大数据环境下区域电网防污闪策略研究及应用

Research on Anti-pollution Flashover Strategy for Regional Power Grid Using Big Data and Its Application

绝缘子表面等值盐密是评估绝缘子积污状况的主要手段,目前基于挂网绝缘子实测盐密存在着时效性差合成本高等不足。本文提出一种基于PM;等实体污染物浓度和气象条件(风向、风速、降雨量、温湿度等)数据的区域电网积污预测与防污闪策略。首先基于空气质量数据建立了空间环境预测模型;其次结合废气等各强污染源排放数据,利用神经网络算法对空气质量数据进行修正;然后根据绝缘子在自然条件下的积污规律、大气环境数据、气象参数等多源大数据构建绝缘子积污预测模型,利用经验公式,修正金华电网污区等级;最后制定基于绝缘子污秽度的防污闪策略。通过上述方法,对金华电网部分绝缘子进行了污秽度评估并对比历史测量数据,显示该方法可行,实现了电网污秽度科学评估及电网污闪精细防控。

The equivalent salt deposit density on insulator surface is the main means to evaluate the pollution accumulation of insulators. At present, the measured salt density based on grid insulators has the disadvantages of poor timeliness and high cost. This paper presents a method based on PM;pollution accumulation prediction and anti-pollution flashover strategy of regional power grid based on the concentration of physical pollutants and meteorological conditions: wind direction, wind speed, rainfall, temperature and humidity, etc. Firstly, a spatial environment prediction model is established based on air quality data. Secondly, combined with the emission data of strong pollution sources such as waste gas, the air quality data is modified by neural network algorithm. Then, according to the multi-source big data such as insulator pollution accumulation law under natural conditions, atmospheric environment data and meteorological parameters, the insulator pollution accumulation prediction model is constructed, and the pollution area grade of Jinhua power grid is modified by using empirical formula. Finally, the anti-pollution flashover strategy based on insulator pollution degree is formulated. Through the above methods, the pollution degree of some insulators in Jinhua power grid is evaluated, and compared with the historical measurement data, it shows that this method is feasible, which realizes the scientific evaluation of power grid pollution degree and the fine prevention and control of power grid pollution flashover.