Hybrid algorithm for directly detecting and classification of multiple power quality disturbances

Detecting potential power quality (PQ) events that may occur in the network is an important issue to provide safe electricity to customers. In this study, a new hybrid algorithm named adaptive fitness distance balance hybrid artificial hummingbird algorithm-artificial rabbit optimization (AFDB-AHAARO) algorithm is introduced for the detection and classification of PQ events. A hybrid algorithm is developed to improve the early convergence of the AHA algorithm by expanding the search to avoid local minima. The proposed method, unlike the optimization algorithms used in the literature, is directly applied to the detection and classification of PQ events. The performance of the proposed algorithm is investigated by 6000 different single and multiple PQ events in the Matlab in noisy and noiseless environments, considering the IEEE-1159 standards. The obtained results are compared with the AHA algorithm and the methods proposed in the literature, and it is shown that the proposed method outperforms in noisy environments. It also provides information about the time interval of PQ events. The validity of the proposed algorithm in real systems is demonstrated by testing it on the voltage signal with flicker disturbance obtained from the electrical network and on experimentally generated sag and swell events. © 2025 Elsevier B.V.