Invasive weed optimization-based automatic generation control for multi-area power systems. (3rd July 2019)

• Record Type: Journal Article
• Authors: Mishra, Somanath; Barisal, A. K.; Babu, B. Chitti
• Is Part Of: International journal of modelling & simulation. Volume 39:Number 3(2019)
• Page Start: 190
• Page End: 202
• Publication Date: 2019-07-03
• Subjects: Automatic generation control (AGC) -- Multi-area multi-source power system -- HVDC link -- Invasive Weed Optimization (IWO) algorithm -- sensitivity analysis -- Generation Rate Constraints (GRC); Mathematical models -- Periodicals; Simulation methods -- Periodicals; Mathematical models; Simulation methods; Periodicals; 003.3
• DOI: 10.1080/02286203.2018.1554403
• Languages: English
• ISSNs: 0228-6203

Abstract

ABSTRACT: In this paper, Invasive weed optimization (IWO) algorithm is proposed for testing automatic generation control (AGC) of multi-area power systems. Initially, a two-area multi-source power system interconnected by a DC link is considered and IWO is employed to optimize the proportional integral derivative (PID) controller for AGC employing a modified objective function. The superiority of the proposed approach is shown by comparing the results with recently published optimal algorithms for the similar system under study. The comparison is made using various performance criteria such as integral of time multiplied absolute error (ITAE), minimum damping ratio (MDR) of dominant eigenvalues, settling times and maximum overshoots. Furthermore, sensitivity analysis is performed to show the robustness of the proposed approach by varying the system parameters, operating load conditions from their nominal values as well as size and locations of disturbance. The proposed approach is also extended to three area system considering both thermal and hydro units with generation rate constraints (GRC) and the superiority of the proposed approach is shown by comparing the results of Adaptive Neuro Fuzzy Inference System (ANFIS), hybrid Bacteria Foraging Optimization Algorithm with Particle Swarm Optimization (hBFOA–PSO) approaches for the identical interconnected power system.