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Design of Evolutionary Algorithm Based PID Controller with filter for an Automatic Voltage Regulator

Year 2020, , 74 - 90, 15.06.2020
https://doi.org/10.31466/kfbd.719953

Abstract

The automatic voltage regulator (AVR) system is commonly used in power systems to keep the terminal voltage of generator at a specified level. The terminal voltage level is controlled by using different controllers in an AVR sistem. Researchers aim to improve dynamic performance of the AVR system and to decrease the steady state error to zero by using different controllers in their studies. They design controllers by utilizing evolutionary algorithms. Evolutionary algorithms are widely used to optimally tune controller parameters according to predefined objective function. In this study, two different proportional-integral-derivative with filter (PID-F) controllers are designed for the AVR system. Atom search optimization (ASO) and particle swarm optimization (PSO) algorithms are used to tune the parameters of the controllers. For each controller, transient response analysis, frequency analysis, and robustness analysis are examined in Matlab/Simulink for the AVR system and performance comparison is made. The results indicate that the ASO algorithm achieves better results than the PSO algorithm. In addition, it is concluded that the PID-F controller designed with ASO algorithm improves the transient response characteristics, stability and robustness compared to the classical PID controller of which the parameters are tuned by ASO, PSO, Biogeography based optimization (BBO) and Artificial bee colony (ABC).

References

  • Al Gizi, A, J., (2018). A particle swarm optimization, fuzzy PID controller with generator automatic voltage regulator. Soft Computing, 23, 8839–8853.
  • Anbarasi, S., Muralidharan, S., (2016). Enhancing the Transient Performances and Stability of AVR System with BFOATuned PID Controller. Control Engineering and Applied Informatics, 18(1), 20-29.
  • Ayas, M. S., (2019). Design of an optimized fractional high-order differential feedback controller for an AVR system. Electrical Engineering, 101, 1221-1233.
  • Bingul, Z., Karahan, O., (2018). A novel performance criterion approach to optimum design of PID controller using cuckoo search algorithm for AVR system. Journal of the Franklin Institute, 355(13) 5534-5559.
  • Blondin, M. J., Sanchis, J., Sicard, P., Herrero, J. M., (2018). New optimal controller tuning method for an AVR system using asimplified Ant Colony Optimization with a new constrained Nelder–Mead algorithm. Applied Soft Computing, 62, 216-229.
  • Bourouba, B., Ladaci, S., Schulte, H., (2019). Optimal Design of Fractional Order PID Controller for an AVR System using Ant Lion Optimizer. IFAC-Papersonline, 52(13), 200-205.
  • Coelho, L. S., (2009). Tuning of PID controller for an automatic Voltage regulator system using chaotic optimization approach. Chaos, Solitons and Fractals 39 (4) 1504-1514.
  • Çelik, E., Durgut, A., (2018). Performance enhancement of automatic voltage regulator by modified cost function and symbiotic organisms search algorithm. Engineering Science and Technology, an International Journal, 21, 1104-1111.
  • Gaing, ZL., (2004). A particle swarm optimization approach for optimum design of PID controller in AVR system. IEEE Transaction on Energy Conversation, 19(2), 384–391.
  • 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.
  • Guvenc U, Yigit T, Isik AH and Akkaya I (2016) Performance analysis of biogeography-based optimization for automatic voltage regulator system. Turkish Journal of Electrical Engineering and Computer Sciences 24(3): 1150–1162.
  • Hameed, N. S. S., Othman, W. A. F. W., Wahab, A. A. A., Alhady, S. S. N., (2019). Optimising pid controller using bees algorithm and firefly algorithm. ROBOTIKA, 1(1), 22-27.
  • Hekimoğlu, B., Ekinci, S., (2018, June). Grasshopper optimization algorithm for automatic voltage regulator system. 2018 5th International Conference on Electrical and Electronic Engineering (ICEEE) (pp. 152-156), Istanbul.
  • Hekimoğlu, B., (2019). Sine-cosine algorithm-based optimization for automatic voltage regulator system. Transactions of the Institute of Measurement and Control, 41(6), 1761-1771.
  • Kennedy, J., Eberhart, R., (1995). Particle Swarm Optimization, Proceedings of the IEEE International Conference on Neural Networks, 4, 1942-1948.
  • Sahib, M. A., (2015). A novel optimal PID plus second order derivative controller for AVR system. Engineering Science and Technology, 18, 194-206.
  • Sambariya, D. K., Paliwal, D., (2016). Optimal design of PIDA controller using harmony search algorithm for AVR power system. 2016 IEEE 6th International Conference on Power Systems (ICPS) (pp. 1-6). New Delhi.
  • Suribabu, A. G., Chiranjeevi, B. T., (2016). Implementation of Fractional Order PID Controller for an AVR System Using GA and ACO Optimization Techniques. IFAC-Papersonline, 49(1), 456-461.
  • Tang, Y., Li, X., Wang, Y., Li, N., Han, M. ve Liu, F., (2017). Optimal fractional order PID controller design for automatic voltage regulator system based on reference model using particle swarm optimization. Int. J. Mach. Learn. & Cyber, 8, 1595–1605.
  • Verma, S. K., Yadav, S., Nagar, S. K., (2017). Optimization of Fractional Order PID Controller Using Grey Wolf Optimizer. J Control Autom Electr Syst, 28, 314-322.
  • Zhao, W., Wang, L., Zhang, Z., (2019). Atom search optimization and its application to solve hydrogeologic parameter estimation problem. Knowledge-Based Systems, 163, 283–304.

