Local Search-based Non-dominated Sorting Genetic Algorithm for Optimal Design of Multimachine Power System Stabilizers
Received: 15 April 2021 | Revised: 30 April 2021 | Accepted: 4 May 2021 | Online: 12 June 2021
Corresponding author: T. Guesmi
Abstract
This study presents a metaheuristic method for the optimum design of multimachine Power System Stabilizers (PSSs). In the proposed method, referred to as Local Search-based Non-dominated Sorting Genetic Algorithm (LSNSGA), a local search mechanism is incorporated at the end of the second version of the non-dominated sorting genetic algorithm in order to improve its convergence rate and avoid the convergence to local optima. The parameters of PSSs are tuned using LSNSGA over a wide range of operating conditions, in order to provide the best damping of critical electromechanical oscillations. Eigenvalue-based objective functions are employed in the PSS design process. Simulation results based on eigenvalue analysis and nonlinear time-domain simulation proved that the proposed controller provided competitive results compared to other metaheuristic techniques.
Keywords:
power system stabilizer, non-dominated sorting genetic algorithm, local search, eigenvalue analysis, nonlinear time domain simulationDownloads
References
A. Farah, T. Guesmi, H. Hadj Abdallah, and A. Ouali, "A novel chaotic teaching–learning-based optimization algorithm for multi-machine power system stabilizers design problem," International Journal of Electrical Power & Energy Systems, vol. 77, pp. 197–209, May 2016. DOI: https://doi.org/10.1016/j.ijepes.2015.11.050
K. Tang and G. K. Venayagamoorthy, "Damping inter-area oscillations using virtual generator based power system stabilizer," Electric Power Systems Research, vol. 129, pp. 126–141, Dec. 2015. DOI: https://doi.org/10.1016/j.epsr.2015.08.004
Y. Welhazi, T. Guesmi, and H. H. Abdallah, "Coordinated Tuning of SVC and PSSs in Power System using Teaching Learning Based Algorithm," in 2019 10th International Renewable Energy Congress (IREC), Sousse, Tunisia, Mar. 2019, pp. 1–6. DOI: https://doi.org/10.1109/IREC.2019.8754644
M. A. Abido, "Robust design of multimachine power system stabilizers using simulated annealing," IEEE Transactions on Energy Conversion, vol. 15, no. 3, pp. 297–304, Sep. 2000. DOI: https://doi.org/10.1109/60.875496
E. Nechadi, "Adaptive Fuzzy Type-2 Synergetic Control Based on Bat Optimization for Multi-Machine Power System Stabilizers," Engineering, Technology & Applied Science Research, vol. 9, no. 5, pp. 4673–4678, Oct. 2019. DOI: https://doi.org/10.48084/etasr.2970
T. Guesmi and B. M. Alshammari, "An improved artificial bee colony algorithm for robust design of power system stabilizers," Engineering Computations, vol. 34, no. 7, pp. 2131–2153, Jan. 2017. DOI: https://doi.org/10.1108/EC-12-2016-0459
D. K. Sambariya, "Power System Stabilizer Design Using Compressed Rule Base of Fuzzy Logic Controller," Journal of Electrical and Electronic Engineering, vol. 3, no. 3, pp. 52–64, Jul. 2015. DOI: https://doi.org/10.11648/j.jeee.20150303.16
O. Kahouli, B. Ashammari, K. Sebaa, M. Djebali, and H. H. Abdallah, "Type-2 Fuzzy Logic Controller Based PSS for Large Scale Power Systems Stability," Engineering, Technology & Applied Science Research, vol. 8, no. 5, pp. 3380–3386, Oct. 2018. DOI: https://doi.org/10.48084/etasr.2234
A. Sabo, N. I. A. Wahab, M. L. Othman, M. Z. A. M. Jaffar, and H. Beiranvand, "Optimal design of power system stabilizer for multimachine power system using farmland fertility algorithm," International Transactions on Electrical Energy Systems, vol. 30, no. 12, 2020, Art. no. e12657. DOI: https://doi.org/10.1002/2050-7038.12657
