1. School of Automation Engineering Shanghai University of Electric Power Shanghai 200090 China; 2. Guodian Nanjing Automation Co., Ltd Nanjing 210032 China
Abstract：This paper proposes a new method of soft-measurement model for transformer winding hot spot temperature based on particle swarm optimization-support vector regression (PSO-SVR) algorithm, and verifies its prediction effect by making a better use of the monitoring information of the transformer. In this method, taken use of an improved particle swarm optimization algorithm based on passive aggregation, the SVR model parameters of the regression of support vector machine are optimized and the optimal solution is found. The relevant factors of the transformer operation are fully considered in predicting the temperature of the winding hot spots. Compared with BP neural network and SVR method, the training and prediction results of 110kV transformer in a sample city show that the model has better prediction ability.
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2018, Vol. 33 
