Available online at ScienceDirect. Procedia Computer Science 92 (2016 )

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1 Available online at ScienceDirect Procedia Computer Science 9 (6 ) 99 5 nd International Conference on Intelligent Computing, Communication & Convergence (ICCC-6) Srikanta Patnaik, Editor in Chief Conference Organized by Intercience Intitute of Management and Technology Bhubanewar, Odiha, Grey wolf optimizer algorithm baed Fuzzy PID controller for AGC of multi-area power ytem with TCPS Deepak Kumar Lal*, A. K. Barial, M. Tripathy Department of Electrical Engineering, Veer Surendra Sai Univerity of Technology Odiha, Burla , India Abtract In thi paper, a meta-heuritic optimization algorithm ha been applied to interconnected Hydro-thermal power ytem for automatic generation control (AGC). The optimal gain of the fuzzy baed proportional, integral and derivative (PID) controller are obtained by employing the propoed Grey Wolf Optimizer (GWO) algorithm. The generation rate contraint of 3% per minute for thermal power plant and 7% up and 36% down per minute for hydro plant have been conidered. The dynamic performance of a two-area interconnected Hydro-thermal power ytem i invetigated and compared with particle warm optimization (PSO) and differential evolution (DE) technique by incorporating a Thyritor Controlled Phae Shifter (TCPS) in erie with the tie-line. The overall performance of the conidered ytem ha been ignificantly improved by controlling the phae angle of TCPS with the help of propoed Fuzzy PID controller. It i revealed that the frequencie of both area and tie-line power ocillation are quickly damped out by the propoed cheme under tep load perturbation. Furthermore, the robutne of the propoed ytem i analyed when ubjected to different loading without recalculating the gain of the controller. 6 The Author. Publihed by by Elevier B.V. B.V. Thi i an open acce article under the CC BY-NC-ND licene ( Selection and peer-review under reponibility of cientific committee of IIMT Reearch Network, Bhubanewar, India Peer-review under reponibility of the Organizing Committee of ICCC 6 Keyword: automatic generation control (AGC); hydro-thermal ytem; thyritor control phae hifter (TCPS); particle warm optimization (PSO) algorithm; differential evolution (DE) algorithm; grey wolf optimizer (GWO) * Deepak Kumar Lal. Tel.: addre:laldeepak.ng@gmail.com The Author. Publihed by Elevier B.V. Thi i an open acce article under the CC BY-NC-ND licene ( Peer-review under reponibility of the Organizing Committee of ICCC 6 doi:.6/j.proc

