A Survey on Optimal Design of Controller for AVR Performance Enhancement

Ahmed M. Mosaad*, Almoataz Y. Abdelaziz**, 0***
* Electrical and Gas Turbine Maintenance Engineer in Khalda-apache Petroleum Company, Cairo, Egypt.
** Professor, Department of Electrical Power and Machines, Faculty of Engineering, Ain Shams University, Cairo, Egypt.
*** Department of Electrical Power and Machines, Faculty of Engineering, Ain Shams University, Cairo, Egypt.
Periodicity:November - January'2018
DOI : https://doi.org/10.26634/jic.6.1.13937


This paper presents a survey on optimization techniques used to tune the controller parameters on Automatic Voltage Regulator (AVR) system. AVR is a device used to adjust the terminal voltage of synchronous generator. Since output voltage has slow response and instability, a Controller is used to improve stability and to get better response by minimizing maximum overshoot, reducing rise time, reducing settling time and improving steady state error. Proportional-Integral- Derivative (PID), Fraction Order PID (FOPID) and fuzzy logic are some examples of controllers which are used. Optimization techniques are used to tune the Controller due to nonlinear loads, time delays, variable operating points and others. There are different types of optimization techniques as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Harmony Search Algorithm (HSA), Local Unimodal Sampling (LUS), Teaching Learned Based Optimization (TLBO), and others. Researches are performed on different optimization techniques to improve terminal voltage response and stability.


AVR, Optimization, PID, FOPID.

How to Cite this Article?

Mosaad, A.M., Abdelaziz, A.Y., Attia, M.A. (2018). A Survey on Optimal Design of Controller for AVR Performance Enhancement. i-manager’s Journal on Instrumentation and Control Engineering, 6(1), 31-43. https://doi.org/10.26634/jic.6.1.13937


[1]. Ahmadi-Javid, A. (2011, June). Anarchic Society Optimization: A human-inspired method. In Evolutionary Computation (CEC), 2011 IEEE Congress on (pp. 2586- 2592). IEEE.
[2]. Alfi, A., & Modares, H. (2011). System identification and control using adaptive particle swarm optimization. Applied Mathematical Modelling, 35(3), 1210-1221.
[3]. Anand, H., & Dalal, V. (2014). Comparative Study of Particle Swarm Optimization and Fuzzy C-Means to Data Clustering. International Journal of Computer Applications Technology and Research, 3(1), 45-47.
[4]. Ang, K. H., Chong, G., & Li, Y. (2005). PID control system analysis, design, and technology. IEEE Transactions on Control Systems Technology, 13(4), 559-576.
[5]. Babu, G. S., & Dinesh, K. (2015, June). Implementation of fractional order PID controller for an AVR system. In Energy, Power and Environment: Towards Sustainable Growth (ICEPE), 2015 International Conference on (pp. 1- 6). IEEE.
[6]. Basir, M. A. B., & Ahmad, F. (2014). Comparison on Swarm Algorithms for Feature Selections/Reductions. International Journal of Scientific & Engineering Research, 5(8), 479-486.
[7]. Bendjeghaba, O., & Boushaki, S. I. (2013). Optimal Tuning of PID Controller in Automatic Voltage Regulator System using Improved Harmony Search Algorithm. Proceedings of the 7th Global Conference on Power Control and Optimization.
[8]. Bhati, S., & Nitnawwre, D. (2012). Genetic optimization tuning of an automatic voltage regulator system. IJSET, 1(3), 120-124.
[9]. Chaiyaratana, N., & Zalzala, A. M. S. (1997). Recent Developments in Evolutionary and Genetic Algorithms: Theory and Applications. 2nd Int. Conf. on Genetic Algorithms in Engineering System: Innovations and Applications, 2(4), 270-277.
[10]. Chatterjee, A., Mukherjee, V., & Ghoshal, S. P. (2009). Velocity relaxed and craziness-based swarm optimized intelligent PID and PSS controlled AVR system. International Journal of Electrical Power & Energy Systems, 31(7-8), 323- 333.
[11]. Chatterjee, S., & Mukherjee, V. (2016). PID controller for automatic voltage regulator using teaching–learning based optimization technique. International Journal of Electrical Power & Energy Systems, 77, 418-429.
