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投稿时间：2022-05-03 修订日期：2022-10-25
投稿时间：2022-05-03 修订日期：2022-10-25
中文摘要: 随着电网中非线性负荷大量接入及电力电子化率的逐步提升，谐波问题日渐严重，开展电力系统谐波污染区域化治理，是一种有效解决思路。谐波污染分区的意义在于，同一分区内的谐波畸变主要由该分区内的谐波源导致，而受其他分区谐波源影响较小。为此，提出了一种抗时移聚类算法kDBA++。首先，考虑到电能质量监测数据具有高维度、含噪声等特点，采用分段聚合近似（picesise aggregate approximation，PAA）算法对数据进行压缩降噪预处理，降低后续计算复杂度。其次，采用kmeans++算法作为逻辑框架。考虑到非同步测量下数据间存在时移现象，难以直接利用kmeans++开展聚类，从而引入动态时间弯曲（dynamic time wraping，DTW）距离对算法进行优化。进而，鉴于DTW距离下聚类质心难以获取，因此采用DTW质心平均算法（DTW barycenter averaging，DBA）克服这一局限性，并最终得到所提kDBA++算法。采用IEEE123节点仿真系统及实际工程案例开展算法对比分析，结果显示所提kDBA++算法聚类精度优于现有算法，可准确进行谐波污染分区。此外，利用谐波污染分区转移阻抗矩阵及谐波贡献度对求得分区加以验证，分析结果表明，各谐波源对其所在分区内节点的谐波畸变影响较大，而对非同一分区节点的影响较小，从而论证了所提方法的实用性和有效性。
中文关键词: 谐波污染 监测数据时移特性 谐波污染分区 kDBA++聚类算法
Abstract:With the large number of nonlinear loads connected into the power grid and the gradual increase of the power electronic rate, the harmonic problem is becoming more and more serious. It is an effective solution to carry out the regional management of harmonic pollution in the power system. The significance of the harmonic pollution zoning is that the harmonic distortion in the same zone is mainly caused by the harmonic source in this zone, and is less affected by the harmonic sources in other zones. On this basis, an anti-time-shift clustering algorithm, i.e. kDBA++ was proposed. First, considering the characteristics of power quality monitoring data such as high dimensions and noise, the Picesise Aggregate Approximation (PAA) algorithm was used to compress and denoise the data to reduce the subsequent computational complexity. Secondly, the kmeans++ algorithm was used as the logical framework. Considering the time shift phenomenon between data under asynchronous measurement, it is difficult to directly use kmeans++ to carry out clustering. To solve this issue, the dynamic time warping (DTW) distance was introduced to optimize the kmeans++ algorithm. Furthermore, to overcome the limitations that the clustering centroids are difficult to obtain under the DTW distance, the DTW Barycenter Averaging algorithm (DBA) was integrated into kmeans++, and finally the proposed kDBA++ algorithm was obtained. The IEEE123 node simulation system and actual engineering cases were used to carry out algorithm comparison research. The analysis results show that the proposed kDBA++ algorithm has better clustering accuracy than the existing algorithms, and can accurately carry out harmonic pollution zoning. In addition, the transfer impedance matrix of the harmonic pollution zoning and the harmonic contribution degree were used to verify the correctness of the obtained zones. The analysis results indicate that each harmonic source has a great influence on the harmonic distortion of the nodes in the zone where it is located, while it has little influence on the nodes in different zones. Thus, the practicability and effectiveness of the proposed method are verified.
keywords: harmonic pollution time-shift characteristics of monitoring data harmonic pollution zoning kDBA++ clustering algorithm
文章编号：202200405 中图分类号：TM711 文献标志码：
基金项目:国家自然科学基金项目（52177104）；南方电网公司科技项目（090000KK52190169；SZKJXM2019669）
作者简介:第一作者：王杨（1990—），男，研究员，博士. 研究方向：电能质量；新能源并网；电力系统广域监测与控制. E-mail：scufrankwang@163.com;通信作者：赵劲帅, E-mail：2510227837@qq.com
引用文本：
王杨,唐文楚,赵劲帅,汪清,张华赢,肖先勇,晁苗苗.基于kDBA++聚类算法的谐波污染分区策略[J].工程科学与技术,2023,55(2):84-96.
WANG Yang,TANG Wenchu,ZHAO Jinshuai,WANG Qing,ZHANG Huaying,XIAO Xianyong,CHAO Miaomiao.Harmonic Pollution Partition Method Based on kDBA++ Clustering Algorithm[J].Advanced Engineering Sciences,2023,55(2):84-96.
引用文本：
王杨,唐文楚,赵劲帅,汪清,张华赢,肖先勇,晁苗苗.基于kDBA++聚类算法的谐波污染分区策略[J].工程科学与技术,2023,55(2):84-96.
WANG Yang,TANG Wenchu,ZHAO Jinshuai,WANG Qing,ZHANG Huaying,XIAO Xianyong,CHAO Miaomiao.Harmonic Pollution Partition Method Based on kDBA++ Clustering Algorithm[J].Advanced Engineering Sciences,2023,55(2):84-96.