基于机器学习算法的10kV配电网带电作业安全检测方法

Machine Learning Algorithm Based Safety Detection Method for Live Working in 10 kV Distribution Network

以往的10 kV配电网带电作业安全检测方法由于规划的检测路径不准确,导致检测效果较差。因此,提出了基于机器学习算法的10 kV配电网带电作业安全检测方法。通过计算配电网的电场强度,构建带电作业的安全检测模型。在此基础上,利用机器学习算法,对作业环境进行实时检测,规划出带电作业安全检测路径,并通过带电作业时配电网中线路的运行特征,实现配电网带电作业的安全检测。通过上述的设计,完成对10 kV配电网带电作业安全检测方法的设计。在仿真试验中,和以往的10 kV配电网带电作业安全检测方法相比,提出的基于机器学习算法的10kV配电网带电作业安全检测方法目标检测正确率高达100%,检测效果更好。

The previous safety detection methods for live working in 10 kV distribution networks have poor detection results due to inaccurate planned detection paths.Therefore,a machine learning algorithm based safety detection method for live working in 10 kV distribution networks has been proposed.By calculating the electric field strength of the distribution network,a safety detection model for live working is constructed.Based on this,machine learning algorithms are used to detect the working environment in real-time,plan the safety detection path for live working,and achieve safety detection for live working in the distribution network by analyzing the operating characteristics of the lines in the distribution network during live working.Through the above design,the design of a safety detection method for live working in a 10 kV distribution network has been completed.In simulation experiments,compared with previous safety detection methods for live working in 10 kV distribution networks,the proposed machine learning algorithm based safety detection method for live working in 10 kV distribution networks has a target detection accuracy of up to 100%and better detection performance.