基于改进模糊聚类的典型日负荷曲线选取方法

Selection method of typical daily load curve based on improved fuzzy clustering

典型日负荷曲线对负荷调度计划以及运行控制有着重要意义,针对常用的传统典型日负荷曲线选取方法不满足目前电力市场需求的问题,提出了基于自适应因子与概率统计法相结合的改进模糊聚类算法典型日负荷曲线选取新方法,应用日负荷率、日负荷波动率等描述性特征指标,确定最优聚类数;引入模糊-离散系数,辨识样本数据中的畸变日,并予以剔除;计算日负荷与月平均负荷之间的相关系数,依据相关系数选取典型日负荷曲线。以新疆电网2015年1月份负荷数据进行实例仿真,结果表明所提方法能够准确选出典型日负荷曲线,验证了方法的可行性和有效性。

The typical daily load curve has great significance to the load dispatching plan and operation control.Aiming at the problem that the traditional typical daily load curve selection method cannot meet the demand of the current electricity market,an improved fuzzy clustering algorithm based on the combination of adaptive factor and probability statistics is proposed to select the typical daily load curve.The optimal clustering number is determined by using the descriptive characteristic indexes such as daily load rate and daily load fluctuation rate.The fuzzy-discrete coefficient is introduced to identify the distortion day in the sample data,and the correlation coefficient between the daily load and the monthly average load is calculated.The typical daily load curve is selected according to the correlation coefficient.The simulation results of January 2015 load data of Xinjiang Power Grid show that the proposed method can accurately select the typical daily load curve and verify the feasibility and effectiveness of the proposed method.