Ma Suliang, Chen Mingxuan, Wu Jianwen, Wang Yuhao, Jia Bowen, Jiang Yuan
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China.
Sensors (Basel). 2018 Apr 16;18(4):1221. doi: 10.3390/s18041221.
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.
高压断路器（HVCB）在长期运行过程中总会出现机械故障，因此提取故障特征并识别故障类型已成为确保供电安全性和可靠性的关键问题。本文基于小波包分解技术和随机森林算法，开发了一种有效的识别系统。首先，与香农熵的不完整描述相比，在特征选择过程中采用小波包时频能量率（WTFER）作为分类器模型的输入向量。然后，使用随机森林分类器诊断HVCB故障，评估特征变量的重要性并优化特征空间。最后，通过考虑六种典型故障类别，基于实际HVCB振动信号对该方法进行了验证。对比实验结果表明，所提方法在原始特征空间下的分类准确率达到93.33%，在分类器输入特征向量优化后高达95.56%。这表明特征优化过程是成功的，且所提诊断算法比传统方法具有更高的效率和鲁棒性。
