Intelligent Fault Diagnosis of HVCB with Feature Space Optimization-Based Random Forest

Sensors

Yuhao Wang¹  Mingxuan Chen¹  Jianwen Wu¹  Bowen Jia¹  Yuan Jiang¹  Suliang Ma¹ 

[1] School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China;

关键词: high-voltage circuit breakers;    mechanical fault diagnosis;    wavelet packet decomposition;    random forest algorithm;    ensemble learning;    feature space optimization;   

DOI  :  10.3390/s18041221

来源: DOAJ

【 摘 要 】

Mechanical faults of high-voltage circuit breakers (HVCBs) always happen over long-term operation, so extracting the fault features and identifying the fault type have become a key issue for ensuring the security and reliability of power supply. Based on wavelet packet decomposition technology and random forest algorithm, an effective identification system was developed in this paper. First, compared with the incomplete description of Shannon entropy, the wavelet packet time-frequency energy rate (WTFER) was adopted as the input vector for the classifier model in the feature selection procedure. Then, a random forest classifier was used to diagnose the HVCB fault, assess the importance of the feature variable and optimize the feature space. Finally, the approach was verified based on actual HVCB vibration signals by considering six typical fault classes. The comparative experiment results show that the classification accuracy of the proposed method with the origin feature space reached 93.33% and reached up to 95.56% with optimized input feature vector of classifier. This indicates that feature optimization procedure is successful, and the proposed diagnosis algorithm has higher efficiency and robustness than traditional methods.

【 授权许可】

Unknown