Design of single- and multi-loop self-adaptive PID controller using heuristic based recurrent neural network for ALFC of hybrid power system

• Published in: Expert systems with applications Vol. 192; p. 116402
• Main Authors: Veerasamy, Veerapandiyan, Abdul Wahab, Noor Izzri, Ramachandran, Rajeswari, Othman, Mohammad Lutfi, Hizam, Hashim, Satheesh Kumar, Jeevitha, Irudayaraj, Andrew Xavier Raj
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 15.04.2022; Elsevier BV
• Subjects: Adaptive control; Automatic load frequency control; Cascade control; Clean energy; Controllers; Differential equations; Electric vehicles; Electrical loads; Electrical plugs; Energy technology; Frequency control; Heuristic; Heuristic based hopfield neural network; Hybrid power system; Hybrid systems; Liapunov functions; Neural networks; Optimization techniques; Particle swarm optimization; Particle swarm optimization-Gravitational search algorithm; Performance indices; Proportional integral derivative; Recurrent neural networks; Search algorithms; Hybrid power system; Particle swarm optimization-Gravitational search algorithm; Automatic load frequency control; Heuristic based hopfield neural network
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2021.116402

•Design of a self-adaptive PID controller for load frequency control.•PSO-GSA based recurrent Hopfield network for tuning the PID controller parameters.•Effectiveness of proposed controller is studied in single- and multi-loop for ALFC.•Self-adaptiveness of proposed controller is validated for system uncertainties. This paper presents a novel heuristic based recurrent Hopfield neural network (HNN) designed self-adaptive proportional-integral-derivative (PID) controller for automatic load frequency control of interconnected hybrid power system (HPS). The control problem is conceptualized as an optimization problem and solved using a heuristic optimization technique with the aim of minimizing the Lyapunov function. Initially, the energy function is formulated and the differential equations governing the dynamics of HNN are derived. Then, these dynamics are solved using hybrid particle swarm optimization-gravitational search algorithm (PSO-GSA) to obtain the initial solution. The effectiveness of the controller is tested for two-area system considering the system non-linearities and integration of plug-in-electric vehicle (PEV). Further, to improve the speed of response of the system, the cascade control scheme is proposed using the presented approach of heuristic based HNN (h-HNN). The efficacy of the method is examined in single- and multi-loop PID control of three-area HPS. The performance of propounded control schemes is compared with PSO-GSA and generalized HNN based PID controller. The results obtained show that the response of proposed controller is superior in terms of transient and steady state performance indices measured. In addition, the control effort of suggested cascade controller is much reduced compared with other controllers presented. Furthermore, the self-adaptive property of the controller is analyzed for random change in load demand and their corresponding change in gain parameters are recorded. This reveals that the proposed controller is more suitable for stable operation of modern power network with green energy technologies and PEV efficiently.