Disturbances Classification Based on a Model Order Selection Method for Power Quality Monitoring

In this paper, a new technique for power quality disturbance classification is proposed. It focuses on voltage sags and swells that are first preclassified into four classes that depend on the number of nonzero symmetrical components and can contain different types of sag and swell. Using the estimated symmetrical component values, we can afterward classify the corresponding sag or swell signature. In this study, we show that the preclassification can be reformulated as a pure model order selection problem. To solve this problem, we propose two preclassifiers based on Information Theoretical Criteria. The former yields the highest statistical performances, whereas the latter has a lower computation complexity. The performances of the proposed classification algorithms are evaluated using Monte Carlo simulations on synthetic signals and using real power system data obtained from the DOE/EPRI National Database of Power System Events. The achieved simulations and experimental results clearly illustrate the effectiveness of the proposed algorithms for voltage sag and swell classification.