The Controller Parameter Optimization for Nonlinear Systems Using Particle Swarm Optimization and Genetic Algorithm

Aqib Ali, Samreen Naeem


The difficulty of creating controllers for non-linear power systems is addressed in this paper. The feed angle and terminal voltage should be monitored during a power outage. The "direct feedback linearization" approach is employed to construct the non-linear controller. The simulation was run with various beginning power angle values, and the results for terminal voltage and power angle were obtained. Particle swarm optimization and genetic algorithm are regarded as optimization strategies for improving controller settings because of the restrictions of the fuzzy logic controller. A genetic algorithm can be used to optimize the fuzzy logic controller's output settings. The optimization strategy can smooth the form of the output function. The findings on power angle and terminal are presented in this article. The results of non-linear power systems: terminal voltage and power angle are presented in this article. The focus is on developing a controller that will be used for power system faults and transient circumstances.


Controller Parameter, Non-Linear Power Systems, Voltage.

Full Text:



. A. Shamshirgaran, H. Javidi, and D. Simon. Evolutionary algorithms for multi-objective optimization of drone controller parameters. In 2021 IEEE Conference on Control Technology and Applications (CCTA), IEEE, pp. 1049-1055, (2021).

. A. Rodríguez-Molina, E. Mezura-Montes, M. G. Villarreal-Cervantes, and M. Aldape-Pérez. Multi-objective meta-heuristic optimization in intelligent control: A survey on the controller tuning problem. Applied Soft Computing, 93, pp. 106342, 2020.

. J. Dagle. Transmission Innovation Symposium, (2021).

. F. J. Vivas, F. Segura, J. M. Andújar, and J. J. Caparrós. A suitable state-space model for renewable source-based microgrids with hydrogen as backup for the design of energy management systems. Energy Conversion and Management, 219, pp. 113053, 2020.

. G. Sharma, A. Panwar, Y. Arya, and M. Kumawat. Integrating layered recurrent ANN with robust control strategy for diverse operating conditions of AGC of the power system. IET Generation, Transmission & Distribution, 14(18), pp. 3886-3895, (2020).

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

. D. Kim, S. H. Kim, T. Kim, B. B. Kang, M. Lee, W. Park, ... and S. Jo. Review of machine learning methods in soft robotics. PLoS One, 16(2), pp. e0246102, (2021).

. M. J. Gibbard. ‘Robust design of fixed-parameter power system stabilisers over a wide range of operating conditions’, IEEE Trans. Power Syst., 6, (2), pp. 794-800, (1991).

. R. A. Jabr B. C. Pal, N. Martins. ‘A sequential conic programming approach for the coordinated and robust Design of power system stabilizers’, IEEE Trans. Power Syst., 25, (3), pp. 1627-1637, (2010).

. R. A. Jabr B. C. Pal, N. Martins, J. C. R. Ferraz. ‘Robust and coordinated tuning of power system stabiliser gains using sequential linear programming’, IET Gener. Transm. Distrib, 4, (8), pp. 893–904, (2010).

. R. Majumber, B. Chaudhuri, B. C. Pal, Q. C. Zhong. ‘A unified Smith predictor approach for power system damping control design using remote signals’, IEEE Trans. Cont. Syst.Tech, 13, (6), pp. 1063-1068, (2005).

. P. Kundur, M. Klein, G. J. Rogres, M. S. Zywno. ‘Application of power system stabilizers for enhancement of overall system stability’, IEEE Trans. Power Syst., 4, (2), pp. 614-626, (1989).

. F. P. Demello, C. Concordia. ‘Concepts of synchronous machine stability as affected by excitation control’, IEEE Trans. Power App. Syst., PAS-88, (4), pp. 316-329, (1969).

. A. A. Shaltout, K. A. Abu Al-Feilat. ‘Damping and synchronizing torque computation in multimachine power systems’, IEEE Trans. Power Syst., 7, (1), pp. 280–286, (1992).

. G. P. Chen, O. P. Malik, Y. H. Qin, and G. Y. Xu, ‘Optimization technique for the design of a linear optimal power system stabilizer’, IEEE Trans. Energy Convers., 7, (3), pp. 453-459, (1992).

