A Large number of monitoring data is conducive to harmonic modeling, so statistical indicators recorded by monitoring devices are utilized for modeling.However, the user's uncertain operational characteristics are not reflected in existing model and the accuracy of proposed harmonic user's model is also affected by it.On this basis, an analyzing method for typical modes of harmonic customers is proposed based on interval arithmetic.Firstly, the harmonic interval data is built based on different types of customer monitoring data, and clustering characteristics are concluded from interval data via the principal component analysis (PCA).Secondly, the partition of typical modes and elimination of transitional modes are realized based on affinity propagation (AP)clustering algorithm and outlier degree index respectively.Thirdly, the typical parameters for each typical mode are defined and calculated.The case study shows that the proposed method can recognize the uncertain information of harmonic in monitoring periods.It also offers the guidance for the access and management of harmonic customers.%大量监测数据有利于谐波建模, 但利用监测装置记录的统计型指标进行建模无法表征用户不确定的运行特性, 并影响所建立的谐波用户模型的精确度.因此, 提出一种基于区间运算的谐波典型工况分析方法:首先基于监测数据类型构造谐波区间数据, 并通过针对区间主成分分析 (PCA)方法提取聚类特征量, 然后分别基于仿射传播 (AP)聚类算法和离度指标划分谐波用户典型工况和剔除过渡工况, 最后定义和计算各个工况的典型参数.算例分析表明, 所提出的方法能够基于谐波用户监测时段内的不确定信息, 划分谐波用户典型工况, 可为谐波用户的接入和治理提供依据.
展开▼
