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| Fault Identification of Automatic Transfer Switching Equipment Based on VMD-WPE and IGWO Optimized DBN |
| Liu Guojin1, Liu Daming1, Miao Jianhua1, Yang Yuze1, Wang Lekang1, Liu Qi2 |
1. State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin 300130 China;
2. Schneider Wingoal (Tianjin) Electric Equipment Co. Ltd Tianjin 300384 China |
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Abstract Automatic Transfer Switching Equipment (ATSE) is a device that ensures the continuous power supply of the system. Fault diagnosis of ATSE is of great significance for the continuous operation of the system. The current data of solenoid coils can be used to identify the electrical and mechanical faults of ATSE. However, the noise in the current and the network structure parameters of the intelligent fault diagnosis method are difficult to confirm. Therefore, an ATSE fault diagnosis method is proposed based on variational mode decomposition (VMD) feature extraction and optimized depth belief network (DBN). In this method, Wavelet Packet Energy (WPE) is extracted as a feature vector from the mode components decomposed by VDM, and the network structure parameters of DBN are set by an improved grey Wolf algorithm (IGWO) to complete ATSE fault identification.
Firstly, the fault current signal is decomposed by VMD, and its sample entropies are obtained for each mode component after decomposition. The decomposition number corresponding to the lowest sample entropy value is taken as the final decomposition number of VMD. Secondly, principal component analysis is used to select the WPE of each mode component after decomposition, and the formed final feature vector space is input to DBN. Meanwhile, to avoid premature convergence and local optimum problems during fault classification, IGWO is used to optimize the network structure parameters of DBN to recognize various faults of ATSE.
ATSE fault simulations are designed and carried out in this paper. After determining the decomposition frequency of VMD, modal decomposition on the coil current is performed. The superiority of VMD decomposition is demonstrated by the absence of modal aliasing in the intrinsic mode functions at different frequencies. The feature vectors are input into the optimized DBN algorithm for fault classification experiments. The experimental results are as follows: (1) The difference in classification accuracy between the optimized DBN and the training set is only 0.4%, indicating no over-fitting problem in the modified model. (2) The optimized DBN achieves a classification accuracy of 98.78% for four common faults. Compared with the non-optimized methods, the proposed method has the highest accuracy and the best stability in fault diagnosis of ATSE.
The following conclusions can be drawn: (1) After determining the decomposition time based on the sample entropy, the VMD can effectively avoid the mode overlap, thus realizing the extraction of fault features from ATSE current signals. (2) Compared with the shallow neural network, DBN has a powerful mapping ability and can accurately characterize the complex mapping relationship between the original current signal and the ATSE fault type. (3) The optimized DBN has no over-fitting phenomena using IGWO, and the classification accuracy of ATSE faults is improved.
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Received: 13 November 2022
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2024, Vol. 39 
