Genetic k-means algorithm based RBF network for photovoltaic MPP prediction

By operating PV systems more close to the maximum power point (MPP), the output efficiency of PV panels can be improved. Traditionally, the k-means algorithm (KMA) is one of the most popular methods to classify the input patterns of the radial basis function (RBF) network. Although the KMA has an ability to cluster the training patterns rapidly, it usually converges to a local minimum and can be oversensitive to randomly initial partitions. To solve these significant problems, a hybrid skill called Genetic k-Means Algorithm (GKA) is proposed to improve the effectiveness of maximum power point track. Besides, the proposed GKA based clustering approach can overcome the problem of oversensitivity to randomly initial partitions in the existing KMA. In order to determine a suitable number of centers in RBF from the input data, the orthogonal least squares (OLS) learning algorithm was used in this paper. By precisely clustering of the training patterns, the objective to accurately and rapidly approximate the MPP of PV system can be achieved with the least squares criterion in RBF network. Also, this paper employed the actual data obtained from the practical PV systems and with which the developed MPP tracker method was proven to be effective.