Otomatik Gerilim Regülatörü için Evrimsel Algoritma Tabanlı Filtreli PID Denetleyici Tasarımı

Year 2020, , 74 - 90, 15.06.2020
https://doi.org/10.31466/kfbd.719953

Abstract

Otomatik gerilim regülatör (OGR) sistemi, generatör terminal gerilimini belirtilen seviyede tutmak için güç sistemlerinde yaygın olarak kullanılır. OGR sisteminde farklı denetleyiciler kullanılarak generatör terminal geriliminin denetimi gerçekleştirilmektedir. Araştırmacılar yaptıkları çalışmalarda OGR sisteminin dinamik performansını iyileştirmeyi ve sürekli durum hatasını sıfıra indirmeyi hedeflemektedir ve bu kapsamda evrimsel algoritmalar yardımıyla denetleyici tasarlamaktadır. Evrimsel algoritmalar, denetleyici parametrelerini belirlenen bir amaç fonksiyonunu göz önüne alarak optimal bir şekilde ayarlamak için yaygın olarak kullanılmaktadır. Bu çalışmada, bir OGR sisteminin denetimi için iki farklı filtreli oransal-integral-türevsel (PID-F) denetleyici tasarlanmıştır. Denetleyicilerin parametrelerini ayarlamak için atom arama optimizasyon (AAO) ve parçacık sürüsü optimizasyon (PSO) algoritmaları kullanılmıştır. Her bir denetleyici için OGR sisteminin geçici yanıt analizi, frekans analizi, dayanıklılık analizi Matlab/Simulink programında incelenmiş ve performans karşılaştırması yapılmıştır. Elde edilen sonuçlara göre, AAO algoritmasının PSO algoritmasından daha iyi sonuçlar verdiği görülmüştür. Ayrıca, AAO algoritması ile tasarlanmış PID-F denetleyicinin, AAO, PSO, biyocoğrafyaya dayalı optimizasyon (BDO) ve yapay arı koloni (YAK) algoritmaları ile ayarlanmış klasik PID denetleyicilere göre geçici yanıt karakteristiklerini iyileştirdiği ve sistemin kararlılığını ve dayanıklılığını arttırdığı sonucuna varılmıştır. 

