基于OVMD-HWOA-KELM模型的变压器油中溶解气体体积分数预测方法

Prediction Method of Dissolved Gas Volume Fraction in Transformer Oil Based on OVMD-HWOA-KELM Model

针对变压器油中溶解气体序列波动性、随机性较强难以精确预测的问题,提出一种基于最优变分模态分解(optimal variational mode decomposition,OVMD)、混合型鲸鱼优化算法(hybrid whale optimization algorithm,HWOA)和核极限学习机(kernel extreme learning machine,KELM)的组合预测模型。首先,运用OVMD获取最优分解参数,并将原始序列分解为一系列相对平稳的分量;其次,通过在鲸鱼种群中融入混沌映射、非线性收敛参数、自适应权重因子和改进的算术优化算法提出HWOA算法,并利用测试函数验证HWOA算法的优越性;然后,对各分量分别构建KELM预测模型,使用HWOA优化KELM的关键参数。最后,将各分量的预测结果叠加重构,得到最终预测结果。案例分析表明,所提模型对变压器正常和异常案例预测的决定系数分别可达97.7%和93.46%,相较于现存方法,该模型具有更好的准确性和适应性,可为电力变压器运维管理提供有利技术支撑。

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.