shown in Fig. 9. While, the membership functions of the outputs are shown in Fig. 10. The rules of the FL control system are in the table of Appendix I. While, the fuzzy system scheme is shown in Fig. 11.

0 20 40 60 80 100 120 140

0

0.5

1

Wind__Power

L__Power Soft V__hardMedium hard

0 10 20 30 40 50 60 70 80 90 1000

0.5

1

Load__PowerDeg

ree

of m

embe

rshi

p

import__L Med__L full__L

0 5 10 15 20 25 30 35 40 45 500

0.5

1

Diesel

Low half full.2full .8full

Figure 9. The Membership of Inputs Control.

-10 -8 -6 -4 -2 0 2 4 6 8 10

0

0.5

1

changeofDiesel

Deg

ree

of m

embe

rship N ZE P

-10 -8 -6 -4 -2 0 2 4 6 8 10

0

0.5

1

changeofload

Deg

ree

of m

embe

rship Z PhN N hP

Figure 10. The Membership of Outputs Control


16

Figure 11. The System Control Parts of FLC.

5. Controller Testing

The feasibility of the proposed control technique is tested with Matlab/Simulink software using the system parameters given in Appendix II. Applying the designed fuzzy management rules on the hybrid wind-diesel generation system led to quick system response when a sudden change in wind speed or a sudden change in the supplied load occurs, as shown in Fig. 12. This figure shows the available wind power and the controlled diesel power. The fast system response when applying the fuzzy managing controller is revealed by examining this figure. It shows that when wind speed decreases at t=25sec., the controller commands the diesel engine to increase its output power to substitute the loss in wind energy. The system response is fast allowing quick supply of load demand. At t = 50 sec., the load decreases from 72% to 67% while the available wind energy is the same, hence the controller commands the diesel engine to decrease its output power to 90%. At t=75 sec. the wind power increases allowing removed loads to be re-loaded.

Figure 13 shows the low overshoots in the corresponding current, ensuring the reliable performance of the generation system with the proposed control technique. From figures 12 and 13, it is worth noticing that the overshoots occurring due to sudden changes do not exceed 11% of the maximum values.

Figure 12. Change of Diesel Engine Power with the Change in Wind Power.


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Figure 13. Corresponding Change in System Current.

6. Conclusion:

In this paper optimum performance of a stand-alone hybrid wind-diesel generation system supplying 3 loads is achieved by designing a FL management scheme. The scheme allows quick response to sudden changes in available wind energy, and sudden load changes, as shown by simulation results. The proposed control scheme allows continuous supply of load demand with minimum possible diesel fuel consumption. The fuzzy rules proposed for system control and management are presented. An equation relating the diesel fuel consumed with the output power is derived. The flowchart describing the steps of system management is given. Two case studies are presented and tables given describing the diesel fuel consumption and the load management according to the available wind energy. These case studies are done using real data from El Hamam city in the Egyptian western desert for a day of minimum wind speed and another of maximum wind speed. 7. Appendix

7.1 Appendix I

Table IV. Fuzzy Rule Base. If Wind_power & Diesel & Load_power Then Change of Load Change of Diesel 1 L_power Low import_L Z P 2 Soft Low import_L Z P 3 Medium Low import_L Z N 4 Hard Low import_L Z N 5 V_hard Low import_L Z N 6 L_power .2full import_L Z P 7 Soft .2full import_L Z Z 8 Medium .2full import_L P N 9 Hard .2full import_L P N

10 V_hard .2full import_L P N 11 L_power half import_L Z Z 12 Soft half import_L Z N


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13 Medium half import_L Z N 14 Hard half import_L Z N 15 V_hard half import_L Z N 16 L_power .8full import_L Z N 17 Soft .8full import_L Z N 18 Medium .8full import_L Z N 19 Hard .8full import_L Z N 20 V_hard .8full import_L Z N 21 L_power full import_L Z N 22 Soft full import_L Z N 23 Medium full import_L Z N 24 Hard full import_L Z N 25 V_hard full import_L Z N 26 L_power Low Med_L hN P 27 Soft Low Med_L hN P 28 Medium Low Med_L N P 29 Hard Low Med_L Z N 30 V_hard Low Med_L Z N 31 L_power .2full Med_L hN P 32 Soft .2full Med_L N P 33 Medium .2full Med_L Z Z 34 Hard .2full Med_L Z N 35 V_hard .2full Med_L Z N 36 L_power half Med_L hN P 37 Soft half Med_L N P 38 Medium half Med_L Z N 39 Hard half Med_L Z N 40 V_hard half Med_L Z N 41 L_power .8full Med_L N P 42 Soft .8full Med_L N Z 43 Medium .8full Med_L Z N 44 Hard .8full Med_L Z N 45 V_hard .8full Med_L Z N 46 L_power full Med_L N Z 47 Soft full Med_L Z Z 48 Medium full Med_L Z N 49 Hard full Med_L Z N 50 V_hard full Med_L Z N 51 L_power Low full_L hN P 52 Soft Low full_L hN P 53 Medium Low full_L hN P 54 Hard Low full_L N P 55 V_hard Low full_L Z N 56 L_power .2full full_L hN P 57 Soft .2full full_L hN P 58 Medium .2full full_L hN P 59 Hard .2full full_L N P 60 V_hard .2full full_L N Z 61 L_power half full_L hN P 62 Soft half full_L hN P 63 Medium half full_L N P 64 Hard half full_L P Z 65 V_hard half full_L P N 66 L_power .8full full_L hN P 67 Soft .8full full_L hN P 68 Medium .8full full_L N Z 69 Hard .8full full_L Z N 70 V_hard .8full full_L Z N 71 L_power full full_L hN P 72 Soft full full_L hN P 73 Medium full full_L Z Z 74 Hard full full_L P N


