Teaching learning based optimization algorithm for automatic generation control of power system using 2-DOF PID controller. (May 2016)

• Record Type: Journal Article
• Authors: Sahu, Rabindra Kumar; Panda, Sidhartha; Rout, Umesh Kumar; Sahoo, Dillip Kumar
• Is Part Of: International journal of electrical power & energy systems. Volume 77(2016:May)
• Page Start: 287
• Page End: 301
• Publication Date: 2016-05
• Subjects: Automatic Generation Control (AGC) -- Multi-source power system -- Teaching Learning Based Optimization (TLBO) -- 2-Degree Freedom of Proportional–Integral–Derivative (2-DOF PID) controller -- Sensitivity; 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.2015.11.082
• Languages: English
• ISSNs: 0142-0615

Abstract

Graphical abstract: Highlights: TLBO algorithm is proposed for AGC with 2-DOF PID controller. Results are compared with TLBO over ZN, GA, BFOA, DE and hBFOA-PSO to show its superiority. Different sources of power generation like thermal, hydro and gas are proposed. Sensitivity analysis is performed by varying the loading and system parameters. The proposed approach is investigated under randomly varying step load. Abstract: In this paper, a novel Teaching Learning Based Optimization (TLBO) algorithm with 2-Degree Freedom of Proportional–Integral–Derivative (2-DOF PID) controller is proposed for Automatic Generation Control (AGC). Initially a widely used two area thermal system is considered and the gains of the PI/PID/2-DOF PID controller are optimized employing a TLBO algorithm. The supremacy of the proposed 2-DOF PID controller has been shown by comparing the results with recently published technique such as conventional ZN, GA, BFOA, DE and hBFOA-PSO based PI controllers for the same system. Additionally, the proposed approach is further extended to multi source power system such as thermal, hydro and gas power plant. The advantage of the proposed approach is demonstrated by comparing the results with some recently published approaches. Sensitivity analysis is performed which demonstrates the ability of the proposed approach to wide changes in system parameters. Finally the proposed approach is investigated under random load variation.