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

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1 American Journal of Energy and Power Engineering 2017; 4(6): ISSN: AGC in Five Area Interconnected Power System of Thermal Generating Unit Through Fuzzy Controller Ashish Dhamanda 1, Gajendra Singh Rawat 1, Arunesh Dutt 2 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, Department of Electrical Engineering, Faculty of Engineering & Technology, Gurukula Kangri University, Haridwar, India 2 Department of Electrical & Electronics Engineering, Bhagwant Institute of Technology, Bijnore, India address dhamanda_ashish@yahoo.co.in (A. Dhamanda), gajendrarawat009@gmail.com (G. S. Rawat), aru_121@rediffmail.com (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 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

2 American Journal of Energy and Power Engineering 2017; 4(6): 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 45 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.

3 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 Transfer function of turbine is Transfer function of Reheat turbine is. Transfer function of generator is Dynamic response of automatic frequency control loop is Fs Equation [5] can be written as Fs P " # $% # & ' (1) (2) (3) (4) (5) & )& Power flow out of control area-1 can be expressed as * (6) P TL1 =,, -. / sin (δ 1 - δ 2 ) (7) tie-line power. Thus, for control area-1 we have ACE 1 = P TL1 + b 1 f 1 (10) Where b 1 = constant = area frequency bias. Taking Laplace transform on both sides of equation (10), we get ACE 1 (s) = P TL1 (s) + b1 F1(s) (11) Similarly, for control area-2, 3, 4 and 5 we have ACE 2 (s) = P TL2 (s) + b2 F2(s) (12) ACE 3 (s) = P TL3 (s) + b3 F 3 (s) (13) ACE 4 (s) = P TL4 (s) + b4 F 4 (s) (14) ACE 5 (s) = P TL5 (s) + b5 F 5 (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 = K p Error Signal + K p K i 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. Where 0 and 0 4 are voltage magnitude of area 1 and area 2, respectively, δ 1 and δ 2 are the power angles of equivalent machines of their respective area, and 5 67 is the tie line reactance. For Control Area-1 P TL1 (s) = 486 -:;< 9 = ;< 4 => (8) For Control Area-2 P TL2 (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 Figure 2. Proportional Plus Integral Plus Derivative Control Scheme Model. Control Area Input = K p Error Signal + K p K i (Error Signal A B C DEFGH + K p K d ) (17) A

4 American Journal of Energy and Power Engineering 2017; 4(6): 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], 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,,,, Z,,, 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) Change in Error (ce) 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 47 ZO PB PB PB ZO PB PB ZO PB ZO PB ZO 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.

5 48 Ashish Dhamanda et al.: AGC in Five Area Interconnected Power System of Thermal Generating Unit Through 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 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 6. Frequency deviation of area 2 in five area R system using PI controller. Figure 7. Frequency deviation of area 3 in five area R system using PI controller.

6 American Journal of Energy and Power Engineering 2017; 4(6): Figure 8. Frequency deviation of area 4 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. Figure 11. Deviation in Ptie of area 2-3 in five area R system using PI controller.

7 50 Ashish Dhamanda et al.: AGC in Five Area Interconnected Power System of Thermal Generating Unit Through Fuzzy Controller Figure 12. Deviation in Ptie of area 3-4 in five area R system using PI controller. Figure 13. Deviation in Ptie of area 4-5 in five area R system using PI controller. Figure 14. Deviation in Ptie of area 5-1 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 tieline deviation under the load disturbance of 0.01 p.u obtained by PI controller in shown in Table 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.

8 American Journal of Energy and Power Engineering 2017; 4(6): Figure 16. Frequency deviation of area 2 in five area R system using PID controller. Figure 17. Frequency deviation of area 3 in five area R system using PID controller. Figure 18. Frequency deviation of area 4 in five area R system using PID controller. Figure 19. Frequency deviation of area 5 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.

