Abstract
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.
Highlights
High voltage circuit breakers (HVCBs) are widely used in the power system and have complex mechanical operation structures
In the process of LightGBM construction, the search time was reduced by building the lightweight features of the vibration signals of high voltage circuit breakers; the multi-threaded parallel histogram acceleration was adopted to normalize all features in buckets, reducing the calculation amount and memory consumption, and effectively improving the training efficiency; the Leaf-wise growth strategy was adopted, and the maximum depth limit was increased, to improve the HVCBs fault diagnosis effect
In order to improve the efficiency of feature extraction and reduce the pressure of equipment cost, Time-Domain Segmentation (TDS) was directly applied to the vibration signal, and 16 kinds of time-domain features were extracted from each segmented segment for circuit breaker state identification and analysis
Summary
High voltage circuit breakers (HVCBs) are widely used in the power system and have complex mechanical operation structures. The time–frequency analysis method has many problems, such as large calculation, difficult parameter selection, easy loss of high-frequency information, and so on, which will affect the classification effect of feature extraction after signal processing. For the mechanical fault diagnosis of a high voltage circuit breaker, the direct extraction of time-domain features can better reflect the mechanical state of the operating mechanism. In the process of LightGBM construction, the search time was reduced by building the lightweight features of the vibration signals of high voltage circuit breakers; the multi-threaded parallel histogram acceleration was adopted to normalize all features in buckets, reducing the calculation amount and memory consumption, and effectively improving the training efficiency; the Leaf-wise growth strategy was adopted, and the maximum depth limit was increased, to improve the HVCBs fault diagnosis effect. The effectiveness and advanced nature of the new method were verified by comparative experiments
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