[12]. Coelho, L. D. S. (2009). Tuning of PID controller for an automatic regulator voltage system using chaotic optimization approach. Chaos, Solitons and Fractals, 39(4), 1504-1514.
[13]. Colorni, A., Dorigo, M., & Maniezzo, V. (1992). Distributed optimization by ant colonies. Proc. of the First European Conference on Artificial Life (pp. 134-142).
[14]. Divya, K., & Seshadri, G. (2015). GA-PID tuned Stabilizer AVR system for Synchronous Generators. International Journal of Innovation Technology, 3(4), 438- 442.
[15]. Duman, S., Yorukeren, N., & Altas, I. H. (2016). Gravitational search algorithm for determining controller parameters in an automatic voltage regulator system. Turkish Journal of Electrical Engineering & Computer Sciences, 24(4), 2387-2400.
[16]. Fister, I., Fong, S., & Brest, J. (2014). A novel hybrid selfadaptive bat algorithm. The Scientific World Journal, 2014. Article ID 709738, doi:10.1155/2014/709738.
[17]. Fourie, J., Mills, S., & Green, R. (2010). Harmony filter: A robust visual tracking system using the improved harmony search algorithm. Image and Vision Computing, 28(12), 1702-1716.
[18]. Gaing, Z. L. (2004). A particle swarm optimization approach for optimum design of PID controller in AVR system. IEEE Transactions on Energy Conversion, 19(2), 384- 391.
[19]. Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A new heuristic optimization algorithm: Harmony search. Simulation, 76(2), 60-68.
[20]. Godjevac, J. (1997). Neuro-fuzzy Controllers: Design and Application. PPUR Presses Polytechniques.
[21]. Goldberg, D. E. (1989). Genetic Algorithms in Search Optimization and Machine Learning, Addison Wesley Publishers, Edmonton.
[22]. Gozde, H., & Taplamacioglu, M. C. (2011). Comparative performance analysis of artificial bee colony algorithm for Automatic Voltage Regulator (AVR) system. Journal of the Franklin Institute, 348(8), 1927-1946.
[23]. Gozde, H., Taplamacioglu, M. C., & Kocaarslan, I. (2010). Application of Artificial Bee Colony algorithm in an Automatic Voltage Regulator (AVR) system. International Journal on Technical and Physical Problems of Engineering, 1(3), 88-92.
[24]. Hasanien, H. M. (2013). Design optimization of PID controller in automatic voltage regulator system using Taguchi combined genetic algorithm method. IEEE Systems Journal, 7(4), 825-831.
[25]. Ho, S. L., Yang, S., Ni, G., Lo, E. W. C., & Wong, H. C. (2005). A particle swarm optimization-based method for multi-objective design optimizations. IEEE Transaction, Magnetics, 41(5), 1756-1759.
[26]. Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. MI. Ann Arbor: University of Michigan Press.
[27]. Howell, M. N., & Best, M. C. (2000). On-line PID tuning for engine idle-speed control using continuous action reinforcement learning automata. Control Engineering Practice, 8(2), 147-154.
[28]. Howell, M. N., & Gordon, T. J. (2001). Continuous action reinforcement learning automata and their application to adaptive digital filter design. Engineering Applications of Artificial Intelligence, 14(5), 549-561.
[29]. Hwang, C. C., Lyu, L. Y., Liu, C. T., & Li, P. L. (2008). Optimal design of an SPM motor using genetic algorithms and Taguchi method. IEEE Transactions on Magnetics, 44(11), 4325-4328.
[30]. Juang, C. F. (2004). A hybrid of genetic algorithm and particle swarm optimization for recurrent network design. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 34(2), 997-1006.
[31]. Jung, S., & Dorf, R. C. (1996). Analytic PIDA controller design technique for a third order system. In Decision and Control, Proceedings of the 35th IEEE Conference on (Vol. 3, pp. 2513-2518). IEEE.
[32]. Kansit, S., & Assawinchaichote, W. (2016). Optimization of PID Controller based on PSOGSA for an Automatic Voltage Regulator System. Procedia Computer Science, 86, 87-90.
[33]. Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization. Technical Report-TR06, Department of Computer Engineering, Erciyes University, Kayseri, Türkiye.
