| 引用本文: | 许良财,邵振国,陈飞雄.基于haar小波编码和改进K-medoids算法聚合的用户负荷典型区间场景挖掘[J].电力自动化设备,2022,42(6): |
| XU Liangcai,SHAO Zhenguo,CHEN Feixiong.Typical interval scene mining of consumer load based on haar wavelet coding and improved K-medoids algorithm aggregation[J].Electric Power Automation Equipment,2022,42(6): |
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| 摘要: |
| 针对单一典型曲线无法满足负荷不确定性分析需求的问题，提出一种基于haar小波编码和改进K-medoids算法聚合的用户负荷典型区间场景挖掘方法。将原始负荷曲线经haar小波变换得到低维负荷近似序列；对负荷近似序列每个维度的特征集分别进行聚类，提取类簇所包含特征的边界值，得到数值区间并进行编码；根据特征占比剔除非显著数值区间，并组合不同维度的显著数值区间得到字符串表征的负荷区间序列；定义字符串差异度衡量负荷区间序列的相似性，利用改进K-medoids算法聚合得到负荷区间序列类簇，并提取类簇所包含的负荷近似序列的边界值以得到典型区间场景；设置差异度阈值实现典型区间场景的粒度调节。使用爱尔兰地区某用户实测负荷数据进行验证，实验结果表明所提方法可以实现不同粒度负荷典型区间场景的挖掘。 |
| 关键词: 典型区间场景 负荷近似序列 haar小波变换 区间编码 K-medoids算法 |
| DOI：10.16081/j.epae.202204005 |
| 分类号:TM714 |
| 基金项目:国家自然科学基金资助项目(51777035)；福建省自然科学基金重点资助项目(2020J02028)；福州市科技平台创新项目(2020-PT-143) |
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| Typical interval scene mining of consumer load based on haar wavelet coding and improved K-medoids algorithm aggregation |
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XU Liangcai1,2, SHAO Zhenguo1,2, CHEN Feixiong1,2
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1.College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China;2.Fujian Smart Electrical Engineering Technology Research Center, Fuzhou 350108, China
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| Abstract: |
| Aiming at the problem that single typical curve cannot satisfy the demand of load uncertainty analysis, a typical interval scene mining method for consumer load based on haar wavelet coding and improved K-medoids algorithm aggregation is proposed. The low dimensional load approximate sequences are obtained by haar wavelet transform of the original load curve. The feature set of load approximate sequence in each dimension is clustered separately, the boundary values of the features contained in the clusters are extracted, and numerical intervals are obtained and coded. The non-significant numerical intervals are eliminated according to the feature proportion, and the significant numerical intervals with different dimensions are combined to obtain the load interval sequences represented by strings. The string difference is defined to measure the similarity of load interval sequences, the improved K-medoids algorithm aggregation is used to obtain the clusters of load interval sequences, and the boundary values of load approximate sequences in the clusters are extracted to obtain the typical interval scenes. The difference threshold is set to realize granularity adjustment of typical interval scenes. The measured load data of a user in Ireland is used for verification, and the experimental results show that the proposed method can realize mining of typical load interval scenes with different granularities. |
| Key words: typical interval scene load approximate sequence haar wavelet transform interval coding K-medoids algorithm |
