Accurate identification of health status of substation equipment based on multi-source data fusion

To improve the accuracy of substation equipment fusion analysis and reduce the failure rate of substation equipment, a precise identification method for the health status of substation equipment based on multi-source data fusion is proposed. Firstly, the multi-source data of substation equipment status information are standardised, including data classification, encoding, and coordinate system transformation. Secondly, by utilising the nonlinear mapping capability of the RBF neural network and the weight allocation of the OWA operator, the effective fusion of multi-source data information for substation equipment can be achieved. Finally, by introducing correlation coefficients, constructing a Model-1 feature domain, and utilising Copula function classification combined with the ID3 algorithm decision tree, accurate identification and classification of the health status of substation equipment were achieved. The test results demonstrate that the proposed method achieves a maximum data matching accuracy of 0.95 and reduces the failure rate of substation equipment to below 2%.