| 引用本文: | 赵晶晶,贾 然,陈凌汉,等.基于深度学习和改进K-means聚类算法的电网
无功电压快速分区研究[J].电力系统保护与控制,2021,49(14):89-95. |
| ZHAO Jingjing,JIA Ran,CHEN Linghan,et al.Research on fast partition of reactive power and voltage based on deep learning and an improved K-means clustering algorithm[J].Power System Protection and Control,2021,49(14):89-95 |
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| 摘要: |
| 基于深度学习和改进K-means聚类算法的电网
无功电压快速分区研究 |
| 关键词: 电耦合强度 稀疏自编码器 改进K-means聚类算法 电网分区 电气模块度 |
| DOI：DOI: 10.19783/j.cnki.pspc.201124 |
| 分类号: |
| 基金项目:国家重点研发计划项目资助(2018YFB0905105) |
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| Research on fast partition of reactive power and voltage based on deep learning and an improved K-means clustering algorithm |
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ZHAO Jingjing, JIA Ran, CHEN Linghan, ZHU Tiantian
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(School of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China)
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| Abstract: |
| With the continuous expansion of the power grid, it has become more and more difficult to perform unified voltage regulation on the entire grid. This paper proposes a fast reactive power and voltage partition method based on deep learning and an improved K-means clustering algorithm. First, the electrical coupling strength matrix is established to reflect the strength of the electrical coupling relationship between the nodes of the system. Then the sparse autoencoder in deep learning is used to realize feature extraction and dimensionality reduction of the input high-dimensional matrix through training. Finally, the improved K-means clustering algorithm is used to perform cluster analysis on the feature sequence after dimensionality reduction, and the final partition is determined by checking the electrical modularity value. The quality of power grid divisions is evaluated with two evaluation indicators: electrical modularity and reactive power reserve verification. The simulation analysis of IEEE39 and IEEE118 bus systems verifies that the proposed method has high electrical modularity on the basis of ensuring connectivity and sufficient reactive power reserve.
This work is supported by the National Key Research and Development Program of China (No. 2018YFB0905105). |
| Key words: electrical coupling strength sparse autoencoder improved K-means clustering algorithm division of reactive power and voltage electrical modularity |