2 Deepak Kumar Lal et al. / Procedia Computer Science 9 ( 6 ) Introduction Electric power ytem are interconnected to generate, exchange and control of electric energy with nominal frequencie and tie-line power interchange at their repective cheduled value. The principal apect of Automatic generation control (AGC) in power ytem i to maintain ytem frequency of each area and tie line power flow between area during normal operating condition a well a during the variation in load demand [-3]. Literature urvey how that coniderable work have been carried out for AGC of interconnected power ytem, including the pioneering work by Elgerd and Foha [4-5]. In pat two decade following the advent of modern intelligent technique uch a Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Fuzzy Logic (FL) and Artificial Neural Network (ANN), new idea have been emerged for the deign of AGC controller to improve the ytem dynamic under the occurrence of the load perturbation [6-4]. Further, improvement of ytem dynamic have been revealed with incluion of Flexible AC Tranmiion Sytem (FACTS) baed controller. Thee FACTS device are capable of enhancing power ytem tability by controlling the power flow in an interconnected power ytem [5]. Thyritor Controlled Phae Shifter (TCPS) belong to the family of FACTS device. It i intalled in erie with the tranmiion line. It i poible to regulate the power flow by connecting a TCPS in erie with the tie-line in between two area of an interconnected power ytem. It control the real power flow by controlling the phae angle of ytem voltage [6]. It alo reduce the frequency of ocillation of power flow following a udden change in load in either of area. Followed by introduction the paper i organized a follow: ytem invetigated i preented in ection. Section 3 preent modeling of TCPS in AGC. Controller tructure and objective function i decribed in ection 4. In ection 5, GWO algorithm i propoed. Reult and dicuion are preented in ection 6. Finally concluion i given in ection 7. Nomenclature P R, PR Rated area capacitie a P R / PR, R Governor peed regulation parameter of thermal and hydro area repectively T P, TP Power ytem time contant D, D PDi / fi K P, KP Power ytem gain, B Frequency bia contant of thermal and hydro area repectively T t Steam turbine time contant X Tranmiion line reactance T Governor time contant in thermal area K Gain parameter of TCPS G K r Steam turbine reheat contant T PS Time contant of TCPS T r Steam turbine reheat time contant max, Maximum and minimum phae angle min variation of TCPS T Synchronizing coefficient F, Deviation of frequency in area and area repectively T, T, T R Governor time contant of hydro area P tie Tie-line power deviation T Water time contant J Objective function w. Sytem invetigated The ytem invetigated for AGC i a two area interconnected hydro-thermal ytem with TCPS in erie with the tie-line [6-7]. A chematic of the two area interconnected hydro-thermal power ytem i hown in Fig.. Area compriing a reheat thermal ytem with generation rate contraint (GRC) of 3% per minute i conidered. Area compriing a hydro ytem and GRC of the order of 7% per minute (4.5%/econd) for riing in generation and 36% per minute (6%/econd) for lowering in generation i conidered. The detail tranfer function model i given in Fig.. The nominal parameter of the ytem are given in Appendix. For analyi of the ytem, % tep load perturbation ha been conidered in thermal area. 3. Modeling of TCPS in AGC The TCPS i connected in erie with the tie line and i placed near area. Reitance of the tie-line i neglected. Without TCPS, the incremental tie-line power flow from area to area can be expreed a,

3 Deepak Kumar Lal et al. / Procedia Computer Science 9 ( 6 ) 99 5 T P tie f f When a TCPS i placed in erie with the tie-line [6], power flow become, T K Ptie F F T F T tie Now, P P P PS tie TCPS (3) K PTCPS T F T Where, PS The TCPS i a frequency tabilizer. The gain K and time contant TPS of the TCPS are collected from reference [6]. Two area Hydro-Thermal ytem with GRC and TCPS Reheat Thermal Area () () B /R du/dt del Pd del f u Fuzzy PID Controller TG.+ Kr*Tr.+ Tr.+ Tt.+ Kp Tp.+ a del Ptie Kp T p.+ TCPS T *pi*t u Fuzzy PID Controller Hydro Area TR.+ T.+ T.+ -Tw+.5*Tw+ a Kp B /R du/dt Tp.+ del f Fig. MATLAB/Simulink model of two area Hydro-thermal ytem with GRC and TCPS 4. Controller tructure and objective function The tructure of the Fuzzy PID controller i adopted from reference [8]. An identical controller i employed in each area. The error input to the controller are the repective area control error (ACE). Fuzzy PID controller i a combination of fuzzy proportion-integral (PI) and fuzzy proportional-derivative (PD) controller. The input caling factor are K and K and the output caling factor are K 3 and K 4. Triangular memberhip function are ued with five fuzzy linguitic variable uch a NB (negative big), NS (negative mall), Z (zero), PS (poitive mall) and PB (poitive big) for both the input and the output. Mamdani fuzzy inference engine i elected for the preent work. The two-dimenional rule bae for error, error derivative are input and u a output to fuzzy model. Integral of time multiplied abolute error (ITAE) i ued a objective function to find the optimum value of the controller parameter [9]. The objective function J for controller parameter optimization of the interconnected power ytem i depicted below. t im F F P. t dt J ITAE (4) tie. del Pd

