A Statistical Feature-Based Anomaly Detection Method for PFC Using Canonical Correlation Analysis

Power factor correction (PFC) converters are widely used in power systems. The anomalous state of a PFC converter can develop into the complete or partial loss of the electric system. In some industrial applications, such as wind power generation and photovoltaic power generation, there is a fluctuation in the input voltage of the PFC converter, which brings great obstacles in anomaly detection. To effectively recognize an anomalous state, especially when the input voltage is unstable, a statistical feature-based anomaly detection method using a canonical correlation analysis (CCA) is proposed. First, statistical features are used to enhance the difference between the normal state and the anomalous state. Then, the proposed anomaly detection method focuses on the correlation between the input voltage and the output voltage, so even if there is a fluctuation in the input voltage, the anomaly states can be detected precisely. The experimental results show the effectiveness of the method.