基于优化组合方法的光伏台区电能质量预警方法研究

Research on Early Warning Method for in Photovoltaic Substation Station Power Quality Based on Optimal Combination Method

日益增多的分布式发电加剧了电能质量问题的产生,为确保电能质量,对电能质量进行合理预警具有重要意义。该文提出一种基于组合优化方法的光伏台区电能质量预警模型。首先针对光伏台区电能质量指标,利用SOM(Self-Organizing Map,自组织映射)神经网络算法建立聚类模型,得到台区分类结果及其对应的聚类中心。然后,利用基于遗传算法改进后的Otsu法(最大类间方差法)确定各类别电能质量指标的预警阈值,构建出基于优化组合方法的电能质量预警体系。最后以江苏省某片光伏台区监测点电能质量数据进行仿真计算,结果表明所提方法具有良好的适应性,并能有效应用于电能质量预警。

The increasing number of distributed generation aggravates the problems of power quality.In order to ensure power quality,reasonable early warning of power quality is of great significance.In this paper,a photovoltaic power quality early warning model based oncombinatorial optimization method is proposed.Firstly,aiming at the power quality index of photovoltaic station,a clustering model is established by using SOM(Self-Organizing Map)neural network algorithm,and the classification results and corresponding clustering centers are obtained.Then,the Otsu method maximum inter-class variance method)improved based on genetic algorithm is used to determine the early warning threshold of all kinds of power quality indicators,and the power quality early warning system based on optimal combination method is constructed.Finally,the power quality data of a photovoltaic station in Jiangsu Province are simulated,and the results show that the proposed method has good adaptability and can be effectively applied to power quality early warning.