DE-Based Design of an Intelligent and Conventional Hybrid Control System with IPFC for AGC of Interconnected Power System

Solomon Feleke (), Balamurali Pydi, Raavi Satish, Degarege Anteneh, Kareem M. AboRas (), Hossam Kotb, Mohammed Alharbi and Mohamed Abuagreb
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Solomon Feleke: Department of Electrical and Computer Engineering, Debre Berhan University, Debre Berhan 445, Ethiopia
Balamurali Pydi: Department of Electrical & Electronics Engineering, Aditya Institute of Technology & Management (A), Tekkali 532201, AP, India
Raavi Satish: Department of Electrical & Electronics Engineering, Anil Neerukonda Institute of Technology and Science (A), Visakhapatnam 531162, AP, India
Degarege Anteneh: Department of Electrical and Computer Engineering, Debre Berhan University, Debre Berhan 445, Ethiopia
Kareem M. AboRas: Department of Electrical Power and Machines, Faculty of Engineering, Alexandria University, Alexandria 21544, Egypt
Hossam Kotb: Department of Electrical Power and Machines, Faculty of Engineering, Alexandria University, Alexandria 21544, Egypt
Mohammed Alharbi: Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia

Sustainability, 2023, vol. 15, issue 7, 1-23

Abstract: In this study, a fuzzy proportional integral derivative controller (FPID) was adjusted using the differential evolution (DE) method to enhance the automated generation control (AGC) of a three-zone reheat-type power system. The objective function used in this study was an integral of the time-weighted absolute error (ITAE). In the optimization, the gain control parameters of the proportional integral (PI), the integral (I), and FPID were optimized and compared to improve the limitations drawn by the controller over a few parameters. To demonstrate that FPID controllers with IPFC produce better and more accurate optimization results than integral and PI controllers optimized by DE, the interline power flow control (IPFC) of a flexible AC transmission system (FACTS) device with suitable connections and control parameter optimization was used. Also, the particle swarm optimization (PSO) PID with IPFC was compared with the proposed DEFPID + IPFC, and better results were achieved by using the DE technique. Similarly, to demonstrate the suggested technology’s strong control capacity, random load changes were applied to the system in various conditions, and it was demonstrated that the suggested control unit easily tolerated random load perturbations and returned the system to a stable functioning state.

Keywords: automatic generation control; area control error; differential evolution; fuzzy; IPFC; optimization; PSO (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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