基于油中溶解气体分析的变压器故障预测

Transformer fault prediction based on analysis of dissolved gas in oil

预测变压器潜伏性故障对评估其健康状态至关重要。提出一种新型变压器故障预测方法,首先以LSTM网络为载体搭建时间注意力机制预测框架,并采用IALO算法优化其参数;其次利用优化的模型预测变压器油中溶解气体;然后采用MPA算法优化的SVM模型对气体预测结果进行故障诊断;最后统计诊断结果并与实际运行状态对比验证模型。实验结果显示在第42~58天内运行状态异常次数最多为29次,未来两个月内运行异常几率为86.89%,其中中温过热故障占比最高为88.67%,与实际情况误差仅为2.46%和1.29%,预测结果与实际运行情况符合较高,证明了所提方法在准确预测变压器运行状态异常时间点和故障类型中的可行性。

Predicting latent faults of transformers is essential to evaluate their health status.This paper proposes a new transformer fault prediction method.First of all,a prediction framework of temporal attention mechanism is built based on LSTM network,and the IALO algorithm is used to optimize the hyperparameters of LSTM.Afterwards,use the optimized model to predict the dissolved gas in transformer oil.Then,the SVM model optimized by the MPA algorithm is used to diagnose the gas prediction results.Finally,the fault diagnosis results are counted,and the model is verified by comparing with the actual operation state.The experimental results show that the abnormal operation status is up to 29 times form the 42 th to 58 th day,and the abnormal operation probability is 86.89%in the next two months,among which the proportion of medium temperature overheating fault is highest,88.67%,and the errors from the actual situations are only 2.46%and 1.29%.The predicted results are in good agreement with the actual operating situations of transformers,which proves the feasibility of the proposed method in accurately predicting the time point and fault type of abnormal operation states of transformers.