Multi-objective vector evaluated PSO with time variant coefficients for outlier identification in power systems

A generic method for multi-objective optimization for bad data identification is presented based on multi-objective vector evaluated particle swarm optimization (VEPSO) algorithm. This multi-objective VEPSO is made adaptive in nature by allowing its vital parameters to change with iterations. This adaptability helps the algorithms to explore the search space more efficiently. After the bad data are detected, eigencurves of correlating load extracted by Kohonen network are used to modify the bad data. The application of the proposed clustering algorithm to the problem of unsupervised classification of electric load data is investigated. The results of simulation show the effectiveness of the algorithm.