Extracting Log Patterns Based on Association Analysis for Power Quality Disturbance Detection

To detect anomalies according to system log is a hot topic recently. For the harmonic monitoring system of the power grid, the common practice of anomaly detection is to conduct machine learning. The learning model is trained with the historical anomaly data, and used for online detection. The premise of this method is to predefine a set of indicators as the input features of the machine learning model. However, existing methods rely mainly on business experience to extract such indicators, which limits the scope of the indicators used for data analysis, but also limits the accuracy of power quality perturbation analysis. In this paper, we propose an algorithm for power quality disturbance detection which investigates the correlation among the harmonic monitoring indicators, and extract the frequently concurrent abnormal indicators as the features to locate power quality disturbance detection. With the verification of the historical disturbance records, we prove that our algorithm can effectively detect the power quality disturbing events.