507 / 2022-03-24 20:24:52

Power Transformer Oil Temperature Prediction Based on Spark Deep Learning

top oil temperature,CNN algorithms,prediction,power transformer,spark

Abstract: Power transformer is the key equipment in power system. The oil temperature is an important factor affecting the service life and load capacity of oil immersed power transformer. Applying big data technology to oil temperature prediction system can more reliably and efficiently evaluate the thermal characteristics of transformer, so as to realize on-line monitoring, state prediction and fault diagnosis of transformer. Therefore, based on Hadoop + spark big data platform and its ecological components, this paper evaluates and predicts the long-time series data of oil temperature at the top of power transformer. Firstly, store the collected data in Hadoop distributed file system. Then combined with Spark operation framework, convolutional neural networks (CNN), long short-term memory (LSTM) and other related algorithms are used to process the data, train the model and evaluate it. The final results show that: CNN algorithm model built in this paper has higher prediction accuracy than other related algorithm models. For quarterly short time series prediction, the average value of root mean square error (RMSE) of the CNN model is 0.907 and the average mean absolute percentage error (MAPE) value is 2.8%. The results are consistent with the existing theories. The platform has high efficiency and reliability, and can accurately predict the oil temperature of transformer combined with CNN algorithm. This paper can provide a certain reference value for power grid big data analysis and stable operation of power transmission and transformation equipment. analysis and stable operation of power transmission and transformation equipment.