References

  • Al Gizi, A, J., (2018). A particle swarm optimization, fuzzy PID controller with generator automatic voltage regulator. Soft Computing, 23, 8839–8853.
  • Anbarasi, S., Muralidharan, S., (2016). Enhancing the Transient Performances and Stability of AVR System with BFOATuned PID Controller. Control Engineering and Applied Informatics, 18(1), 20-29.
  • Ayas, M. S., (2019). Design of an optimized fractional high-order differential feedback controller for an AVR system. Electrical Engineering, 101, 1221-1233.
  • Bingul, Z., Karahan, O., (2018). A novel performance criterion approach to optimum design of PID controller using cuckoo search algorithm for AVR system. Journal of the Franklin Institute, 355(13) 5534-5559.
  • Blondin, M. J., Sanchis, J., Sicard, P., Herrero, J. M., (2018). New optimal controller tuning method for an AVR system using asimplified Ant Colony Optimization with a new constrained Nelder–Mead algorithm. Applied Soft Computing, 62, 216-229.
  • Bourouba, B., Ladaci, S., Schulte, H., (2019). Optimal Design of Fractional Order PID Controller for an AVR System using Ant Lion Optimizer. IFAC-Papersonline, 52(13), 200-205.
  • Coelho, L. S., (2009). Tuning of PID controller for an automatic Voltage regulator system using chaotic optimization approach. Chaos, Solitons and Fractals 39 (4) 1504-1514.
  • Çelik, E., Durgut, A., (2018). Performance enhancement of automatic voltage regulator by modified cost function and symbiotic organisms search algorithm. Engineering Science and Technology, an International Journal, 21, 1104-1111.
  • Gaing, ZL., (2004). A particle swarm optimization approach for optimum design of PID controller in AVR system. IEEE Transaction on Energy Conversation, 19(2), 384–391.
  • 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.
  • Guvenc U, Yigit T, Isik AH and Akkaya I (2016) Performance analysis of biogeography-based optimization for automatic voltage regulator system. Turkish Journal of Electrical Engineering and Computer Sciences 24(3): 1150–1162.
  • Hameed, N. S. S., Othman, W. A. F. W., Wahab, A. A. A., Alhady, S. S. N., (2019). Optimising pid controller using bees algorithm and firefly algorithm. ROBOTIKA, 1(1), 22-27.
  • Hekimoğlu, B., Ekinci, S., (2018, June). Grasshopper optimization algorithm for automatic voltage regulator system. 2018 5th International Conference on Electrical and Electronic Engineering (ICEEE) (pp. 152-156), Istanbul.
  • Hekimoğlu, B., (2019). Sine-cosine algorithm-based optimization for automatic voltage regulator system. Transactions of the Institute of Measurement and Control, 41(6), 1761-1771.
  • Kennedy, J., Eberhart, R., (1995). Particle Swarm Optimization, Proceedings of the IEEE International Conference on Neural Networks, 4, 1942-1948.
  • Sahib, M. A., (2015). A novel optimal PID plus second order derivative controller for AVR system. Engineering Science and Technology, 18, 194-206.
  • Sambariya, D. K., Paliwal, D., (2016). Optimal design of PIDA controller using harmony search algorithm for AVR power system. 2016 IEEE 6th International Conference on Power Systems (ICPS) (pp. 1-6). New Delhi.
  • Suribabu, A. G., Chiranjeevi, B. T., (2016). Implementation of Fractional Order PID Controller for an AVR System Using GA and ACO Optimization Techniques. IFAC-Papersonline, 49(1), 456-461.
  • Tang, Y., Li, X., Wang, Y., Li, N., Han, M. ve Liu, F., (2017). Optimal fractional order PID controller design for automatic voltage regulator system based on reference model using particle swarm optimization. Int. J. Mach. Learn. & Cyber, 8, 1595–1605.
  • Verma, S. K., Yadav, S., Nagar, S. K., (2017). Optimization of Fractional Order PID Controller Using Grey Wolf Optimizer. J Control Autom Electr Syst, 28, 314-322.
  • Zhao, W., Wang, L., Zhang, Z., (2019). Atom search optimization and its application to solve hydrogeologic parameter estimation problem. Knowledge-Based Systems, 163, 283–304.
There are 21 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Büşra Özgenç 0000-0001-5687-7248

Mustafa Şinasi Ayas 0000-0001-8113-4817

İsmail Altaş 0000-0001-9298-4091

Publication Date June 15, 2020
Published in Issue Year 2020

Cite

APA Özgenç, B., Ayas, M. Ş., & Altaş, İ. (2020). Otomatik Gerilim Regülatörü için Evrimsel Algoritma Tabanlı Filtreli PID Denetleyici Tasarımı. Karadeniz Fen Bilimleri Dergisi, 10(1), 74-90. https://doi.org/10.31466/kfbd.719953