Particle swarm optimisation-based parameters optimisation of PID controller for load frequency control of multi-area reheat thermal power systems. (2017)

• Record Type: Journal Article
• Authors: Jagatheesan, K.; Anand, B.; Samanta, Sourav; Dey, Nilanjan; Ashour, Amira S.; Balas, Valentina E.
• Is Part Of: International journal of advanced intelligence paradigms. Volume 9:Number 5/6(2017)
• Page Start: 464
• Page End: 489
• Publication Date: 2017
• Subjects: load frequency control -- LFC -- proportional-integral-derivative -- PID -- evolutionary computational intelligence -- optimisation -- hill climbing -- HC -- algorithm -- genetic algorithm -- GA -- particle swarm optimisation -- PSO; Artificial intelligence -- Periodicals; Machine theory -- Periodicals; Fuzzy logic -- Periodicals; 006.305
• Languages: English
• ISSNs: 1755-0386

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 (KP ), integral gain (KI ) and derivative gain (KD ) 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.