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American Journal of Energy and Power Engineering 2017; 4(6): 44-58 http://www.aascit.org/journal/ajepe ISSN: 2375-3897

Keywords Automatic Generation Control (AGC), Proportional Plus Integral Plus Derivative (PID), Proportional Plus Integral (PI), Fuzzy Logic Controller, Dynamic Response, Five Area Received: August 12, 2017 Accepted: September 12, 2017 Published: November 2, 2017

AGC in Five Area Interconnected Power System of Thermal Generating Unit Through Fuzzy Controller

Ashish Dhamanda1, Gajendra Singh Rawat

1, Arunesh Dutt

2

1Department of Electrical Engineering, Faculty of Engineering & Technology, Gurukula Kangri University, Haridwar, India

2Department of Electrical & Electronics Engineering, Bhagwant Institute of Technology, Bijnore, India

Email address [email protected] (A. Dhamanda), [email protected] (G. S. Rawat), [email protected] (A. Dutt)

Citation Ashish Dhamanda, Gajendra Singh Rawat, Arunesh Dutt. AGC in Five Area Interconnected Power System of Thermal Generating Unit Through Fuzzy Controller. American Journal of Energy and

Power Engineering. Vol. 4, No. 6, 2017, pp. 44-58.

Abstract Automatic Generation Control (AGC) is an important issue in power system operation and control for supplying sufficient and reliable electric power with good quality. As the number of generating unit increases in generating station, the AGC’s issue also having great importances. This paper help to improve the dynamic response of load frequency and corresponding tie-line power of five area interconnected thermal power system by using three different Controller; One is Fuzzy Logic Controller, Second is PID Controller and Third is PI Controller. Fuzzy Logic Controller are proposed controller and dynamic response of load frequency and tie-line power obtained by proposed controller and compared with the PI and PID controller’s response. The results show that the proposed controller exhibit better performance and satisfy the automatic generation control requirements with a reasonable dynamic response. The performances of the controllers are simulated using MATLAB/SIMULINK software.

1. Introduction

In modern time, major changes have been introduced into the structure of electric power utilities all around the world. The successful operation in power system requires the matching of total generation with total load demand and associated system losses. As the demand deviates from its normal value with an unpredictable small amount, the operating point of power system changes, and hence, system may experience deviations in nominal system frequency which may yield undesirable effects. So the objective of AGC in interconnected thermal generating unit is to maintain the system frequency and tie line power at nominal value (60 Hz) [4], [5], [17], [18], [20], [21].

A control strategy is needed to maintain constancy of frequency and tie-line power and also achieves zero steady state error. The fuzzy controller employed to solve AGC problem and these controller gives the good response, reduces the oscillation & steady state error, over the PI and PID controller.

The basic task of power system is to maintain the balance between power demand and power generation, to provide users with reliable, high-quality electric power [13]. Automatic Generation Control (AGC) is a generic term used to designate the automatic

American Journal of Energy and Power Engineering 2017; 4(6): 44-58 45

regulation of the mechanical power input to the synchronous generators within a predefined control area. Since the load of a power system is always changing, at least one generator in a power system must respond to the changing load in order to maintain the power balance. To maintain the power balance, the maintenance of system frequency should be close to the nominal value, is also important. The ACE (Area Control Error) measures the balance of generation and demand for

electricity, and the contract adherence between control areas. This is referred to as secondary control and requires each control area to meet its own demand and, as a result, maintain the nominal frequency in the system [19].

The control action comprises of different controller like PI, PID and Fuzzy controller. The model of five area thermal generating unit with controller is shown in below figure 1;

Figure 1. Five Area AGC Model of Thermal Generating System Common for all Controller.

