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19 October 2023 Fault detection method of DC distribution network based on GRNN
Yingliang li, Zhiwei Dong, Deming Wang, Fei Li, Qi Zhu
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Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 127091U (2023) https://doi.org/10.1117/12.2684998
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China
Abstract
DC distribution network has a good consumption effect on new energy, and DC fault detection is one of the key technical problems for the large-scale application of DC distribution network. Aiming at the problems of difficult threshold setting and long detection time in existing fault detection schemes, a fault detection scheme for DC distribution network based on generalized regression neural network is proposed. Firstly, the transient voltage change rate of positive and negative lines is used as the fault start criterion. Then the disturbance of DC distribution network is eliminated by low voltage protection. Secondly, the Spearman correlation coefficient is used to analyze the correlation of the transient voltage of the positive and negative pole lines to distinguish symmetric faults and asymmetric faults. For symmetric faults combined with the transient voltage change rate of positive and negative pole lines, bipolar short circuit faults and AC faults can be distinguished. For asymmetric faults, GRNN is trained offline with simulation data, and the fault poles are distinguished according to the online output results of GRNN. The fault detection scheme is verified on the MATLAB / simulink platform to build a simulation model of the DC distribution network at both ends, which can quickly and accurately identify the fault.
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Yingliang li, Zhiwei Dong, Deming Wang, Fei Li, and Qi Zhu "Fault detection method of DC distribution network based on GRNN", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 127091U (19 October 2023); https://doi.org/10.1117/12.2684998
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KEYWORDS
Detection and tracking algorithms
Correlation coefficients
Neural networks
MATLAB
Simulink
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