To address the problems that it is difficult to accurately predict the volatility and stochasticity of dissolved gas sequences in transformer oil,this paper proposes a combined prediction model based on optimal variational mode de-composition(OVMD),hybrid whale optimization algorithm(HWOA),and kernel extreme learning machine(KELM).Firstly,OVMD is applied to obtain the optimal decomposition parameters and decompose the original sequence into a se-ries of relatively smooth components.Secondly,the HWOA algorithm is proposed by incorporating chaotic mapping,nonlinear convergence parameters,adaptive weight factors and improved arithmetic optimization algorithm in the whale population,and the superiority of the HWOA algorithm is verified by using the test function.Then,the KELM prediction model is constructed for each component separately,and the key parameters of KELM are optimized by using HWOA.Finally,the prediction results of each component are superimposed and reconstructed to obtain the final prediction results.The case study shows that the decision coefficients of the model proposed in this paper for the prediction of normal and abnormal transformer cases can be up to 97.7%and 93.46%,respectively.Compared with the existing methods,the model in this paper has better accuracy and adaptability,and it can provide favorable technical supports for the operation and maintenance management of power transformers.
关键词
油中溶解气体/最优变分模态分解/融合型鲸鱼优化算法/核极限学习机/变压器状态预测
Key words
dissolved gas in oil/optimal variational mode decomposition/hybrid whale optimization algorithm/kernel extreme learning machine/transformer condition prediction
维普
万方数据