Abstract
A hybrid Many Optimizing Liaisons Gravitational Search Algorithm (hMOL-GSA)-based fuzzy PID controller is proposed in this work for Automatic Generation Control problem. MOL is a simplified version of particle swarm optimization which ignores the particle best position consequently simplifying the algorithm. The proposed method is employed to tune the fuzzy PID parameters. The outcomes are equated with some newly proposed methods like Artificial Bee Colony (ABC)-based PID for the identical test systems to validate the supremacy of GSA and proposed hMOL-GSA techniques. Further, the design task has been carried out in a three-area test system and the outcomes are equated with newly proposed Firefly Algorithm (FA) optimized PID and Teaching Learning-Based Optimization (TLBO) tuned PIDD controller for the identical system. Better system response has been observed with proposed hMOL-GSA method. Finally, sensitivity study is being carried out and robustness of the proposed method is established.
Highlights
Automatic generation control (AGC) is a vital problem for the satisfactory operation of power systems
The Hardware-In-the Loop (HIL) setup, contains of an OPAL-RT as a Real Time Simulator (RTS), which simulates the power system models shown in Figures 1 and 11; a PC as command station in which the Matlab/Simulink-based codes are generated for execution on the OPAL-RT and a router to connect all the setup devices in the same sub-network
The advantage of the gravitational search algorithm (GSA) optimized PID is demonstrated by comparing the outcomes with Artificial Bee Colony (ABC)-based PID for the identical test system and objective function
Summary
Automatic generation control (AGC) is a vital problem for the satisfactory operation of power systems. Various novel technique/control approaches such as Firefly Algorithm (FA) optimized PI controller [11,12], Flower Pollination Algorithm (FPA) tuned PID controller [13], Grey Wolf Optimization (GWO) tuned classical controller with PI and PID structure[14], Differential Evolution (DE) tuned 2-DOF PID [15], TLBO tuned 2-DOF PID [16], ICA-based fuzzy-PI [17], hybrid FA and Pattern Search (hFA-PS) optimized PI and PID [18], A hybrid gravitational search algorithm (GSA) and PS tuned PI/PIDF controller [19], hybrid Stochastic Fractal Search and Local Unimodal Sampling (hSFS-LUS) optimized multistage PDF plus (1 + PI) [20] have been recently suggested for various types of power systems for frequency control It is evident from the literature review that, there is scope to work on AGC by investigating new control structures and optimization technique.
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