Fault diagnosis of power equipment based on dissolved gas analysis and LS fusion combining neural network

In this paper, a new method for power equipment fault diagnosis is presented based on a least square (LS) fusion combining neural network and dissolved gas analysis (DGA). Contents of five characteristic gases obtained by DGA are preprocessed through a special dada dealing process, and 6 features for fault diagnosis are extracted Then five child back-propagation (BP) artificial neural networks (ANNs) with different structure are applied to diagnosis the fault respectively. The diagnosing results of the child ANNs are fused by the LS weighted fusion algorithm. The fault is identified based on the fused results at last. Compared with single neural network, the LS fusion combining network can identify fault type safely when the fault is deceptive, however, a single neural network may fail in this case. Furthermore, the combining neural network is more reliable than single neural network. The test results of power transformer fault diagnosis proved the conclusions.