46 Ashish Dhamanda et al.: AGC in Five Area Interconnected Power System of Thermal Generating Unit Through Fuzzy Controller

Let us consider the problem of controlling the power

output of the generators of a closely knit electric area so as to maintain the scheduled frequency. All the generators in such an area constitute a coherent group so that all the generators speed up and slow down together maintaining their relative power angles. Such an area is defined as a control area. To understand the AGC problem of frequency control, let us consider a single turbo-generator system supplying an isolated load. [2]

To simplicity the frequency-domain analyses, transfer functions are used to model each component of the area. [4], [17], [18]

Transfer function of governor is

�� (1)

Transfer function of turbine is

�� (2)

Transfer function of Reheat turbine is

�.� � � � � �� (3)

Transfer function of generator is

�� (4)

Dynamic response of automatic frequency control loop is

∆F�s� � ��

�� ∆�� (5)

Equation [5] can be written as

∆F�s� � �∆P" #�$%#��&� '�� – �

�� &�� )&�* (6)

Power flow out of control area-1 can be expressed as

PTL1 = |,�| |,-|

.�/ sin (δ1- δ2) (7)

Where|0�| and |04| are voltage magnitude of area 1 and area 2, respectively, δ1 and δ2 are the power angles of equivalent machines of their respective area, and 567 is the tie line reactance.

For Control Area-1

∆PTL1 (s) = 486�-9 :;<��=� � ;<4�=�> (8)

For Control Area-2

∆PTL2 (s) = ?48@�-6�-9 :;<��=� � ;<4�=�> (9)

In single area power system, Area Control Error (ACE) is the change in frequency. In a two power system, ACE is the linear combination of the change in frequency and change in

tie-line power. Thus, for control area-1 we have

ACE1 = ∆PTL1 + b1 ∆f1 (10)

Where b1 = constant = area frequency bias. Taking Laplace transform on both sides of equation (10), we get

ACE1(s) = ∆PTL1 (s) + b1∆F1(s) (11)

Similarly, for control area-2, 3, 4 and 5 we have

ACE2(s) = ∆PTL2 (s) + b2∆F2(s) (12)

ACE3(s) = ∆PTL3 (s) + b3 ∆F3(s) (13)

ACE4(s) = ∆PTL4 (s) + b4 ∆F4(s) (14)

ACE5(s) = ∆PTL5 (s) + b5 ∆F5(s) (15)

Equation (11), (12), (12), (14), (15) indicate that a control signal made of tie-line flow deviation added to frequency deviation weighted by a bias factor would accomplish the desired objectives. This control signal is known as area control error (ACE).

2. Controller

In automatic generation control of thermal generating unit need to control or maintain the frequency constancy, reduced oscillation and zero steady state error, so following types of controller are used, [10], [21]

2.1. PI Controller

This is a combination of proportional and integral control action.

Control Area Input = Kp Error Signal + Kp Ki ∫Error Signal (16)

2.2. PID Controller

This is a combination of proportional, integral and derivative controller so called three action controllers. This controller are using from many year back for controlling such action with maintaining their performance.

Figure 2. Proportional Plus Integral Plus Derivative Control Scheme Model.

Control Area Input = Kp Error Signal + Kp Ki ∫ (Error Signal

+ KpKdA BC DE�FGH

A� ) (17)


2.3. Fuzzy Logic Controller

Fuzzy logic establishes the rules of a nonlinear mapping. There has been extensive use of fuzzy logic in control applications. One of its main advantages is that controller parameters can be changed very quickly depending on the system dynamics because no parameter estimation is required in designing controller for nonlinear systems. Fuzzy logic controller is shown below [6],

Figure 3. Fuzzy Logic Control Scheme Model.

The variable error is equal to the real power system frequency deviation (∆f). The inputs of the proposed fuzzy

controller are error ne, and rate of change in error nce. The appropriate membership function and fuzzy rule base is shown in below, where 7 membership function, NB, NM, NS, Z, PS, PM, and PB represent negative big, negative medium, negative small, zero, positive small, positive medium, and positive big, respectively make 49 (7×7) rule [7].

Table 1. Fuzzy Inference Rule.

