配电自动化供电可靠性监测方法研究

Research on power supply reliability monitoring method of distribution automation

为了研判配电自动化供电系统的停电原因,评估供电系统的可靠性,需对供电系统的运行过程进行监测。传统监测方法多采用人工监测的方式,过分依赖维护人员自觉性和专业性,导致监测效果较差。为此提出基于贝叶斯网络的配电系统可靠性监测方法。分析配电自动化供电系统的结构,引入分布式电源接入方式,设置时间参数创建配电自动化供电的自愈控制功能,根据该功能对系统运行过程中可能出现的停电问题进行研判。建立由逻辑关系“与”、“或”、“因果”节点模型共同组成系统运行监测的贝叶斯网络模型,并计算系统运行节点的工作概率。设计系统运行监测流程,将不同类型停电故障在不同地点按照其发生时间进行统计,得出停电发生概率,根据前后停电故障区域提供负荷量以及供应负荷的重要指标,获取配电系统运行安全指标。实验结果可知,该方法对供电系统运行监测的最高精度可达到92%,能够有效研判出停电原因,为准确评估供电系统的可靠性提供帮助。

In order to judge the cause of power failure of the distribution automation power supply system and evaluate the reliability of the power supply system,it is necessary to monitor the operation process of the power supply system.Traditional monitoring methods often use manual monitoring methods,relying too much on the consciousness and professionalism of maintenance personnel,resulting in poor monitoring results.To this end,a reliability monitoring method based on Bayesian network is proposed.Analyze the structure of the distribution automation power supply system,introduce the distributed power access mode,set the time parameter to create the self-healing control function of the distribution automation power supply,and judge the power failure problem that may occur during the system operation according to the function.A Bayesian network model consisting of logical operation"and","or",and"causal"node models is constructed,and the working probability of the system running nodes is calculated.The system operation monitoring process is designed to collect different types of power outage faults according to their occurrence time in different locations,and the probability of power outage is obtained.According to the important indicators of the load and supply load in the front and rear power failure areas,the power distribution system operation safety indicators are obtained.The experimental results show that the highest accuracy of the power supply system operation monitoring can reach 92%,which can effectively determine the cause of power failure and provide assistance for accurately evaluating the reliability of the power supply system.