[34]. Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: Artificial Bee Colony (ABC) algorithm. Journal of Global Optimization, 39(3), 459-471.
[35]. Kashki, M., Abdel-Magid, Y. L., & Abido, M. A. (2008, September). A reinforcement learning automata optimization approach for optimum tuning of PID controller in AVR system. In International Conference on Intelligent Computing (pp. 684-692). Springer, Berlin, Heidelberg.
[36]. Kashki, M., Abdel-Magid, Y. L., & Abido, M. A. (2009). Application of novel reinforcement learning automata approach in power system regulation. Journal of Circuits, Systems, and Computers, 18(08), 1609-1625.
[37]. Kennedy, J., & Eberhart, R. (1995). Particle Swarm Optimization, Proceeding of IEEE International Conference on Neural Network (pp. 1942-1948).
[38]. Kim, D. H., & Cho, J. H. (2005, September). Intelligent control of AVR system using GA-BF. In International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (pp. 854-859). Springer, Berlin, Heidelberg.
[39]. Kim, D. H., & Cho, J. H. (2006). A biologically inspired intelligent PID controller tuning for AVR systems. International Journal of Control, Automation, and Systems, 4(5), 624-636.
[40]. Kim, D. H., & Park, J. I. (2005, August). Intelligent PID controller tuning of AVR system using GA and PSO. In International Conference on Intelligent Computing (pp. 366-375). Springer, Berlin, Heidelberg.
[41]. Kumar, V., & Mittal, A. P. (2010). Parallel fuzzy P+fuzzy I+ fuzzy D controller: Design and performance evaluation. International Journal of Automation and Computing, 7(4), 463-471.
[42]. Li, C., Li, H., & Kou, P. (2014). Piecewise function based gravitational search algorithm and its application on parameter identification of AVR system. Neurocomputing, 124, 139-148.
[43]. Li, L., Yang, Y., Peng, H., & Wang, X. (2006). An optimization method inspired by “chaotic” ant behavior. International Journal of Bifurcation and Chaos, 16(08), 2351-2364.
[44]. Li, X., Wang, Y., Li, N., Han, M., Tang, Y., & Liu, F. (2017). Optimal fractional order PID controller design for automatic voltage regulator system based on reference model using particle swarm optimization. International Journal of Machine Learning and Cybernetics, 8(5), 1595-1605.
[45]. Liu, Y., Qin, Z., & He, X. (2004, June). Supervisor-student model in particle swarm optimization. In Evolutionary Computation, 2004. CEC2004. Congress on (Vol. 1, pp. 542-547). IEEE.
[46]. Madasamy, G., & Ravichandran, C. S. (2015). Optimal Tuning of PID controller by BAT Algorithm in an Automatic Voltage Regulator System. International Journal of Innovative Science, Engineering & Technology, 2(1), 336-339.
[47]. Mallesham, G., & Rajani, A. (2006). Automatic tuning of PID controller using fuzzy logic. 8th International Conference on Development and Application Systems (pp. 120-127).
[48]. Mirjalili, S., & Hashim, S. Z. M. (2010, December). A new hybrid PSOGSA algorithm for function optimization. In Computer and information application (ICCIA), 2010 International Conference on (pp. 374-377). IEEE.
[49]. Mohanty, P. K., Sahu, B. K., & Panda, S. (2014). Tuning and assessment of proportional–integral–derivative controller for an automatic voltage regulator system employing local unimodal sampling algorithm. Electric Power Components and Systems, 42(9), 959-969.
[50]. Mohanty, P. K., Sahu, B. K., Panda, S., Kar, S. K., & Mishra, N. (2012, December). Performance analysis and design of Proportional Integral Derivative controlled automatic voltage regulator system using local unimodal sampling optimization technique. In International Conference on Swarm, Evolutionary, and Memetic Computing (pp. 566-576). Springer, Berlin, Heidelberg.
[51]. Mosaad, A. M., Attia, M. A., & Abdelaziz, A. Y. (2016). Optimization Techniques to tune the PID and PIDA Controllers for AVR Performance Enhancement. i-manager's Journal on Instrumentation & Control Engineering, 5(1), 1-10.
[52]. Mukherjee, V., & Ghoshal, S. P. (2007). Intelligent particle swarm optimized fuzzy PID controller for AVR system. Electric Power Systems Research, 77(12), 1689- 1698.