. C. Zhu, R. Zhou, and Y. Wang. ‘A new nonlinear voltage controller for power systems’, Electr. Power Energy Syst., 19, (1), pp. 19-27, (1997).

. M. Klein, L. X. Le, G. J. Roger, S. Farrokhpay, and N. J. Balu. ‘Ho damping controller design in large power systems’, IEEE Trans. Power Syst., 10, (l), pp. 158-166, (1995).

. S. Panda, N. P. Padhy, R. N. Patel. ‘Robust coordinated design of PSS and TCSC using PSO technique for power system stability enhancement’, J. Electr. Syst., 3, (2), pp. 109-123, (2007).

. A. Sabo, N. I. Abdul Wahab, M. L. Othman, M. Z. A. Mohd Jaffar, H. Beiranvand, and H. Acikgoz. Application of a neuro-fuzzy controller for single machine infinite bus power system to damp low-frequency oscillations. Transactions of the Institute of Measurement and Control, 43(16), pp. 3633-3646, (2021).

. Y. Xu, L. Mili, M. Korkali, and X. Chen. An adaptive Bayesian parameter estimation of a synchronous generator under gross errors. IEEE Transactions on Industrial Informatics, 16(8), pp. 5088-5098, (2019).

. B. Dasu, M. S. Kumar, and R. S. Rao. Design of robust modified power system stabilizer for dynamic stability improvement using Particle Swarm Optimization technique. Ain Shams Engineering Journal, 10(4), pp. 769-783, (2019).

. S. Ekinci, A. Demiroren, and B. Hekimoglu. Parameter optimization of power system stabilizers via kidney-inspired algorithm. Transactions of the Institute of Measurement and Control, 41(5), pp. 1405-1417, (2019).

. M. Eslami, H. Shareef, and A. Mohamed. Application of artificial intelligent techniques in PSS design: a survey of the state-of-the-art methods. Przegląd Elektrotechniczny (Electrical Review), 87(4), pp. 188-197, (2011).

. H. Shayeghi, H. A. Shayanfar, A. Safari, and R. Aghmasheh. A robust PSSs design using PSO in a multi-machine environment. Energy Conversion and Management, 51(4), pp. 696-702, (2010).

. S. M. Mahmoudi, A. Maleki, and D. R. Ochbelagh. Optimization of a hybrid energy system with/without considering back-up system by a new technique based on fuzzy logic controller. Energy Conversion and Management, 229, pp. 113723, (2021).

. K. Lamamra, F. Batat, and F. Mokhtari. A new technique with improved control quality of nonlinear systems using an optimized fuzzy logic controller. Expert Systems with Applications, 145, pp. 113148, (2020).

. S. Katoch, S. S. Chauhan, and V. Kumar. A review on genetic algorithm: past, present, and future. Multimedia Tools and Applications, 80(5), pp. 8091-8126, (2021).

. S. M. H. Mousakazemi. Computational effort comparison of genetic algorithm and particle swarm optimization algorithms for the proportional–integral–derivative controller tuning of a pressurized water nuclear reactor. Annals of Nuclear Energy, 136, pp. 107019, (2020).

. L. Wang, and H. T. Liu. Parameter optimization of bidirectional re-entrant auxetic honeycomb metamaterial based on genetic algorithm. Composite Structures, 267, pp. 113915, (2021).

. A. P. Piotrowski, J. J. Napiorkowski, and A. E. Piotrowska. Population size in particle swarm optimization. Swarm and Evolutionary Computation, 58, pp. 100718, (2020).

. Y. Chaturvedi, and S. Kumar. Selection of stand-alone self-excited induction generator parameters to obtain maximum allowable operating range under unbalanced operations using particle swarm optimization. International Journal of System Assurance Engineering and Management, 11(3), pp. 677-689, (2020).


Creative Commons License
Journal of Applied and Emerging Sciences by BUITEMS is licensed under a Creative Commons Attribution 4.0 International License.
Based on a work at
Permissions beyond the scope of this license may be available at

Contacts | Feedback
© 2002-2014 BUITEMS