4 Deepak Kumar Lal et al. / Procedia Computer Science 9 ( 6 ) 99 5 In the above equation, F and F are the ytem frequency deviation; Ptie power. tim i the time range of imulation. 5. Overview of GWO algorithm i the incremental change in tie line Grey Wolf Optimizer (GWO) i a new meta-heuritic algorithm propoed by Mirjalili et al. in 4 for olving many multi-modal function []. It i inpired by grey wolve. Four type of grey wolve uch a alpha, beta, delta and omega are employed to derive the leaderhip of hierarchy of grey wolve. The main tep are hunting, earching for prey, encircling prey and attacking prey. 4. Social hierarchy i conidered to be the fittet olution followed by For modeling of the ocial behavior of the grey wolf, alpha beta and delta, repectively, and the ret of the candidate olution are grouped under omega the hunting (optimization) proce i guided by alpha, beta and delta, where a omega. In GWO, wolve alway follow thee three wolve. 4. Encircling prey Grey wolve encircle prey during the hunt. In order to mathematically model encircling behavior the following equation are preented. D C. X p t X t and X t X p t A. D (5) Where t indicate the current iteration. A and C are coefficient vector, X i the poition vector of the prey, and X indicate the poition vector of a grey wolf. The vector A and C are calculated a follow: A a. r a ; C. r (6) Where the component of a are linearly decreaed from to over the coure of iteration and r,r are random vector in [,]. 4.3 Hunting The following formula are propoed in thi regard. D C. X X, D C. X X, D C3. X X ; (7) X X A. D, X X A D, X3 X A3. D (8) X X X 3 X t (9) Attacking prey (exploitation) In the above ection, it i dicued that how the grey wolve finih the hunt by attacking prey when it top moving. In order to mathematically expre the model approaching the prey, two parameter, a decribed below are conidered. a i linearly decreaing from to and fluctuation of A i alo decreaed with a. 4.5 Search for prey (exploration) Optimum earch in grey wolf algorithm i baed on the poition of alpha, beta and delta. They diverge from each other when they earch for prey and converge during attacking the prey. Mathematically, when the random value of A i greater than or le than - then earch agent diverge to prey. Thi emphaize exploration behavior in GWO algorithm. Thu, GWO how more random behavior throughout the optimization and favoring exploration and local optima avoidance. Finally, the algorithm tep of GWO may be ummarized a follow: (a) The earch proce i tarted with random initialization of candidate olution (wolve) in the earch pace. (b) Alpha, beta and delta wolve are etimated baed on the poition of prey. (c) To find the optimum location of prey, each wolf update it poition. (d) A control parameter a linearly decreae from to for better exploitation and exploration. (e) Candidate olution tend to diverge when A and to converge when A and at the end GWO give the optimum olution. p

5 Deepak Kumar Lal et al. / Procedia Computer Science 9 ( 6 ) Reult and dicuion of the imulated tet ytem For DE algorithm the optimal gain are K P =.86 and K I =.76 (thermal); and K P =.853 and K I =.65(hydro). Similarly, for PSO algorithm the optimal gain are K P = and K I =.36 (thermal); and K P =.657 and K I =.668 (hydro). For GWO algorithm the PI controller gain are K P =.33 and K I =.368 (thermal); and K P =.6975 and K I = (hydro). For GWO algorithm the gain of Fuzzy PID controller are K =.66, K =.459, K 3 =.979, K 4 =.837(thermal) and K =., K =.534, K 3 =.464, K 4 =.786 (hydro). K and K are weight coefficient[8]..4. F in Hz -. Without TCPS Optimal I controller with TCPS [6] -.4 DE PI controller with TCPS PSO PI controller with TCPS -.6 GWO Fuzzy PID controller with TCPS GWO PI controller with TCPS Time (econd) Fig. Frequency deviation of area under a tep load perturbation of.pu in area.4. F in Hz Without TCPS Optimal I controller with TCPS [6] DE PI controller with TCPS PSO PI controller with TCPS -.6 GWO Fuzzy PID controller with TCPS GWO PI controller with TCPS Time (econd) Fig. 3 Frequency deviation of area under a tep load perturbation of.pu in area 5 x -3 P tie in p.u. MW Without TCPS -5 Optimal I controller with TCPS [6] DE PI controller with TCPS - PSO PI controller with TCPS GWO Fuzzy PID controller with TCPS GWO PI controller with TCPS Time (econd) Fig. 4 Tie line power deviation of ytem under a tep load perturbation of.pu in area