[53]. Nawikavatan, A., Tunyasrirut, S., & Puangdownreong, D. (2014, October). Application of intensified current search to optimum PID controller design in AVR system. In Asian Simulation Conference (pp. 255-266). Springer, Berlin, Heidelberg.
[54]. Nirmal, J. F., & Auxillia, D. J. (2013, March). Adaptive PSO based tuning of PID controller for an Automatic Voltage Regulator system. In Circuits, Power and Computing Technologies (ICCPCT), 2013 International Conference on (pp. 661-666). IEEE.
[55]. Oonsivilai, A., & Pao-La-Or, P. (2008). Application of adaptive tabu search for optimum PID controller tuning AVR system. WSEAS Transactions on Power Systems, 3(6), 495- 506.
[56]. Pan, I., & Das, S. (2013). Frequency domain design of fractional order PID controller for AVR system using chaotic multi-objective optimization. International Journal of Electrical Power & Energy Systems, 51, 106-118.
[57]. Panda, S., Sahu, B. K., & Mohanty, P. K. (2012). Design and performance analysis of PID controller for an automatic voltage regulator system using simplified Particle Swarm Optimization. Journal of the Franklin Institute, 349(8), 2609-2625.
[58]. Passino, K. M. (2001). Biomimicry of Bacterial Foraging for Distributed Optimization. University Press, Princeton, New Jersey.
[59]. Pedersen, M. E. H., & Chipperfield, A. J. (2008). Local Unimodal Sampling. HL0801 Hvass Laboratories.
[60]. Pedersen, M. E. H., & Chipperfield, A. J. (2010). Simplifying particle swarm optimization. Applied Soft Computing, 10(2), 618-628.
[61]. Petras, I. (1999). The fractional-order controllers: Methods for their synthesis and application. Electrical Engineering Journal, 50(9-10), 284-288.
[62]. Priyambada, S., Mohanty, P. K., & Sahu, B. K. (2014, December). Automatic voltage regulator using TLBO algorithm optimized PID controller. In Industrial and Information Systems (ICIIS), 2014 9th International Conference on (pp. 1-6). IEEE.
[63]. Priyambada, S., Sahu, B. K., & Mohanty, P. K. (2015, June). Fuzzy-PID controller optimized TLBO approach on automatic voltage regulator. In Energy, Power and Environment: Towards Sustainable Growth (ICEPE), 2015 International Conference on (pp. 1-6). IEEE.
[64]. Puangdownreong, D. (2012). Application of current search to optimum PIDA controller design. Intelligent Control and Automation, 3(04), 303-312.
[65]. Puangdownreong, D. (2015). Multiobjective multipath adaptive tabu search for optimal PID controller design. International Journal of Intelligent Systems and Applications, 7(8), 51-58.
[66]. Puangdownreong, D., Areerak, K. N., Srikaew, A., Sujitjorn, S., & Totarong, P. (2002, December). System identification via adaptive tabu search. In Industrial Technology, 2002. IEEE ICIT'02. 2002 IEEE International Conference on (Vol. 2, pp. 915-920). IEEE.
[67]. Puangdownreong, D., Kluabwang, J., & Sujitjorn, S. (2012). Multipath adaptive tabu search: Its convergence and application to identification problem. International Journal of Physical Sciences, 7(33), 5288-5296.
[68]. Puangdownreong, D., Sujitjorn, S., & Kulwora wanichpong, T. (2004). Convergence analysis of adaptive tabu search. International Journal of Science Asia, 38(2), 183-190.
[69]. Rajasekhar, A., Rani, R., Ramya, K., & Abraham, A. (2012, October). Elitist teaching learning opposition based algorithm for global optimization. In Systems, Man, and Cybernetics (SMC), 2012 IEEE International Conference on (pp. 1124-1129). IEEE
[70]. Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011). Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43(3), 303-315.
[71]. Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2012). Teaching–learning-based optimization: An optimization method for continuous non-linear large scale problems. Information Sciences, 183(1), 1-15.
[72]. Rashedi, E., Nezamabadi-Pour, H., & Saryazdi, S. (2009). GSA: A gravitational search algorithm. Information Sciences, 179(13), 2232-2248.