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75 V_hard full full_L hP N

7.2. Appendix II

Squirrel cage Induction Generator P=600Kw, V= 380 V, F=50Hz, Rs=0.016 pu, LS= 0.06 pu Rr=0.015 pu, Lr=0.06 pu Msr= 3.5 pu Diesel engine Synchronous machine P=50Kw, S= 63 KVA V= 380 V, F=50Hz, Poles= 4, Xd=1.1 pu, Xq=1.0 pu, RS=0.0036 pu,

8. References [1] Mohammad Reza Aghaebrahimi, Mahmed Mehdizadeh, and Hamid Reza Najafi, “A new algorithm

for reliability assessment of wind-diesel system in islanding mode of operation” International Conference on Power Engineering, Energy and Electrical Drives (POWERENG), 2011.

[2] Michael Ross, Rodrigo Hidalgo, Chad Abbey, and Gabr Joós, “Energy storage system scheduling for an isolated microgrid” Renewable Power Generation, IET, Vol. 5, 2011, pp: 117 - 123

[3] Abo Haruni, Ameen Gargoom, Md Enamul Haque, and Michae Negnevitsky, “Dynamic operation and control of a hybrid wind-diesel stand alone power systems” IEEE Twenty-Fifth Annual Applied Power Electronics Conference and Exposition (APEC), 2010, pp: 162-169.

[4] Norman Lipman, “Overview of wind/diesel systems” Renewable Energy, vol.5, No. 1, August 1994, pp. 595–617.

[5] Gao Scott, Victor Wilreker, and Richard Shaltens, “Wind turbine generator interaction with diesel generators on an isolated power system,” IEEE Trans. Power Appar. Sys., no. PAS-103, pp. 933–937, 1984.

[6] Kjetil Uhlen, Bjarne Foss, and Ole Gjbmter, "Robust Control and Analysis of a Wind-Diesel Hybrid Power Plant" IEEE Transactions on Energy Conversion, Vol. 9, No. 4, December 1994, pp 701-708

[7] Hai Suhana, Gunung Agung Suteja, Agus Priyanto, and Pekik Argo Dahono, "An Operational Experience of Wind -Diesel Hybrid Power System in Indonesia" 10th International Conference on Environment and Electrical Engineering (EEEIC), 8-11 May, 2011,

[8] Ran Sebastian, Manuel Castro, Elio Sancristobal, Fent Yeves, Jouse Peire and Jon Quesada "Approaching hybrid wind-diesel systems and Controller Area Network" 28th Annual Conference of the IEEE Industrial Electronics Society, IECON-2002, 5-8 Nov., Vol. 4, 2002, pp 2300-2305.

[9] Arunan Arulampalam, Nadarajah Mithulananthan, Ramesh Bansal, and Tanusri Saha, “Micro-grid control of PV-Wind-Diesel hybrid system with islanded and grid connected operations” IEEE International Conference on Sustainable Energy Technologies (ICSET), 2010.

[10] Farid Katiraei, and Carlton Abbey, “Diesel Plant Sizing and Performance Analysis of a Remote Wind-Diesel Microgrid” IEEE Power Engineering Society General Meeting, 24-28 June, 2007.

[11] Mehdi Vafaei, and Mehrdad Kazerani, “Optimal unit-sizing of a wind-hydrogen-diesel microgrid system for a remote community” IEEE Power Tech, 19-23 June, 2011.

[12] Philip Panickar, Sheik Rahman, Sharif Islam, and Trevor Pryor “Adaptive Control Strategies in Wind-Diesel Hybrid Systems” AUPEC 2002 the Australasian Universities Power Engineering Conference 1 May 2002.

[13] Riad B. Chedid, Sami H. Karaki, and Chadi El-Chamali “Adaptive Fuzzy Control for Wind-Diesel Weak Power Systems” IEEE Transaction on Energy Conversion, Vol. 15, No. 1, March 2000.


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