Research into Power Transformer Health Assessment Technology Based on Uncertainty of Information and Deep Architecture Design

The uncertainty of the evaluation information is likely to affect the accuracy of the evaluation, when conducting a health evaluation of a power transformer. A multilevel health assessment method for power transformers is proposed in view of the three aspects of indicator criterion uncertainty, weight uncertainty, and fusion uncertainty. Firstly, indicator selection is conducted through the transformer guidelines and engineering experience to establish a multilevel model of transformers that can reflect the defect type and defect location. Then, a Gaussian cloud model is used to solve the uncertainty of the indicator criterion boundary. Based on association rules, AHP, and variable weights, the processed weights are calculated from the update module to obtain comprehensive weights, which overcomes the uncertainty of the weights. Improved DSmT theory is used for multiple evidence fusion to solve the high conflict and uncertainty problems in the fusion process. Finally, through actual case analysis, the defect type, defect location, and overall state of the transformer of the device are obtained. By comparing with many defect cases in a case-study library, the evaluation accuracy rate is found to reach 96.21%, which verifies the practicability and efficiency of the method.