Hybrid Approach Based on GA and PSO for Parameter Estimation of a Full Power Quality Disturbance Parameterized Model

Power quality (PQ) and PQ disturbances (PQD) are relevant for the industry due to the implied costs in most industrial processes. Besides, it is necessary to maintain the quality standards of the electrical grid to avoid damages in the equipment that is connected to the grid. Due to the nature and characteristics of the PQD present in the voltage and current signals, several studies have focused on detecting and classifying particular disturbances, or simple combinations between two or three of them, without presenting a methodology that describes all of them automatically. Hence, this paper proposes a hybrid approach integrating genetic algorithms (GA) and particle swarm optimization (PSO) with other techniques that make use of their individual capabilities to automatically find a wide range of PQD present in a voltage or current signal, regardless of their nature. To achieve this hybrid approach parameterization, a full PQD model is adopted to automate the search of every one of their parameters. The proposed approach is validated through synthetic signals, real data from the IEEE data base, and through data readings from a real process. A comparison using other recent heuristic techniques is made to show the robustness of the proposed hybrid approach.