Qi Jiajin, Gao Xu, Huang Nantian
Hangzhou Power Supply Company of State Grid, Hangzhou 310009, China.
Department of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China.
Entropy (Basel). 2020 Apr 22;22(4):478. doi: 10.3390/e22040478.
The fault samples of high voltage circuit breakers are few, the vibration signals are complex, the existing research methods cannot extract the effective information in the features, and it is easy to overfit, slow training, and other problems. To improve the efficiency of feature extraction of a circuit breaker vibration signal and the accuracy of circuit breaker state recognition, a Light Gradient Boosting Machine (LightGBM) method based on time-domain feature extraction with multi-type entropy features for mechanical fault diagnosis of the high voltage circuit breaker is proposed. First, the original vibration signal of the high voltage circuit breaker is segmented in the time domain; then, 16 features including 5 kinds of entropy features are extracted directly from each part of the original signal after time-domain segmentation, and the original feature set is constructed. Second, the Split importance value of each feature is calculated, and the optimal feature subset is determined by the forward feature selection, taking the classification accuracy of LightGBM as the decision variable. After that, the LightGBM classifier is constructed based on the feature vector of the optimal feature subset, which can accurately distinguish the mechanical fault state of the high voltage circuit breaker. The experimental results show that the new method has the advantages of high efficiency of feature extraction and high accuracy of fault identification.
高压断路器的故障样本较少，振动信号复杂，现有的研究方法无法提取特征中的有效信息，且容易出现过拟合、训练速度慢等问题。为提高断路器振动信号特征提取效率和断路器状态识别准确率，提出一种基于时域特征提取和多类型熵特征的Light梯度提升机（LightGBM）方法，用于高压断路器机械故障诊断。首先，对高压断路器原始振动信号进行时域分段；然后，从时域分段后的原始信号各部分直接提取包括5种熵特征在内的16个特征，构建原始特征集。其次，计算各特征的分裂重要性值，以前向特征选择法确定最优特征子集，将LightGBM的分类准确率作为决策变量。之后，基于最优特征子集的特征向量构建LightGBM分类器，可准确区分高压断路器的机械故障状态。实验结果表明，新方法具有特征提取效率高和故障识别准确率高的优点。
