Otomatik gerilim regülatör sistemi için karşıt tabanlı atom arama optimizasyon algoritması

Year 2020, Volume: 35 Issue: 3, 1141 - 1158, 07.04.2020

Serdar Ekinci , Ayşen Demirören Hatice Lale Zeynelgil Baran Hekimoğlu

https://doi.org/10.17341/gazimmfd.598576

Cited By: 11

Abstract

Bu makale, arama uzayı
keşfini geliştirmek için karşıt tabanlı öğrenmeyi (OBL) kullanan atom arama
optimizasyon (ASO) algoritmasının değiştirilmiş bir versiyonunu sunmaktadır.
OBL, sezgisel-üstü algoritmaların performansını artırmak için yaygın olarak
kullanılan bir makine öğrenme stratejisidir. Yeni bir tasarım metodu olarak
sunulan karşıt tabanlı ASO (OBASO) algoritması, otomatik gerilim regülatörü
(AVR) sistemindeki oransal-integral-türevsel artı ikinci dereceden türevsel
(PIDD²) kontrolör parametrelerinin optimum değerlerinin bulunmasında
ilk kez önerilmiştir. Tasarım probleminde, zaman ağırlıklı karesel hatanın
integrali (ITSE) ve aşımı birlikte içeren yeni bir amaç fonksiyonu, önerilen
OBASO algoritması ile optimize edilerek PIDD² kontrolör
parametrelerinin en iyi değerleri bulundu. Önerilen OBASO ayarlı PIDD²
(OBASO-PIDD²) kontrolörün performansı, klasik ASO ayarlı PIDD²
(ASO-PIDD²) kontrolörün yanı sıra modern sezgisel-üstü
algoritmalarla ayarlanan PID, kesir dereceli PID (FOPID) ve PIDD²
kontrolörleriyle karşılaştırılmıştır. Önerilen yaklaşımın kararlılığını
değerlendirmek için karşılaştırmalı geçici hal ve frekans cevabı analizleri
gerçekleştirilmiştir. Ayrıca, AVR parametrelerindeki muhtemel değişimler göz
önüne alınarak önerilen yaklaşımın gürbüzlüğü test edilmiştir. Kapsamlı
simülasyon sonuçları ve mevcut diğer kontrolörler ile yapılan karşılaştırmalar,
yeni bir amaç fonksiyonuna sahip önerilen OBASO-PIDD² kontrolörün
üstün bir kontrol performansına sahip olduğunu ve model belirsizliklerine göre
sistem gürbüzlüğünü oldukça artırabildiğini göstermektedir.

Keywords

Otomatik gerilim regülatörü, karşıt tabanlı öğrenme, atom arama optimizasyon algoritması, parametre ayarı

