Transient stability enhancement of interconnected power systems via coordinated tuning of wide-area damping controllers employing novel social grey wolf metaheuristic optimization

Abstract

Power system stability challenges arising from low-frequency oscillations are becoming increasingly acute due to the inefficiency of local damping controllers. This paper proposes a two-level architecture for wide area damping controllers (WADCs), optimized by a novel Social Learning Grey Wolf Optimizer (SLGWO) to increase the damping of critical inter-area modes, utilizing an objective function based on the integral of the time-weighted absolute error. The grey wolf optimizer (GWO) suffers from poor exploitation and local optimum trapping, primarily due to its reliance on the three fittest individuals for optimization. The SLGWO tackles these limitations by incorporating a social learning strategy, which facilitates the collective sharing of useful information among individuals, enabling them to enhance accuracy. Additionally, a computationally cheap elitist learning strategy propels the leading wolves to explore better regions, while a nonlinear control parameter is employed to optimize the exploration–exploitation trade-off. The performance of the SLGWO is benchmarked on 21 well-known test functions and compared against the original GWO, Bat Algorithm (BA), Whale Optimization Algorithm (WOA), Harris Hawks Optimization (HHO) algorithm,and Hippopotamus optimization(HO) algorithm. The comparison results are statistically confirmed using the Friedman test, confirming the superiority of the proposed approach. The effectiveness of the proposed WADCs is assessed through nonlinear simulations in the 10 machine 39 bus New England power system under severe disturbances, demonstrating a significant increase in the damping of inter-area oscillations.