Title: Particle swarm optimisation-based parameters optimisation of PID controller for load frequency control of multi-area reheat thermal power systems

Authors: K. Jagatheesan; B. Anand; Sourav Samanta; Nilanjan Dey; Amira S. Ashour; Valentina E. Balas

Addresses: Department of Electrical and Electronics Engineering, Mahendra Institute of Engineering and Technology, Namakkal, Tamil nadu, India ' Department of Electrical and Electronics Engineering, Hindusthan College of Engineering and Technology, Coimbatore, Tamilnadu, India ' Department of Computer Science and Engineering, University Institute of Technology, BU, Burdwan, Westbengal, India ' Department of Information Technology, Techno India College of Technology, West Bengal, India ' Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Egypt ' Faculty of Engineering, Aurel Vlaicu University of Arad, Romania

Abstract: The current study presents the load frequency control (LFC) of multi-area reheat thermal power system with proportional-integral-derivative (PID) controller. The interconnected control areas are provided with a single stage reheat turbine in all areas. The proportional gain (K_P), integral gain (K_I) and derivative gain (K_D) values of the PID controller are simultaneously optimised using recent and powerful evolutionary computational intelligence technique, namely the particle swarm optimisation (PSO) algorithm. The superiority of the PSO-based PID controller has been proved by comparing its performance to recent modern optimisation techniques such as hill climbing (HC) algorithm and genetic algorithm (GA) tuned controllers for the same multi-area thermal power system. For the analysis, the time domain specification and 1% step load perturbation (1% SLP) are considered in thermal area 1. The simulation result showed that the proposed PSO-based PID controller provides superior dynamic response over other optimisation technique (HC and GA)-based PID controller.

Keywords: load frequency control; LFC; proportional-integral-derivative; PID; evolutionary computational intelligence; optimisation; hill climbing; HC; algorithm; genetic algorithm; GA; particle swarm optimisation; PSO.

DOI: 10.1504/IJAIP.2017.088143

International Journal of Advanced Intelligence Paradigms, 2017 Vol.9 No.5/6, pp.464 - 489

Received: 07 Sep 2015
Accepted: 20 Sep 2015
Published online: 27 Nov 2017 *

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