References

• [1] Hekimoğlu B., Sine-cosine algorithm-based optimization for automatic voltage regulator system, Transactions of the Institute of Measurement and Control, 41 (6), 1761-1771, 2019.
• [2] Devaraj D., Selvabala B., Real-coded genetic algorithm and fuzzy logic approach for real-time tuning of proportional-integral-derivative controller in automatic voltage regulator system, IET Generation Transmission and Distribution, 3 (7), 641-649, 2009.
• [3] Gaing Z.-L., A particle swarm optimization approach for optimum design of PID controller in AVR system, IEEE Transactions on Energy Conversion, 19 (2), 384-391, 2004.
• [4] Gozde H., Taplamacioglu M.C., Comparative performance analysis of artificial bee colony algorithm for automatic voltage regulator (AVR) system, Journal of the Franklin Institute, 348 (8), 1927-1946, 2011.
• [5] Tang Y., Cui M., Hua C., Li L., Yang Y., Optimum design of fractional order PIλDμ controller for AVR system using chaotic ant swarm, Expert Systems with Applications, 39 (8), 6887-6896, 2012.
• [6] Mohanty P.K., Sahu B.K., Panda S., 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, 2014.
• [7] Sahib M.A., A novel optimal PID plus second order derivative controller for AVR system, Engineering Science and Technology, an International Journal, 18 (2), 194-206, 2015.
• [8] Zeng G.-Q., Chen J., Dai Y.-X., Li L.-M., Zheng C.-W., Chen M.-R., Design of fractional order PID controller for automatic regulator voltage system based on multi-objective extremal optimization, Neurocomputing, 160, 173-184, 2015.
• [9] Güvenç U., Yiğit T., Işık A.H., Akkaya İ., Performance analysis of biogeography-based optimization for automatic voltage regulator system, Turkish Journal of Electrical Engineering and Computer Sciences, 24, 1150-1162, 2016.
• [10] Chatterjee S., Mukherjee V., PID controller for automatic voltage regulator using teaching learning based optimization technique, International Journal of Electric Power and Energy Systems, 77, 418-429, 2016.
• [11] Duman S., Yörükeren N., Altaş İ.H., Gravitational search algorithm for determining controller parameters in an automatic voltage regulator system, Turkish Journal of Electrical Engineering & Computer Sciences, 24, 2387-2400, 2016.
• [12] Sambariya D.K., Paliwal D., Optimal design of PIDA controller using harmony search algorithm for AVR power system, 2016 IEEE 6th International Conference on Power Systems (ICPS), New Delhi, India, 1-6, 4-6 March, 2016.
• [13] Lahcene R., Abdeldjalil S., Aissa K., Optimal tuning of fractional order PID controller for AVR system using simulated annealing optimization algorithm, 2017 5th International Conference on Electrical Engineering-Boumerdes (ICEE-B), Boumerdes, Algeria, 1-6, 29-31 October, 2017.
• [14] Pradhan R., Majhi S.K., Pati B.B., Design of PID controller for automatic voltage regulator system using ant lion optimizer, World Journal of Engineering, 15 (3), 373-387, 2018.
• [15] Bingul Z., Karahan O., 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, 2018.
• [16] Sikander A., Thakur P., Bansal R.C., Rajasekar S., A novel technique to design cuckoo search based FOPID controller for AVR in power systems, Computers & Electrical Engineering, 70, 261-274, 2018.
• [17] Ekinci S., Hekimoğlu B., Kaya S., Tuning of PID controller for AVR system using salp swarm algorithm, 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), Malatya, Turkey, 424-429, 28-30 September, 2018.
• [18] Çelik E., Incorporation of stochastic fractal search algorithm into efficient design of PID controller for an automatic voltage regulator system, Neural Computing and Applications, 30 (6), 1991-2002, 2018.
• [19] Hekimoğlu B., Ekinci S., Grasshopper optimization algorithm for automatic voltage regulator system, 2018 5th International Conference on Electrical and Electronic Engineering (ICEEE), Istanbul, Turkey, 152-156, 3-5 May, 2018.
• [20] Celik E., Durgut R., Performance enhancement of automatic voltage regulator by modified cost function and symbiotic organisms search algorithm, Engineering Science and Technology, an International Journal, 21 (5), 1104-1111, 2018.
• [21] Gong C., Jaya algorithm-optimized PID controller for AVR system, Advances in Intelligent, Interactive Systems and Applications, Proceedings of the 3rd International Conference on Intelligent, Interactive Systems and Applications (IISA2018), Vol. 885, Eds: Xhafa F., Patnaik S., Tavana M., Springer Nature, HongKong, China, 382-393, 2018.
• [22] Zhou Y., Zhang J., Yang X., Ling Y., Optimization of PID controller based on water wave optimization for an automatic voltage regulator system, Information Technology and Control, 48 (1), 160-171, 2019.
• [23] Ekinci S., Hekimoğlu B., Improved kidney-inspired algorithm approach for tuning of PID controller in AVR system, IEEE Access, 7, 39935-39947, 2019.
• [24] Ekinci S., Hekimoğlu B., Multi-machine power system stabilizer design via HPA algorithm, Journal of the Faculty of Engineering and Architecture of Gazi University, 32 (4), 1271-1285, 2017.
• [25] Ekinci S., Optimal design of power system stabilizer using sine cosine algorithm, Journal of the Faculty of Engineering and Architecture of Gazi University, 34 (3), 1329-1350, 2019.
• [26] Tizhoosh H.R., Opposition-based learning: A new scheme for machine intelligence, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06), Vienna, Austria, 695-701, 28-30 November, 2005.
• [27] Elaziz M.A., Oliva D., Xiong S., An improved opposition-based sine cosine algorithm for global optimization, Expert Systems with Applications, 90, 484-500, 2017.
• [28] Elaziz M.A., Oliva D., Parameter estimation of solar cells diode models by an improved opposition-based whale optimization algorithm, Energy Conversion and Management, 171, 1843-1859, 2018.
• [29] Ibrahim R.A., Elaziz M.A., Oliva D., Cuevas E., Lu S., An opposition-based social spider optimization for feature selection, Soft Computing, 1-21, 2019.
• [30] Pradhan M., Roy P.K., Pal T., Oppositional based grey wolf optimization algorithm for economic dispatch problem of power system, Ain Shams Engineering Journal, 9 (4), 2015-2025, 2018.
• [31] Bao X., Jia H., Lang C., Dragonfly algorithm with opposition-based learning for multilevel thresholding Color Image Segmentation, Symmetry, 11 (5), 716, 2019.
• [32] Zhao W., Wang L., Zhang Z., Atom search optimization and its application to solve a hydrogeologic parameter estimation problem, Knowledge-Based Systems, 163, 283-304, 2019.
• [33] Zhao W., Wang L., Zhang Z., A novel atom search optimization for dispersion coefficient estimation in groundwater, Future Generation Computer Systems, 91, 601-610, 2019.
• [34] Hekimoğlu B., Optimal tuning of fractional order PID controller for DC motor speed control via chaotic atom search optimization algorithm, IEEE Access, 7, 38100-38114, 2019.
• [35] Mirjalili S., SCA: A sine cosine algorithm for solving optimization problems, Knowledge-Based Systems, 96, 120-133, 2016.
• [36] Saadat H., Power System Analysis, McGraw-Hill, New York, NY, USA, 1999.
• [37] Raju M., Saikia L.C., Sinha N., Automatic generation control of a multi-area system using ant lion optimizer algorithm based PID plus second order derivative controller, International Journal of Electrical Power & Energy Systems, 80, 52-63, 2016.