Prediction Method of Gas Concentration in Transformer Oil Based on CEEMD and TGSCSO-LSTM
The prediction of dissolved gas concentration in oil can provide important data basis for power transformer condition assessment and early fault diagnosis.Therefore,in order to solve the problem of difficult parameters selection of long short-term memory network(LSTM)prediction model,and to improve the prediction accuracy of dissolved gas concentration in transformer oil,a gas concentration prediction method in transformer oil is proposed based on complementary ensemble empirial mode decomposition(CEEMD)combined with TGSCSO-LSTM algorithm.The CEEMD algorithm is used to decompose the original gas concentration series into a series of components with certain frequency characteristics to improve the predictable performance of the original series.The LSTM prediction model is established for each component,meanwhile,the LSTM network parameters are optimized and selected by using the Tent mapping random initialization population and Gaussian disturbance improved sand cat swarm optimization algorithm(SCSO)to improve the prediction accuracy of the algorithm.Finally,the predicted results of each component is reconstructed to obtain the final predicted results of dissolved gas concentration in oil.The proposed method is tested by using the actual gas concentration data of a 500 kV transformer.The experimental results show that the proposed method has excellent prediction performance of dissolved gas concentration in oil and has good application value.
Dissolved gas in oilcomplementary ensemble empirical mode decompositionsand cat swarm optimizationlong short-term memory network
NETL
NSTL
万方数据
