Variable structure model predictive controller based gain scheduling for frequency regulation in renewable based power system. (24th January 2022)

• Record Type: Journal Article
• Authors: Sibtain, Daud; Gulzar, Muhammad Majid; Murtaza, Ali F.; Murawwat, Sadia; Iqbal, Muhammad; Rasool, Imran; Hayat, Aamir; Arif, Arslan
• Is Part Of: International journal of numerical modelling. Volume 35:Number 4(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-01-24
• Subjects: firefly algorithm -- gain scheduling -- genetic algorithm -- load frequency control -- model predictive controller -- particle swarm optimization -- variable structure system; Electric networks -- Mathematical models -- Periodicals; Electronics -- Mathematical models -- Periodicals; 621.3011
• DOI: 10.1002/jnm.2989
• Languages: English
• ISSNs: 0894-3370

Abstract

Abstract: This paper presents a load frequency control (LFC) design using the variable structure model predictive controller (VSMPC) for gain scheduling (GS) of PI controller in PV connected thermal for multi‐area system. Due to the increasing impact of renewable energy sources into the grid system, the system frequency deteriorates. The proposed PV connected thermal system is evaluated under varying load conditions under increased penetration of the PV source. The combination of renewable energy into the thermal power system aggravates the frequency and it is indispensable to mitigate such a problem by introducing an optimal controller. Perturbation in load greatly affects the system frequency and this problem is addressed by using VSMPC based gain scheduling knowing the effect of proportional and integral in transient and steady‐state conditions. The oscillations in frequency are controlled by a proposed controller and the impact of frequency under penetration of PV is monitored. Further verification of the proposed technique is accomplished through communication delay in the governor and turbine. The Hardware in the loop (HIL) which validates the accuracy and real time performance of the controller. Finally, the validation of the proposed controller is compared with MPC to tune PI controller and renowned evolutionary tuned techniques like particle swarm optimization (PSO), genetic algorithm (GA) and firefly algorithm (FA) to optimize PI controller. The VSMPC with GS of PI controller shows promising results.