Error (e)

NB NM NS ZO PS PM PB

Change in Error (ce)

NB PB PB PB PB PM PM PS NM PB PM PM PM PS PS PS

NS PM PM PS PS PS PS ZO ZO NS NS NS ZO PS PS PS

PS ZO NS NS NS NS NM NM PM NS NS NM NM NM NB NB

PB NS NM NB NB NB NB NB

The Five area model of AGC of an interconnected power system for thermal generating unit using Fuzzy controller is shown in figure 4, and comparative response of frequency and tie line power can be obtained in figure 35 and figure 36

Figure 4. Five Area AGC Model of Thermal Generating System Using Fuzzy Controller.


3. Results and Discussion

To investigate the performance of five areas thermal generating system, all the simulation results are carried out by using MATLAB/Simulink software. The step load disturbance of 0.01 p.u. was applied in five areas for all the cases and deviations in frequency and corresponding tie-line power were investigated. The AGC performance through fuzzy logic controller is compared with PI and PID controller. The change in frequency and corresponding tie-line deviation under the load disturbances of 0.01 p.u. in five areas are

shown in figure 5 to Figure 34 and combined response obtain in figure 35 and figure 36. Comparative value of settling time shown in Table 2, it is observed that the fuzzy logic controller improve the dynamic performance of the system as compared to PI and PID Controller. The power system parameters are given in appendix.

3.1. PI Controller

Five area system response obtained from PI controller are shown below;

Figure 5. Frequency deviation of area 1 in five area R system using PI controller.





Figure 9. Frequency deviation of area 5 in five area R system using PI controller\.

The frequency response of area 1, area 2, area 3, area 4 and area 5, after frequency deviation under the load disturbance of 0.01 p.u obtained by PI controller are shown from figure 5 to figure 9. The settling time of the five areas developed model after frequency deviation are 36sec, 37sec, 38sec, 31sec, 31 second.

Figure 10. Deviation in Ptie of area 1-2 in five area R system using PI controller.






The tie-line power response of five areas has been obtained from figure 10 to figure 14. Settling time of these figures are 70sec, 44sec, 55sec, 68sec, 78sec to settle down the deviation in tie-line deviation.

The step load disturbance of 0.01 p.u was applied in five areas reheat thermal generating system and deviation in frequency and corresponding tie-line power were obtained. The settling time of five area system after frequency and tie-

line deviation under the load disturbance of 0.01 p.u obtained by PI controller in shown in Table 2.

3.2. PID Controller

Five area reheat system response obtained from PID controller are shown below;

Figure 15. Frequency deviation of area 1 in five area R system using PID controller.






The frequency response of area 1, area 2, area 3, area 4 and area 5, after frequency deviation under the load disturbance of 0.01 p.u obtained by PI controller are shown from figure 15 to figure 19. The settling times of the five areas developed model after frequency deviation are 38sec, 38sec, 39sec, 20sec, 30sec.


Figure 20. Deviation in Ptie of area 1-2 in five area R system using PID controller.







The settling time of five area system after frequency and tie-line deviation under the load disturbance of 0.01 p.u obtained by PID controller in shown in Table 2.

3.3. Fuzzy Logic Controller

Five area reheat system response obtained from Fuzzy logic controller are shown below;

Figure 25. Frequency deviation of area 1 in five area R system using Fuzzy controller.






The frequency response of area 1, area 2, area 3, area 4 and area 5, after frequency deviation under the load disturbance of 0.01 p.u obtained by PI controller are shown from figure 25 to figure 29. The settling time of the five areas developed model after frequency deviation are 23sec, 21sec, 21sec, 20sec, 20sec.

Figure 30. Deviation in Ptie of area 1-2 in five area R system using Fuzzy controller.



For simplicity the combined response of five area AGC thermal generating system has been plotted between frequency deviation and settling time in figure 35.

Figure 35. Combined Response of Frequency Deviation of Area 1.

The combined response of five area AGC thermal generating system has been plotted between tie line deviation and settling time in figure 36.

