基于深度信念网络的高压断路器故障识别算法

针对高压断路器故障现有故障诊断算法中,特征提取不准确导致分类正确率较低的问题,提出了基于深度信念网络的高压断路器故障识别方法。深度信念网络(Deep Belief Network,DBN)是非监督的深度神经网络,由多个受限玻尔兹曼机(Restricted Boltzmann Machine,RBM)叠加起来组成。首先使用无标签的数据样本自下而上的对各RBM层逐层训练,得到各层最优参数;再以此为初始参数将DBN展开成反向传播的结构,使用带标签的数据样本进行全局的参数微调;最后得到DBN分类网络。这一过程中,有效避免了特征提取的人工操作,解决了网络训练的局部最优问题,使断路器故障诊断更加智能化。通过试验结果可知,该方法可准确、可靠地用于诊断断路器主要机械故障。

Fault recognition algorithm for high voltage circuit breakers based on deep belief network

To solve the problem of low classification accuracy due to incorrect feature extraction in the existing fault diagnosis algorithms for high voltage circuit breaker, the fault recognition algorithm based on deep belief network is proposed. Deep Belief Network (DBN), as an unsupervised deep learning algorithm, is composed of multiple Restricted Boltzmann Machines (RBM). Firstly, using the unlabeled data samples, each RBM layer is trained layer by layer from bottom to top, and the optimal parameters of each layer are obtained. Secondly, using the parameters obtained before as the initial parameters, the DBN is expanded into a structure that propagates backwards. Then the labeled data samples are used for global parameter tuning. Finally, the DBN classification network is established. In this process, the artificial feature extraction is effectively avoided, the local optimum problem of network training is solved, and the circuit breaker fault diagnosis is more intelligent. Experimental results show that this method can be applied to the diagnosis of major mechanical faults of circuit breakers accurately and reliably.