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Substation electric power equipment detection based on patrol robots

机译:基于巡逻机器人的变电站电力设备检测

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The expansion of power grid scale not only increases the transmission capacity, but also increases the probability of power plant facilities failure. The large scale of power grid and its high voltage make fault detection have heavy workload and high risk. In this paper, patrol robot, infrared imaging technology for detecting equipment faults and support vector machine (SVM) for identifying infrared image of faulty equipment were briefly introduced. Then, SVM for identifying infrared image of faulty equipment was simulated and analyzed with MATLAB software and compared with information entropy method. Then, patrol robot which applied two recognition methods in substation of X city power supply bureau were operated for one month. The results showed that the recognition accuracy of SVM was above 97% in the simulation experiment, which was significantly higher than that of information entropy method. In actual operation, the efficiency of patrol robot was higher than that of the traditional manual patrol, and the failure recognition rate of patrol robot which applied the two methods was close to the simulation results.
机译:电网量表的扩展不仅提高了传输容量,而且还增加了电厂设施故障的概率。大规模的电网及其高压使故障检测具有繁重的工作量和高风险。本文简要介绍了巡逻机器人,用于检测设备故障的红外成像技术和用于识别故障设备红外图像的设备故障和支持向量机(SVM)。然后,模拟和分析用于识别故障设备的红外图像的SVM并用MATLAB软件分析,并与信息熵方法进行比较。然后,在X城市电源局变电站中应用两种识别方法的巡逻机器人运营一个月。结果表明,仿真实验中SVM的识别精度高于97%,显着高于信息熵方法。在实际操作中,巡逻机器人的效率高于传统手动巡逻的效率,并且应用这两种方法的巡逻机器人的故障识别率接近模拟结果。

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