Abstract
Due to the difficulty in low diagnostic accuracy under the three-level inverter with multiple faults, a novel fault detection approach based on the improved stacked auto-encoder (SAE) is proposed in this paper. By analyzing the circuit characteristics of the three-level Neutral Point Clamped (NPC) inverter, the inverter bridge arm phase voltage is selected as the fault characteristic signal, and the feature vector formed by the amplitude spectrum is used as the input of SAE network. Since the feature extraction performance of the SAE network has a great correlation with the number of hidden layer nodes, the gravitational search algorithm is chosen to obtain the optimal neuron number of hidden layers, so that the deep learning network could extract features automatically and classify the multiple faults accurately. Finally, the simulation and comparison analysis results show that the proposed method has higher classification accuracy compared with traditional BP neural network and support vector machine. The classification accuracy rate of the three-level NPC inverter faults can reach 100%, which further validates the effectiveness of the developed method.
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This work is supported by Shanghai Science and Technology Innovation Plan (17595800900).
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Wu, J., Yan, Z., Sun, Q. (2020). Multiple Faults Detection of Three-Level NPC Inverter Based on Improved Deep Learning Network. In: Abawajy, J., Choo, KK., Islam, R., Xu, Z., Atiquzzaman, M. (eds) International Conference on Applications and Techniques in Cyber Intelligence ATCI 2019. ATCI 2019. Advances in Intelligent Systems and Computing, vol 1017. Springer, Cham. https://doi.org/10.1007/978-3-030-25128-4_195
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DOI: https://doi.org/10.1007/978-3-030-25128-4_195
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