9 52 Ashish Dhamanda et al.: AGC in Five Area Interconnected Power System of Thermal Generating Unit Through Fuzzy Controller Figure 20. Deviation in Ptie of area 1-2 in five area R system using PID controller. Figure 21. Deviation in Ptie of area 2-3 in five area R system using PID controller. Figure 22. Deviation in Ptie of area 3-4 in five area R system using PID controller. Figure 23. Deviation in Ptie of area 4-5 in five area R system using PID controller.

10 American Journal of Energy and Power Engineering 2017; 4(6): Figure 24. Deviation in Ptie of area 5-1 in five area R system using PID controller. The tie-line power response of five areas has been obtained from figure 20 to figure 24. Settling time of these figures are 59sec, 47sec, 59sec, 58sec, 68sec to settle down the deviation in tie-line deviation. 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 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. Figure 26. Frequency deviation of area 2 in five area R system using Fuzzy controller.

11 54 Ashish Dhamanda et al.: AGC in Five Area Interconnected Power System of Thermal Generating Unit Through Fuzzy Controller Figure 27. Frequency deviation of area 3 in five area R system using Fuzzy controller. Figure 28. Frequency deviation of area 4 in five area R system using Fuzzy controller. Figure 29. Frequency deviation of area 5 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.

12 American Journal of Energy and Power Engineering 2017; 4(6): Figure 31. Deviation in Ptie of area 2-3 in five area R system using Fuzzy controller. Figure 32. Deviation in Ptie of area 3-4 in five area R system using Fuzzy controller. Figure 33. Deviation in Ptie of area 4-5 in five area R system using Fuzzy controller. Figure 34. Deviation in Ptie of area 5-1 in five area R system using Fuzzy controller.

13 56 Ashish Dhamanda et al.: AGC in Five Area Interconnected Power System of Thermal Generating Unit Through Fuzzy Controller The tie-line power response of five areas has been obtained from figure 30 to figure 34. Settling time of these figures are 38sec, 32sec, 33sec, 42sec, 38sec to settle down the deviation in tie-line deviation. 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

14 American Journal of Energy and Power Engineering 2017; 4(6): 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 Area 2 Area 3 Area 4 Area 5 Area 1 Area Area 2 Area Area 3 Area Area 4 Area Area 5 Area (Sec) (Sec) (Sec) (Sec) (Sec) 2 (Sec) 3 (Sec) 4 (Sec) 5 (Sec) 1 (Sec) PI PID Fuzzy 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; R 1 = R 2 = R 3 = R 4 = 2.4Hz/p.u MW; T sg1 = T sg2 = T sg3 = T sg4 = 0.08Sec; T ps1 = T ps2 = T ps3 = T ps4 = 20Sec; Tt 1 = Tt 2 = Tt 3 = Tt 4 = 0.3Sec; Tr 1 = Tr 2 = Tr 3 = Tr 4 = 10Sec; Kr 1 = Kr 2 = 0.5TU; Kr 3 = 3.33TU; Kr 4 = 3TU; a 12 = a 23 = a 34 = a 41 = 1; H 1 = H 2 = H 3 = H 4 = 5MW-S/MVA; P r1 = P r2 = P r3 = P r4 = 2000MW; K ps1 = K ps2 = K ps3 = K ps4 = 120 Hz/pu MW; K sg1 = K sg2 = K sg3 = K sg4 = 1; K t1 = K t2 = K t3 = K t4 = 1; D 1234 = 8.33*10-3 p.u MW/Hz.; B 1234 = 0.425p.u. MW/hz; P D1234 = 0.01p.u; T 12 = T 23 = T 34 = T 41 = MW/Radian; P tie max = 200MW. Nomenclature AGC P ri f f D i T sg T t T ps K sg K t Automatic Generation Control Rated power capacity of area i Nominal system frequency Change in supply frequency System damping area i Speed governor time constant Steam turbine time constant Power system time constant Speed governor gain constant Steam turbine gain constant K ps Bi P Di i H R a T P tie max Power system gain constant Frequency bias parameter Incremental load change in area i Subscript referring to area etc. Inertia constant Speed regulation of governor Ratio of rated power of a pair of areas four area system Synchronous coefficient of tie-line system 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. References [1] A. Magla, J. Nanda, Automatic Generation Control of an Interconnected Hydro- Thermal System Using Conventional Integral and Fuzzy logic Control, in Proc. IEEE Electric Utility Deregulation, Restructuring and Power Technologies, Apr [2] D. P. Kothari, Nagrath Modern Power System Analysis ; Tata McGraw Hill, Third Edition, [3] Elgerd O. I, Elctric Energy System Theory; An Introduction McGraw Hill, [4] Surya Prakash, S K Sinha, Four Area Load Frequency Control of Interconnected Hydro-Thermal Power System by Intelligent PID Control Technique ; /12, IEEE [5] K. P. SinghParmar, S. Majhi, D. P. Kothari, Optimal Load Frequency Control of an Interconnected Power System ; MIT International Journal of Electrical and Instrumentation Engineering Vol. 1, No. 1, Jan. 2011, pp. 1-5, ISSS No , MIT Publications. [6] Surya Prakash, S K Sinha, Intelligent PI Control Technique in Four Area Load Frequency Control of Interconnected Hydro-thermal Power System ; /12, IEEE [7] Rishabh Verma, Shalini Pal, Sathans, Intelligent Automatic Generation Control of Two-Area Hydrothermal Power System using ANN and Fuzzy Logic ; /13, IEEE 2013.

