Three-Layer Bayesian Network for Classification of Complex Power Quality Disturbances

In this paper, a new classification approach for detection and classification of complex power quality disturbances (PQDs) using a three-level multiply connected Bayesian network is proposed. First, the model consisting of features evidence layer, disturbances state layer, and circumstance evidence layer is established, which represent the features extracted from the sample signal, the state of each single label of PQDs and the circumstance factors that may affect the PQDs, respectively. Second, the parameters of three-level multiply connected Bayesian network (TLBN) are studied from statistical data and Monte Carlo simulations. Finally, the classification is determined by computing the posterior marginal probabilities of each event given observed evidences. The new method not only utilizes the existing features extracting methods, but also takes the historical data, and other surrounding factors into account. Simulation results and real-life PQ signal tests show that the performances of TLBN classification of complex disturbances are better than the other approaches in existing literatures.