Research on power transformer fault prediction model based on LSTM neural network

Power transformer is one of the core equipment of power grid system, and the related research on safe and stable operation of power transformer has always been the focus of attention in the power industry. In recent years, neural networks have developed rapidly and have been widely used in the power industry. Studies have shown that the neural network method has strong applicability to the prediction and diagnosis of the power transformer operating state. Aiming at the limitations of traditional neural networks that cannot use time series information and have long incubation periods and various types of power transformer faults, this paper establishes a power transformer dissolved characteristic gas time series data prediction model based on long and short-term memory neural networks.