15 58 Ashish Dhamanda et al.: AGC in Five Area Interconnected Power System of Thermal Generating Unit Through Fuzzy Controller [8] Kiran Kumar Challa, P. S. Nagendra Rao, Analysis and Design of Controller for Two Area Thermal-Hydro-Gas AGC System ; /10, IEEE [9] S. Sivanagaraju, G. Sreenivasan, Power System Operation and Control. PEARSON (2011). [10] S. HasanSaeed, Automatic Control System, [11] Hadi Sadat, Power System Analysis. Tata MCGraw Hill [12] Ranjit Roy, S. P. Ghoshal, Praghnesh Bhatt, Evolutionary Computation based Four-Area Automatic Generaton Control in Restructured Environment ; /09, IEEE [13] Xiangjie Liu, Xiaolei Zhan, Dianwei Quian, Load Frequency Control considering Generation Rate Constraints ; /10, IEEE [14] Sapna Bhati, Dhiiraj Nitnawwre, Genetic Optimization Tuning of an Automatic Voltage Regulator System ; IJSET, Volume No. 1, Issue No. 3, pg: , ISSN: , 01 July [15] K. F. Man, K. S. Tang and S. Kwong, Genetic algorithm: Concepts and applications ; IEEE Trans. Ind. Electron, vol. 43, no. 5, pp , may [16] Naresh Kumari, A. N. Jha, Automatic Generation Control Using LQR based PI Conreoller for Five Area interconnected Power System ; Advance in Electronic and Electric Engineering, ISSN , Volume 4, pp , November 2, [17] S. Prakash, S. K. Sinha, Nero-Fuzzy Computational Technique to Control Load Frequency in Hydro-Thermal Interconnected Power System ; J, Inst, Eng, India Ser, B, DOI 10,1007/s , ISSN , Published by Springer 2014 [18] Surya Prakash, SK Sinha, Load Frequency Control of Multi- Area power System Using Neuro Fuzzy Hybrid Intelligent Controllers, IETE Journal of Research: DOI: / , Taylor & Francis, Apr [19] Arthur R. Bergen, Vittal Vijay, Power system analysis, second edition, Prentice- Hall Inc, [20] Ashish Dhamanda, Arunesh Dutt, A. K. Bhardwaj, Automatic Generation Control in Four Area Interconnected Power System of Thermal Generating Unit through Evolutionary Technique. International Journal on Electrical Engineering and Informatics (IJEEI), Volume 7, Number 4, DOI: /ijeei December [21] Ashish Dhamanda, A. K. Bhardwaj, AGC in Four Area Interconnected Power System of Thermal Generating Unit through Evolutionary Technique. Paper is accepted to publish in Research Journal of Applied Science, Engineering and Technology, 13 (2): , 2016 DOI: /rjaset ISSN: , Maxwell Science Publication July 15, 2016.