Chaotic Optimization of BP Neural Networks for Oil-Paper Insulated Transformer Life Prediction Based on Health Index Models

The aging of oil-paper insulated transformer components significantly impacts their service life. Accurate health assessment is crucial for predicting failure rates and residual life, which is vital for ensuring operational safety. This paper employs the bathtub curve concept and Weibull distribution to fit collected oil-paper insulated transformer failure rate data, obtaining the failure rate curve. Considering operational environment and load factors, a health index model is established for residual life prediction. By optimizing the weight and bias parameters of the backpropagation (BP) neural network using an adaptive chaotic sequence strategy, a multi-parameter correlated transformer life prediction model is constructed. A cross-validation mechanism is introduced to enhance the model’s generalization ability. Experimental results from training and testing demonstrate that the proposed method achieves higher prediction accuracy, with average errors of 5.36% for annual failure rate and 3.32% for residual life, confirming its effectiveness and applicability in transformer life prediction.