6 4 Deepak Kumar Lal et al. / Procedia Computer Science 9 ( 6 ) Time (econd) Fig. 5 Frequency deviation of area under different loading condition The dynamic performance of the two-area interconnected hydro-thermal power ytem i invetigated with propoed GWO optimized Fuzzy PID controller and compared with PSO, DE and GWO optimized PI controller by incorporating a TCPS in erie with the tie-line and with a tep load perturbation of %. Simulation reult of the ytem frequency deviation and tie-line power deviation are hown in Fig. 4. The ytem i imulated for 5 econd. The reult how that the performance of the ytem in term of overhoot and ettling time i better with the propoed controller. The effect of variation of operating loading condition by 5 % from their nominal value taking one at a time i hown in Fig, 5. It i proved form Fig. 5 that there i negligible effect on variation of loading condition on the deviation of frequency repone which validate the robutne of propoed controller. 7. Concluion In thi paper, the GWO optimized Fuzzy PID controller i propoed for the AGC tudy of two area nonlinear hydrothermal ytem with TCPS. The imulation reult are compared with optimal integral controller, PSO optimized PI controller, DE optimized PI controller and GWO optimized PI controller for the imilar ytem. The reult obtained from imulation confirm the effectivene of the propoed controller. Alo the robutne of the ytem i verified by varying the loading condition of +5% and -5% from their nominal value without changing the gain of controller. Reference F in Hz Nominal Loading + 5 % Loading - 5 % Loading [] P. Kundur, Power ytem tability and control. New York: McGraw Hill; 994. [] O. I. Elgerd, Electric Energy Sytem Theory: An Introduction, econd edition, McGraw Hill; 8. [3] D. P. Kothari, I. J. Nagrath, Modern Power Sytem Analyi, New Delhi: McGraw Hill;. [4] O. I. Elgerd, C. E. Foha, Optimum megawatt-frequency control of multi-area electric energy ytem, IEEE Tran Power Apparatu Sytem 97; PAS-89(4): [5] O. I. Elgerd, C. E. Foha, The megawatt-frequency control problem: a new approach via optimal control theory, IEEE Tran Power Apparatu Sytem 97; PAS-89(4): [6] Pingkang, Li, Zhu Hengjun, and Li Yuyun. "Genetic algorithm optimization for AGC of multi-area power ytem." In TENCON'. Proceeding. IEEE Region Conference on Computer, Communication, Control and Power Engineering, vol. 3, pp IEEE,. [7] Abdel-Magid, Youef L., and Ad Abido, "AGC tuning of interconnected reheat thermal ytem with particle warm optimization", In Electronic, Circuit and Sytem, 3. ICECS 3. Proceeding of the 3 th IEEE International Conference on, vol., pp IEEE, 3. [8] Yeşil, E., Güzelkaya, M., I. Ekin, "Self tuning fuzzy PID type load and frequency controller", Energy Converion and Management 45, no. 3 (4): [9] Ghohal S. P., "Application of GA/GA-SA baed fuzzy automatic generation control of a multi-area thermal generating ytem", Electric Power Sytem Reearch 7, no. (4): 5-7. [] Ghohal S. P., "Optimization of PID gain by particle warm optimization in fuzzy baed automatic generation control", Electric Power Sytem Reearch 7, no. 3 (4): 3-. [] Sahu, B. K., Pati, S., Panda, S., "Hybrid differential evolution particle warm optimiation optimied fuzzy proportional integral derivative controller for automatic generation control of interconnected power ytem", IET Generation, Tranmiion & Ditribution 8, no. (4): [] Gozde Haluk, M. Cengiz Taplamacioglu, and İlhan Kocaarlan, "Comparative performance analyi of Artificial Bee Colony algorithm in automatic generation control for interconnected reheat thermal power ytem", International Journal of Electrical Power & Energy Sytem 4, no. ():

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