Bat inspired algorithm based optimal design of model predictive load frequency control. (December 2016)

• Record Type: Journal Article
• Authors: Elsisi, M.; Soliman, M.; Aboelela, M.A.S.; Mansour, W.
• Is Part Of: International journal of electrical power & energy systems. Volume 83(2016:Dec.)
• Page Start: 426
• Page End: 433
• Publication Date: 2016-12
• Subjects: Bat inspired algorithm (BIA) -- Load frequency control (LFC) -- Model predictive control (MPC) -- Time delays -- Nonlinearities; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2016.04.036
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: BAT algorithm based design of model predictive controller (MPC) is proposed for LFC. Two-area hydro-thermal system, installed with MPLFCs, is considered for this study. BIA is devoted to search for optimistic controller parameters. A candidate time-domain based objective function is considered for optimization. Comparative simulation results confirm the effectiveness of MPLFCs. Abstract: Bat inspired algorithm (BIA) has recently been explored to develop a novel algorithm for distributed optimization and control. In this paper, BIA-based design of model predictive controllers (MPCs) is proposed for load frequency control (LFC) to enhance the damping of oscillations in power systems. The proposed model predictive load frequency controllers are termed as MPLFCs. Two-area hydro-thermal system, equipped with MPLFCs, is considered to accomplish this study. The suggested power system model considers generation rate constraint (GRC) and governor dead band (GDB). Time delays imposed to the power system by governor-turbine, thermodynamic process, and communication channels are accounted for as well. BIA is utilized to search for optimal controller parameters by minimizing a candidate time-domain based objective function. The performance of the proposed controller has been compared to those of the conventional PI controller based on integral square error (ISE) technique and the PI controller optimized by genetic algorithms (GA), in order to demonstrate the superior efficiency of the BIA-based MPLFCs. Simulation results emphasis on the better performance of the proposed MPLFCs compared to conventional and GA-based PI controllers over a wide range of operating conditions and system parameters uncertainties.