A hybrid ensemble learning approach for the aging-dependent reliability prediction of power transformers

As the most critical equipment for electricity transmission, power transformers (PTs) generally have irreversible insulating degradation, which makes them susceptible to aging breakdowns. Predicting the aging-related failure probability (AFP) of PTs as precisely as possible is beneficial for reliability-centered maintenance and retirement strategies. This paper proposes a Long Short Term Memory (LSTM) network and Particle Swarm Optimization (PSO) combined prediction approach for this essential task. Firstly, a classic AFP evaluation method and computation-based degree of polymerization (DP) estimation method are combined to construct the historical AFP time series of PTs. Then, the produced AFP time series are treated as input to train the LSTM neural network, which can be used to predict the AFPs of individual PTs within a predefined horizon. To enhance the prediction performance of the LSTM network, the PSO algorithm is introduced to search the optimal LSTM network parameters adaptively. Numerical tests based on real transformer data from China’s Chongqing Electric Power Company have been conducted to show the efficacy and utility of the suggested approach.