Based on image processing of SF6 gas leakage on-line pattern recognition method, this paper achieves gas leakage feature extracting, on-line identification of gas leakage and leakage points, SF6 gas leakage can be on-line automatic identification. The simulation results show the feasibility of the algorithm. Compared with the traditional method, paper provides a more intuitive discrimination basis for field staff , as well as for the depth of the late testing data mining provides a research way of thinking

Based on multi-source substation equipment inspection data, this article achieve fault diagnosis in the case of incomplete understanding of the mechanism of substation equipment, by establishing fault diagnosis model.This article selected a substation equipment failures and operational information in different conditions as simulation study. The simulation results show the feasibility of the algorithm. Compared with the traditional fault diagnosis model, this method is more flexible , have a stronger ability to handle noisy data and good prospects.

本文提出了一种基于RFID无线射频技术、无线局域网技术、物联网技术以及支持移动终端的配网智能巡检培训系统,创建了电力培训的一种全新的工作模式,可以有效提高配网巡检工作效率,规范配网的巡检作业,提高配网巡检的培训效果和配网管理水平,从而满足配网精益化管理的需求.

To analysis the main factors that lead to device fail and identify the fault indicator, this paper proposes a method about correlation identification between the multi-factor and equipments' failure. Firstly, this paper sets the related factors influencing the equipment's failure as numerical value, based on distance correlation definition, calculate the correlation between each factors and equipment's failure. So that determines the power of the main causes of equipments failure. At last, through 330 kV transformer fault case validates the effectiveness and unbiasedness of the proposed method.

To analyze the main causes of electric power equipment's failure, this paper based on. distance correlation definition, proposed a method about correlation identification between the multi-factor and equipment's failure. Firstly, this paper sets the related factors influencing the equipment's failure as numerical value, based on distance correlation definition, calculate the correlation between each factors and equipment's failure, so as to determine the power of the main causes of equipment's failure. At last, through 220 kV transformer fault case validates the effectiveness of the proposed method.

Fault diagnosis is an effective means to assure the safe operation of power system. In this paper, a detection and monitoring of transformer fault diagnosis of multi-source data fusion technology is introduced. Based on the correlation distance, to calculate the relationship between detection and monitoring data and equipment failure, using the weight function to set fault indicators, status online monitoring data over the fault indicators, judging the equipment what kind of failure is happened, improving the precision of fault diagnosis.

In order to ensure the safe and stable operation of power grid and reduce the huge economic loss by equipment failure, Failure prognostic system plays a vital role. Based on distance correlation principle ,this paper proposes a method of failure prognostic. We analyze correlation between various factors influencing the equipment failure and failure from a large number of historical data. Then sets fault indicators by weight function. When online testing data are beyond the fault indicator ,system alarms. Then system prompts operating personnel to check equipments state. At last, through overheating fault of transformer case validates the effectiveness of the proposed method.