Multiple Impact Factor Based Accuracy Analysis for Power Quality Disturbance Detection

卷:9

Article

关键词: OPTIMAL FEATURE-SELECTION;    WAVELET TRANSFORM;    FEATURE-EXTRACTION;    CLASSIFICATION;    IDENTIFICATION;    PACKET;    RECOGNITION;    ALGORITHM;    ENTROPY;    SYSTEM;   

DOI  :  10.17775/CSEEJPES.2020.01270

来源: SCIE

【 摘 要 】

Nowadays, power quality problems are affecting people's daily life and production activities. With an aim to improve disturbance detection accuracy, a novel analysis approach, based on multiple impact factors, is proposed in this paper. First, a multiple impact factors analysis is implemented in which two perspectives, i.e., the wavelet analysis and disturbance features are simultaneously considered. Five key factors, including wavelet function, wavelet decomposition level, redundant algorithm, event type and disturbance intensity, and start and end moment of disturbance, have been considered. Next, an impact factor based accuracy analysis algorithm is proposed, through which each factor's potential impact on disturbance location accuracy is investigated. Three transforms, i.e., the classic wavelet, lifting wavelet and redundant lifting wavelet are employed, and their superiority on disturbance location accuracy is investigated. Finally, simulations are conducted for verification. Through the proposed method, the wavelet based parameters can be validly selected in order to accurately detect power quality disturbance.