Weighted mean of vectors optimization algorithm and its application in designing the power system stabilizer

Accurate design of the power system stabilizer (PSS) models is a crucial issue due to their significant impact on the stability of power system operation. However, identifying the parameters of a PSS model is a challenging task owing to its nonlinearity and multi-modality characteristics. Due to such characteristics, handling algorithms may be prone to stagnation in local optima. Therefore, this paper proposes a potent integrated optimization algorithm by comprising the weIghted meaN oF vectOrs (INFO) optimizer with chaotic-orthogonal based learning (COBL) and Gaussian bare-bones (GBB) strategies, named INFO-GBB, for achieving the optimal parameters of a PSS model used in a single-machine infinite-bus (SMIB) system. In the INFO-GBB, the COBL aims to enhance the searching capability to explore new regions using the orthogonal design aspect and thus improving the diversity of solutions. Also, the GBB is adopted to assist the algorithm to perform an immediate vicinity of the best solution and thus enhances the exploitation capabilities. The effectiveness and efficacy of the INFO-GBB algorithm is validated on CEC 2020 benchmark suits and the designing task of the PSS model. The achieved results by the INFO-GBB are compared with eighteen well-known algorithms. The statistical verifications along with the Friedman test have ascertained that the INFO-GBB is capable of achieving promising performances compared to the other counterparts. The results obtained based on the Friedman test illustrate that the INFO-GBB offers superior performance over the state-of-the-art algorithms as it outperforms fifteen out of eighteen algorithms by an average rank greater than 61% for benchmark problems while outperforming O-LSHADE, LSHADE, and TSA algorithms by 25%,33%, and 58%, respectively. Furthermore, the applicability of the INFO-GBB is realized through designing the PSS model used in a SMIB system. The obtained results indicate that the INFO-GBB algorithm exhibits accurate and superior performance compared to other peers as it provides the lowest value for the integral of time multiplied absolute error (ITAE) performance index which is used as an objective function. For example, the achieved results of the mean ITAE found by INFO-GBB is 1.36E−03 with improvement percentages of 24.93%, 19.78%, 13.04%, 26.64%, and 24.86%, over the LSHADE, GWO, EO, RSA, and original INFO algorithms, respectively. Therefore, the INFO-GBB can efficiently affirm its superiority and stability to deal with the function optimization task and parameters’ estimation of the PSS model.