EN基于改进ISODATA算法的变电站负荷特性聚类
Clustering of substation load characteristics based on improved ISODATA algorithm
- 中国电力 2026年59卷第3期 页码：125-133
- 作者机构：
1. 国网冀北电力有限公司唐山供电公司,河北,唐山,063000
2. 输变电装备技术全国重点实验室（重庆大学）,重庆,400044
- 作者简介：
- 基金信息：国家自然科学基金资助项目（52277081）。
DOI：10.11930/j.issn.1004-9649.202505079
中图分类号：-
网络首发：2026-03-16，
纸质出版：2026-03-16
- 稿件说明：
移动端阅览
蒋达飞, 艾洪克, 孟巧, 董彪, 翁一帆, 张谦. 基于改进ISODATA算法的变电站负荷特性聚类[J]. 中国电力, 2026, 59(3): 125-133.
JIANG Dafei, AI Hongke, MENG Qiao, et al. Clustering of substation load characteristics based on improved ISODATA algorithm[J]. 2026, 59(3): 125-133.
蒋达飞, 艾洪克, 孟巧, 董彪, 翁一帆, 张谦. 基于改进ISODATA算法的变电站负荷特性聚类[J]. 中国电力, 2026, 59(3): 125-133. DOI： 10.11930/j.issn.1004-9649.202505079.
JIANG Dafei, AI Hongke, MENG Qiao, et al. Clustering of substation load characteristics based on improved ISODATA algorithm[J]. 2026, 59(3): 125-133. DOI： 10.11930/j.issn.1004-9649.202505079.
摘要
新型电力系统高压配电网面临规模化、多元化负荷接入的挑战。变电站负荷聚类是精准识别用户用电规律、优化电网资源配置的核心手段，其分析结果可直接支撑电网规划、需求侧管理及新能源消纳策略制定。因此亟须通过变电站负荷曲线聚类分析，精准解析差异化负荷模式及其动态演化规律，为智能配电网运行决策提供数据支撑。针对迭代式自组织数据分析算法（iterative self organizing data analysis techniques algorithm，ISODATA）存在收敛速度慢和难以捕捉数据高维特征的局限，尤其是负荷数据的动态特性捕捉不足的问题，分别通过优化初始聚类中心选取策略与引入核函数映射机制，以提升算法对变电站负荷曲线高维特征的解析能力。在完成缺失值填补与数据标准化预处理后，本算法首先基于最大距离准则优化初始聚类中心选取，最大化初始中心间异质性以提升聚类稳定性；其次，引入核函数映射机制，映射负荷曲线至高维空间聚类，实现高维特征的显式解耦与聚类分析。仿真结果表明，在特征提取能力方面，改进算法生成的主成分分析（principal component analysis，PCA）特征空间中变电站四季负荷特征呈现显著差异性，能更好地获取负荷高维特征；在算法性能方面，改进算法使执行时间减少32.8%，聚类评价指标戴维斯-布尔丁指数（davies-bouldin index，DBI）降低了29.1%，邓恩指数（dunn index，DI）提高了42.9%，验证了所提算法的有效性和优越性。
Abstract
The new power system's high-voltage distribution grid faces challenges posed by the large-scale and diversified connection of loads. Substation load clustering is a core method for accurately identifying user electricity consumption patterns and optimizing grid resource allocation. Its analysis results can directly support grid planning
demand-side management
and the formulation of renewable energy integration strategies. Therefore
it is urgent to conduct substation load curve clustering analysis to precisely analyze differentiated load patterns and their dynamic evolution patterns
thereby providing data support for intelligent distribution grid operation decisions. Addressing the limitations of the iterative self-organizing data analysis techniques algorithm (ISODATA)
such as slow convergence speed and difficulty in capturing high-dimensional data features—particularly the insufficient capture of load data's dynamic characteristics—this study enhances the algorithm's ability to analyze high-dimensional features of substation load curves by optimizing the initial cluster center selection strategy and introducing a kernel function mapping mechanism. After completing missing value filling and data standardization preprocessing
this algorithm first optimizes the selection of initial clustering centers based on the maximum distance criterion to maximize the heterogeneity between initial centers and improve clustering stability. Second
it introduces a kernel function mapping mechanism to map load curves to high-dimensional space clustering
achieving explicit decoupling and clustering analysis of high-dimensional features. Simulation results indicate that in terms of feature extraction capability
the principal component analysis (PCA) feature space generated by the improved algorithm exhibits significant differences in the seasonal load characteristics of substations
enabling better capture of high-dimensional load features; In terms of algorithm performance
the improved algorithm reduces execution time by 32.8%
lowers the Davies-Bouldin Index (DBI) by 29.1%
and increases the Dunn Index (DI) by 42.9%
validating the effectiveness and superiority of the proposed algorithm.
关键词
Keywords
references
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