An online life prediction method considering adaptive fault failure threshold differentiation of transformer in consideration of missing of deterioration information

Abstract

The application discloses a transformer adaptive fault failure threshold differentiation online life prediction method considering missing degradation information, belongs to the field of power equipment degradation evaluation and operation and maintenance, and comprises the following steps: a prediction framework including degradation data analysis, comprehensive degradation data composition, intelligent smoothing fitting, degradation model construction and adaptive dynamic prediction is constructed. Through a fuzzy logic and TIME-LLM hybrid model, multi-index weighted fusion is realized, intelligent adaptive smoothing fitting is adopted to process comprehensive degradation data with different noise characteristics, a TFD-Hformer model is used to capture time-frequency domain nonlinear coupling characteristics, and a variational Bayesian neural network is used to generate a differentiated fault threshold correction factor by combining stochastic variation inference. The application can realize reliable prediction in the historical data missing scene, adaptively adapt to the individual differences of different transformers, and support efficient prediction of large-scale clusters.

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