X. Li, Z. Wang, J. Xu, and B. Chen, "Power System Stabilizer Parameters Designing Based on Genetic Simulated Annealing Algorithm," Journal of Clean Energy Technologies, vol. 4, no. 3, pp. 178–182, Jan. 2015. DOI: https://doi.org/10.7763/JOCET.2016.V4.275
S. Zhang and F. L. Luo, "An Improved Simple Adaptive Control Applied to Power System Stabilizer," IEEE Transactions on Power Electronics, vol. 24, no. 2, pp. 369–375, Feb. 2009. DOI: https://doi.org/10.1109/TPEL.2008.2007490
R. Hemmati, "Power system stabilizer design based on optimal model reference adaptive system," Ain Shams Engineering Journal, vol. 9, no. 2, pp. 311–318, Jun. 2018. DOI: https://doi.org/10.1016/j.asej.2016.03.002
D. K. Sambariya and R. Prasad, "Design of Optimal Proportional Integral Derivative Based Power System Stabilizer Using Bat Algorithm," Applied Computational Intelligence and Soft Computing, vol. 2016, Mar. 2016, Art. no. e8546108. DOI: https://doi.org/10.1155/2016/8546108
T. Guesmi, B. M. Alshammari, Y. Almalaq, A. Alateeq, and K. Alqunun, "New Coordinated Tuning of SVC and PSSs in Multimachine Power System Using Coyote Optimization Algorithm," Sustainability, vol. 13, no. 6, Jan. 2021, Art. no. 3131 DOI: https://doi.org/10.3390/su13063131
S. M. Abd-Elazim and E. S. Ali, "A hybrid Particle Swarm Optimization and Bacterial Foraging for optimal Power System Stabilizers design," International Journal of Electrical Power & Energy Systems, vol. 46, pp. 334–341, Mar. 2013. DOI: https://doi.org/10.1016/j.ijepes.2012.10.047
Y. L. Abdel-Magid and M. A. Abido, "Optimal multiobjective design of robust power system stabilizers using genetic algorithms," IEEE Transactions on Power Systems, vol. 18, no. 3, pp. 1125–1132, Aug. 2003. DOI: https://doi.org/10.1109/TPWRS.2003.814848
D. Butti, M. Siva Kumar, and R. Srinivasa Rao, "Design of robust modified power system stabilizer for dynamic stability improvement using Particle Swarm Optimization technique," Ain Shams Engineering Journal, vol. 10, no. 4, pp. 769–783, Dec. 2019. DOI: https://doi.org/10.1016/j.asej.2019.07.002
D. Butti, M. Sivakumar, and R. Srinivasarao, "Interconnected multi-machine power system stabilizer design using whale optimization algorithm," Protection and Control of Modern Power Systems, vol. 4, no. 1, Feb. 2019, Art. no. 2. DOI: https://doi.org/10.1186/s41601-019-0116-6
S. M. Abd-Elazim and E. S. Ali, "Power System Stability Enhancement via Bacteria Foraging Optimization Algorithm," Arabian Journal for Science and Engineering, vol. 38, no. 3, pp. 599–611, Mar. 2013. DOI: https://doi.org/10.1007/s13369-012-0423-y
A. Ghasemi, H. Shayeghi, and H. Alkhatib, "Robust design of multimachine power system stabilizers using fuzzy gravitational search algorithm," International Journal of Electrical Power & Energy Systems, vol. 51, pp. 190–200, Oct. 2013. DOI: https://doi.org/10.1016/j.ijepes.2013.02.022
Ι. Marouani, A. Boudjemline, T. Guesmi, and H. H. Abdallah, "A Modified Artificial Bee Colony for the Non-Smooth Dynamic Economic/Environmental Dispatch," Engineering, Technology & Applied Science Research, vol. 8, no. 5, pp. 3321–3328, Oct. 2018. DOI: https://doi.org/10.48084/etasr.2098
K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, "A fast and elitist multiobjective genetic algorithm: NSGA-II," IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182–197, Apr. 2002. DOI: https://doi.org/10.1109/4235.996017
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