基于油中溶解气体分析的DBN-SSAELM变压器故障诊断方法

Transformer DGA fault diagnosis method based on DBN-SSAELM

为了保证油浸式变压器故障诊断精度的同时,提高诊断方法的收敛速度以及泛化能力,提出一种基于DBN-SSAELM的变压器故障诊断方法。首先,利用深度置信网络(deep belief networks, DBN)对油中溶解气体浓度比值数据进行特征提取。其次,利用具有较强学习能力的极限学习机(extreme learning machine, ELM)替换传统DBN分类模型中的Softmax分类器,深入分析特征值与故障类型之间的关联性,提高模型的收敛速度。然后,利用麻雀搜索算法(sparrow search algorithm, SSA)优化ELM模型的输入权值和隐藏层节点偏置,以提高模型诊断结果的准确率和稳定性。最后,选用准确率、查全率、查准率和收敛速度对优化前后的模型进行性能评估。最终实验结果表明:所提出的DBN-SSAELM变压器故障诊断方法,故障诊断准确率高、泛化能力强、稳定性好,平均准确率达到96.50%,适用于变压器故障诊断。

To ensure the fault diagnosis accuracy of an oil immersed transformer and improve convergence speed and generalizability, this paper proposes a transformer fault diagnosis method based on deep belief network and extreme learning machine optimized by a sparrow search algorithm(DBN-SSAELM). First, deep belief networks(DBN) are used to extract the features of dissolved gas data in oil. Second, an extreme learning machine(ELM) with strong learning ability is used to replace the Softmax classifier in the traditional DBN classification model to deeply analyze the correlation between features and fault types and improve convergence speed. Then, a sparrow search algorithm(SSA) is used to optimize the input weights and bias of the hidden layer node of the ELM to improve the accuracy rate and stability of the mode. Finally, the rate of accuracy, recall, precision and convergence speed of fault diagnosis are selected to evaluate the performance of the model before and after optimization. The results of transformer fault diagnosis show that the proposed DBN-SSAELM transformer model has higher fault diagnosis accuracy, stronger generalizability and better stability, and the average accuracy is 96.50%. This is suitable for transformer fault diagnosis.