A new robust fuzzy-PID controller design using gravitational search algorithm. (19th March 2019)

• Record Type: Journal Article
• Authors: Kouba, Nour EL Yakine; Menaa, Mohamed; Hasni, Mourad; Boudour, Mohamed
• Is Part Of: International journal of computer aided engineering and technology. Volume 11:Number 3(2019)
• Page Start: 331
• Page End: 360
• Publication Date: 2019-03-19
• Subjects: ancillary frequency control -- load frequency control -- LFC -- PID controller -- fuzzy logic controller -- FLC -- optimal control -- gravitational search algorithm -- GSA; Computer-aided engineering -- Periodicals; 620.00285
• Languages: English
• ISSNs: 1757-2657

Abstract

This paper proposes the design of a novel robust load frequency control (LFC) strategy-based optimised fuzzy-PID controller employing gravitational search algorithm (GSA). The suggested GSA algorithm was applied to optimise the input scaling factors of the fuzzy logic and the PID controller gains. To show the potential of the proposed control methodology, a multi-sources two-area interconnected power system was investigated for the simulation. The considered test system comprises various power generating units from hydro, thermal and nuclear sources in area-1, and power generation from hydro, nuclear and diesel sources in area-2. Initially, the simulation was carried considering a centralised controller for both areas to cope with load changes, and then was extended with decentralised controller. Further, sensitivity analysis was performed to demonstrate the ability of the proposed approach in face of wide changes in system parameters and position of load changes. The frequency deviations and the tie-line power flow change were presented, and the superiority of the proposed control strategy was demonstrated by comparing the results with individual gravitational search algorithm (GSA), fuzzy logic controller (FLC) and with some reported techniques in the literature such as Ziegler-Nichols, genetics algorithm (GA), bacterial foraging optimisation algorithm (BFOA) and particle swarm optimisation (PSO).