Optimizing Load Frequency Control of Micro-grid using Black Widow Optimization Algorithm

Sandeep Bhongade, Devendra Patel, Ankit Singh, R. S. Mandloi

Abstract


This paper investigates the application of advanced optimization algorithms, namely the Particle Swarm Optimization (PSO) and Black Widow Optimization Algorithm (BWOA), for tuning Proportional-Integral-Derivative (PID) controllers in load frequency control (LFC) systems. The study explores the performance of controllers under various tuning strategies, including the conventional Ziegler-Nichols method, PSO, and BWOA, focusing on power generation, power deviation, and frequency deviation. The BWOA, inspired by the hunting and survival strategies of black widow spiders, emerges as a promising optimization method, showcasing superior performance compared to traditional and contemporary approaches. The results demonstrate that BWOA-tuned controllers exhibit enhanced dynamic response, stability, and efficiency in load frequency control systems.


Keywords


Black Widow Optimization Algorithm ;Load Frequency Control; Particle Swarm Optimization; Proportional-Integral-Derivative; Ziegler–Nichols

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References


Tungadio, D.H. and Sun, Y., 2019. Load frequency controllers considering renewable energy integration in power system. Energy Reports, 5, pp.436-453.

Peddakapu, K., Mohamed, M.R., Srinivasarao, P., Arya, Y., Leung, P.K. and Kishore, D.J.K., 2022. A state-of-the-art review on modern and future developments of AGC/LFC of conventional and renewable energy-based power systems. Renewable Energy Focus.

Ranjan, M. and Shankar, R., 2022. A literature survey on load frequency control considering renewable energy integration in power system: Recent trends and future prospects. Journal of Energy Storage, 45, p.103717.

Pappachen, A. and Fathima, A.P., 2017. Critical research areas on load frequency control issues in a deregulated power system: A state-of-the-art-of-review. Renewable and Sustainable Energy Reviews, 72, pp.163-177.

Arya, Y., Dahiya, P., Çelik, E., Sharma, G., Gözde, H. and Nasiruddin, I., 2021. AGC performance amelioration in multi-area interconnected thermal and thermal-hydro-gas power systems using a novel controller. Engineering Science and Technology, an International Journal, 24(2), pp.384-396.

Çelik, E., Öztürk, N., Arya, Y. and Ocak, C., 2021. (1+ PD)-PID cascade controller design for performance betterment of load frequency control in diverse electric power systems. Neural Computing and Applications, 33(22), pp.15433-15456.

Tabak, A. and Duman, S., 2022. Levy flight and fitness distance balance-based coyote optimization algorithm for effective automatic generation control of PV-based multi-area power systems. Arabian Journal for Science and Engineering, 47(11), pp.14757-14788.

Hote, Y.V. and Jain, S., 2018. PID controller design for load frequency control: Past, present and future challenges. IFAC-PapersOnLine, 51(4), pp.604-609.

Duman, S. and YÖRÜKEREN, N., 2012. Automatic generation control of the two area non-reheat thermal power system using gravitational search algorithm. system, 1, p.2.

Çelik, E., 2020. Improved stochastic fractal search algorithm and modified cost function for automatic generation control of interconnected electric power systems. Engineering Applications of Artificial Intelligence, 88, p.103407.

Barisal, A.K., 2015. Comparative performance analysis of teaching learning based optimization for automatic load frequency control of multi-source power systems. International Journal of Electrical Power & Energy Systems, 66, pp.67-77.

Shabani, H., Vahidi, B. and Ebrahimpour, M., 2013. A robust PID controller based on imperialist competitive algorithm for load-frequency control of power systems. ISA transactions, 52(1), pp.88-95.

Ali, E.S. and Abd-Elazim, S.M., 2013. BFOA based design of PID controller for two area load frequency control with nonlinearities. International Journal of Electrical Power & Energy Systems, 51, pp.224-231.

Panwar, A., Sharma, G. and Bansal, R.C., 2019. Optimal AGC design for a hybrid power system using hybrid bacteria foraging optimization algorithm. Electric Power Components and Systems, 47(11-12), pp.955-965.

Kaliannan, J., Baskaran, A. and Dey, N., 2015. Automatic generation control of thermal-thermal-hydro power systems with PID controller using ant colony optimization. International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), 6(2), pp.18-34.

Das, D.C., Roy, A.K. and Sinha, N., 2012. GA based frequency controller for solar thermal–diesel–wind hybrid energy generation/energy storage system. International Journal of Electrical Power & Energy Systems, 43(1), pp.262-279.

Jagatheesan, K., Anand, B., Samanta, S., Dey, N., Santhi, V., Ashour, A.S. and Balas, V.E., 2017. Application of flower pollination algorithm in load frequency control of multi-area interconnected power system with nonlinearity. Neural Computing and applications, 28, pp.475-488.

Simhadri, K.S. and Mohanty, B., 2020. Performance analysis of dual?mode PI controller using quasi?oppositional whale optimization algorithm for load frequency control. International Transactions on Electrical Energy Systems, 30(1), p.e12159.

Saikia, L.C. and Sinha, N., 2016. 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, pp.52-63.

Kumarakrishnan, V., Vijayakumar, G., Boopathi, D., Jagatheesan, K., Saravanan, S. and Anand, B., 2020. Optimized PSO technique based PID controller for load frequency control of single area power system. Solid State Technology, 63(5), pp.7979-7990.

Sadeghi, B., Shafaghatian, N., Alayi, R., El Haj Assad, M., Zishan, F. and Hosseinzadeh, H., 2022. Optimization of synchronized frequency and voltage control for a distributed generation system using the Black Widow Optimization algorithm. Clean Energy, 6(1), pp.105-118.

Hayyolalam, V. and Kazem, A.A.P., 2020. Black widow optimization algorithm: a novel meta-heuristic approach for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 87, p.103249.




DOI (PDF): https://doi.org/10.20508/ijsmartgrid.v8i1.319.g342

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