Grid-moving equivalent source method in a probability framework for the transformer discharge fault localization

The transformer discharge fault localization is important for the power supply reliability. The audible sound radiated by the transformer can reflect the extensive operation information. Thus, discharge faults can be localized by high spatial resolution acoustic imaging techniques to facilitate the inspection and maintenance of power equipment. In this paper, the grid-moving equivalent source method (GMESM) is proposed to visualize the discharge fault on a transformer, where the equivalent source grids can approach the discharge fault position adaptively. A probability framework is established in GMESM. The noise power can be firstly estimated by the Bayes rule to avoid an additional measurement of the background noise. Then, the expectation maximization algorithm is used to optimize the proposed model. Two experiment cases are used to validate the proposed method. It turns out that the proposed GMESM can visualize the discharge fault on the transformer with high spatial resolution and accuracy.