| 引用本文: | 丁明,黄冯,邹佳芯,刘金山,宋晓皖.改进谱聚类与遗传算法相结合的电力时序曲线聚类方法[J].电力自动化设备,2019,39(2): |
| DING Ming,HUANG Feng,ZOU Jiaxin,LIU Jinshan,SONG Xiaowan.Power time series curve clustering method combining improved spectral clustering and genetic algorithm[J].Electric Power Automation Equipment,2019,39(2): |
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| 摘要: |
| 为改善传统聚类算法在电力时序数据上的聚类效果，提出一种基于优化特征向量选取的遗传谱聚类算法。针对应用数据结构特点，合理优化谱聚类算法中特征向量的提取过程，避免传统方法可能造成的数据信息缺失问题；采用遗传聚类优化算法对优选后的特征向量进行聚类划分，并将最终划分结果映射回原始数据。以UCI标准合成时间序列数据与美国区域电网运营商PJM提供的日负荷数据为例，对比分析现有常用聚类算法与所提算法测试结果的聚类有效性指标与形态特征。研究结果表明，所提算法分类效果显著，有较高的聚类质量和算法稳健性，具有工程应用前景。 |
| 关键词: 时序数据 谱聚类 遗传算法 特征向量提取 负荷聚类 |
| DOI：10.16081/j.issn.1006-6047.2019.02.014 |
| 分类号:TM714 |
| 基金项目:国家电网公司科技项目(5228001600DX) |
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| Power time series curve clustering method combining improved spectral clustering and genetic algorithm |
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DING Ming1, HUANG Feng1, ZOU Jiaxin2, LIU Jinshan2, SONG Xiaowan1
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1.Anhui Key Laboratory of New Energy Utilization and Energy Conservation, Hefei University of Technology, Hefei 230009, China;2.Key Laboratory of Photovoltaic Power Generation and Grid Integration, State Grid Qinghai Electric Power Research Institute, Xining 810008, China
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| Abstract: |
| To improve the clustering effect of traditional clustering algorithms on power time series data, a genetic spectrum clustering algorithm using selected optimal feature vectors is proposed. According to the characteristics of the application data structure, the extraction process of the feature vectors in the spectral clustering algorithm is optimized to avoid possible lack of data information suffered from traditional approaches. Then, the genetic clustering optimization algorithm is used to cluster the optimized feature vectors, and map the final division results back to the original data. The UCI standard synthetic time series data and the daily load data provided by the USA regional power grid operator PJM are employed as an example. Test results obtained from the traditional clustering algorithm and the proposed algorithm are compared and analyzed in terms of cluster validity indicators and morphological features. These results indicate that the proposed algorithm has remarkable classification effect, high clustering quality and robustness, which exhibits promising engineering applications. |
| Key words: time series data spectral clustering genetic algorithm feature vector extraction load clustering |
