Online monitoring and fault early warning prediction method for the operational status of steam turbine sliding pin systems

Abstract

Abstract In modern power systems, ensuring the safe and reliable operation of the sliding pin system in large steam turbine generator sets is crucial. However, the measurable parameters in the current distributed control system are insufficient for fault early detection of the sliding pin system’s operational state. Additionally, there is a lack of relevant research in this area at present. This paper utilizes a typical 300 MW-class unit as a case study. By analyzing the operational mechanism and fault modes of the sliding pin system, a method for online monitoring of its operational status based on cylinder expansion measurement parameters is proposed. Based on this foundation, taking the advantage of long short-term memory (LSTM) network to effectively extract features from univariate time series, and integrating improved particle swarm optimization (IPSO) for automatic hyperparameter optimization, a multi-step prediction model and fault prediction method for the operational status of sliding pin systems based on IPSO-LSTM is designed. Test results based on various performance evaluation metrics indicate that the IPSO-LSTM algorithm significantly enhances the prediction model’s accuracy. Specifically, the TVIWAC-PSO model, which varies the inertia weight (TVIW) and acceleration coefficients simultaneously in the PSO algorithm, optimizes by enhancing global search in the early stages and emphasizing local search in the later stages of iteration. Furthermore, TVIWAC-PSO demonstrates superior performance in optimizing the hyperparameters of the LSTM algorithm. Finally, based on the gap standard between sliding pins and keyway in the actual operating procedures of the unit, combined with the low-pressure cylinder and rotor expansion difference operation standard, thresholds for anomaly detection and early fault prediction of the sliding pin system’s operational status are provided. This study holds significant engineering application value.