Small-Signal Stability Analysis for Microgrids Under Uncertainty Using MALANN Control Technique

Abstract

Microgrids are often considered as the solution for affordable and clean energy in the distribution sector. This article presents the small-signal stability analysis of a distributed generation unit in an autonomous microgrid operation. The purpose of the proposed strategy is to optimally improve the capacity of the power system to restore the reasonable operating condition following a small physical disturbance. The proposed strategy is the joined execution of both the modified antlion optimization algorithm (MALO) and artificial neural network (ANN), and hence it is abbreviated to MALANN. In this article, the proposed controller comprises two control loops, namely the inner current control loop and the outer power control loop. The MALO technique is incorporated to generate the dataset of possible proportional integral (PI) gain parameters. By using the accomplished dataset of MALO, the ANN is trained, and convincing estimate execution is brought out through the entire machine working condition. The proposed strategy is implemented in MATLAB/Simulink, and the results are examined with two test cases and compared with various solution techniques such as base method and ant-lion optimization. The results prove that the stability analysis is reasonably accurate, and the controller offers a reliable system's operation.