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

被引：0
|
作者：
Liu, Cuiyu [1 ]
Yang, Zhiming [1 ]
Xiang, Gang [2 ]
Yu, Yang [1 ]
机构：
[1] Harbin Inst Technol, Sch Elect & Informat Engn, Harbin 150001, Peoples R China
[2] Beijing Aerosp Automat Control Inst, Dept Syst Engn, Beijing 100854, Peoples R China
来源：
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT | 2022年 / 71卷
基金：
中国国家自然科学基金;
关键词：
Feature extraction; Anomaly detection; Voltage; Correlation; Training; Testing; Capacitors; canonical correlation analysis (CCA); power factor correction (PFC) converter; statistical feature; FAULT-DETECTION; RELIABILITY; CONVERTERS; CAPACITOR; DESIGN;
D O I：
10.1109/TIM.2022.3210944
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
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.
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收藏
页数：11
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