Figure 36. Combined Response of Tie Line Power Deviation of Area 1.

All the results are carried out by MATLAB/Simulink software. The step load disturbance of 0.01 p.u was applied in five areas thermal generating system and deviation in frequency and corresponding tie-line power response was obtained by using


fuzzy, PI and PID controller. The comparative table of settling time of five area systems after frequency and tie-line deviation under the load disturbance of 0.01 p.u is shown in Table 2.

Table 2. Comparative value of settling time.

Frequency Deviation’s Separate Thermal Area

Settling Time (Sec) Tie-Line Deviation’s Thermal-Thermal Settling Time (Sec)

Controllers Area 1

(Sec)

Area 2

(Sec)

Area 3

(Sec)

Area 4

(Sec)

Area 5

(Sec)

Area 1 Area

2 (Sec)

Area 2 Area

3 (Sec)

Area 3 Area

4 (Sec)

Area 4 Area

5 (Sec)

Area 5 Area

1 (Sec)

PI 36 37 38 31 31 70 44 55 68 78 PID 38 38 39 20 30 59 47 59 58 68 Fuzzy 23 21 21 20 20 38 32 33 42 38

From the Table 2, it is clear that the proposed controller

(Fuzzy Logic) gives the best value of settling time for both frequency and tie line power deviation in comparison to PI and PID controller.

4. Conclusions

The settling time of developed five area model for thermal generating system is investigated in this paper. To demonstrate the effectiveness of proposed controller (Fuzzy Logic), the control strategy based on PI and PID controller is applied. The performance of these controllers is evaluated through the MATLAB/Simulink Software. The results are tabulated in Table 2 respectively.

The results of proposed controller have been compared with PI and PID controller and it shows that the proposed controller give good dynamic response. So it can be concluded that the fuzzy controller gives good settling time than the PI and PID controllers.

Appendix

Power System Parameters are as follows: f = 60Hz; R1 = R2 = R3 = R4 = 2.4Hz/p.u MW; Tsg1 = Tsg2

= Tsg3 = Tsg4 = 0.08Sec; Tps1 = Tps2 = Tps3 = Tps4 = 20Sec; Tt1 = Tt2 = Tt3 = Tt4 = 0.3Sec; Tr1 = Tr2 = Tr3 = Tr4 = 10Sec; Kr1 = Kr2 = 0.5TU; Kr3 = 3.33TU; Kr4 = 3TU; a12 = a23 = a34 = a41 = 1; H1 = H2 = H3 = H4 = 5MW-S/MVA; Pr1 = Pr2 = Pr3 = Pr4 = 2000MW; Kps1 = Kps2 = Kps3 = Kps4 = 120 Hz/pu MW; Ksg1 = Ksg2 = Ksg3 = Ksg4 = 1; Kt1 = Kt2 = Kt3 = Kt4 = 1; D1234 = 8.33*10-3p.u MW/Hz.; B1234 = 0.425p.u. MW/hz; ∆PD1234 = 0.01p.u; T12 = T23 = T34 = T41 = 0.0867MW/Radian; Ptie max = 200MW.

Nomenclature

AGC Automatic Generation Control Pri Rated power capacity of area i f Nominal system frequency ∆f Change in supply frequency Di System damping area i Tsg Speed governor time constant Tt Steam turbine time constant Tps Power system time constant Ksg Speed governor gain constant Kt Steam turbine gain constant

Kps Power system gain constant Bi Frequency bias parameter ∆PDi Incremental load change in area i i Subscript referring to area 1 2 3 etc. H Inertia constant R Speed regulation of governor

a Ratio of rated power of a pair of areas four area system

T Synchronous coefficient of tie-line system Ptie max Tie-line power

Acknowledgements

This work is supported by electrical engineering department, Faculty of Engineering and Technology, Gurukula Kangri Vishwavidyalaya Haridwar and electrical engineering department, Sam Higginbottom Institute of Agriculture Technology and Sciences, Allahabad, India.

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