Voltage violation of the distribution network greatly affects the power supply quality and the use’s power consumption experience. To better improve the voltage quality of the power grid, real-time analysis of voltage violation can helps power grid personnel to handle voltage violation instantly and efficiently though analyzing the attribute indicators on dis-tribution network lines. However, many studies are concerned only with the single voltage violation cause, and ignore the more complicated phenomenon of voltage violations. In this paper, we proposed a joint attributes based neural network multi-classification (JANN) model that take mutual influence between attributes from different nodes in the distribution network into account when voltage violations are detected. Concretely, we construct the set of joint attributes from each node in the distribution network though real-time monitoring of the power grid. Then the joint attribute based neural network model is constructed to analyze the voltage violation phenomenon, and determine the cause multi-classification of voltage violations. Experimental results show that the proposed (JANN) method can reach 95.79% F1-score rate on multi-classification of voltage violation causes.

In this paper, a self powered circuit with composite energy storage for monitoring terminal in distribution network is proposed, which is consist of a rectifier circuit, DC/DC converter and composite energy storage. Firstly, the self powered circuit obtained energy by CT (Current Transformer) from the lines in distribution network. Then, the expected DC voltage was obtained by rectifier circuit and regulated by DC/DC converter. To enhance the power supply reliability, composite energy storage is applied, which is regulated by coordinated control. To verify the effectiveness, a simulation model was built in MATLAB, and the result are satisfactory.

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