[73]. Sakulin, A., & Puangdownreong, D. (2012). A novel meta-heuristic optimization algorithm: Current search. Recent Researches in Artificial Intelligence and Database Management, 2(1), 125-130.
[74]. Sambariya, D. K., & Nath, V. (2015). Optimal control of automatic generation with Automatic Voltage Regulator using Particle Swarm Optimization. Universal Journal of Control and Automation, 3(4), 63-71.
[75]. Sambariya, D. K., & Paliwal, D. (2016). Design of PIDA controller using BAT algorithm for AVR power system. Advances in Energy and Power, 4(1), 1-6.
[76]. Sambariya, D. K., & Paliwal, D. (2016, March). Optimal design of PIDA controller using harmony search algorithm for AVR power system. In Power Systems (ICPS), 2016 IEEE 6th International Conference on (pp. 1-6). IEEE.
[77]. Sambariya, D. K., Gupta, R., & Prasad, R. (2016). Design of optimal input–output scaling factors based fuzzy PSS using bat algorithm. Engineering Science and Technology, an International Journal, 19(2), 991-1002.
[78]. Selvi, V., & Umarani, D. R. (2010). Comparative analysis of Ant Colony and Particle Swarm Optimization techniques. International Journal of Computer Applications, 5(4), 1-6.
[79]. Shayeghi, H., & Dadashpour, J. (2012). Anarchic society optimization based PID control of an Automatic Voltage Regulator (AVR) system. Electrical and Electronic Engineering, 2(4), 199-207.
[80]. Shayeghi, H., Younesi, A., & Hashemi, Y. (2015). Optimal design of a robust discrete parallel FP+FI+FD controller for the Automatic Voltage Regulator system. International Journal of Electrical Power & Energy Systems, 67, 66-75.
[81]. Shi, Y., & Eberhart, R. (1998, May). A modified particle swarm optimizer. In Evolutionary Computation Proceedings, 1998. IEEE World Congresson Computational Intelligence., The 1998 IEEE International Conference on (pp. 69-73). IEEE.
[82]. Soundarrajan, A., Sumathi, S., & Sundar, C. (2010). Ant colony optimization based PID tuning for AVR in autonomous power generating systems. International Journal of Recent Trends in Engineering and Technology, 3(4), 125-129.
[83]. Sujitjorn, S., Kulworawanichpong, T., Puangdo wnreong, D., Areerak, K. (2006). Adaptive tabu search and applications in engineering design. Integrated Intelligent Systems for Engineering Design, 149, 233-257.
[84]. Sultan, A. J. (2017). Optimal AVR Control System using Particle Swarm Optimization. International Journal of Advanced Research in Computer and Communication Engineering, 6(1), 139-142.
[85]. Tang, Y., Cui, M., Hua, C., Li, L., & Yang, Y. (2012). Optimum design of fractional order PID controller for AVR system using chaotic ant swarm. Expert Systems with Applications, 39(8), 6887-6896.
[86]. Ula, A. H. M. S., & Hasan, A. R. (1992). Design and implementation of a personal computer based automatic voltage regulator for a synchronous generator. IEEE Transactions on Energy Conversion, 7(1), 125-131.
[87]. Yadav, P., Kumar, R., Panda, S. K., & Chang, C. S. (2012). An intelligent tuned harmony search algorithm for optimisation. Information Sciences, 196, 47-72.
[88]. Yang, X. S. (2010). A new metaheuristic bat-inspired algorithm. In Nature Inspired Cooperative Strategies for Optimization (NICSO 2010) (pp. 65-74). Springer, Berlin, Heidelberg.
[89]. Zamani, M., Karimi-Ghartemani, M., Sadati, N., & Parniani, M. (2009). Design of a fractional order PID controller for an AVR using particle swarm optimization. Control Engineering Practice, 17(12), 1380-1387.
[90]. Zhang, Y., Li, Y., Xia, F., & Luo, Z. (2012, September). Immunity-based gravitational search algorithm. In International Conference on Information Computing and Applications (pp. 754-761). Springer, Berlin, Heidelberg.
[91]. Zhu, H., Li, L., Zhao, Y., Guo, Y., & Yang, Y. (2009). CAS algorithm-based optimum design of PID controller in AVR system. Chaos, Solitons & Fractals, 42(2), 792-800.

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