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Advanced Hybrid Information Processing. Third EAI International Conference, ADHIP 2019, Nanjing, China, September 21–22, 2019, Proceedings, Part II

Research Article

Optimal Method of Load Signal Control of Power Based on State Difference Clustering

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  • @INPROCEEDINGS{10.1007/978-3-030-36405-2_7,
        author={Yan Zhao and Pengfei Lang},
        title={Optimal Method of Load Signal Control of Power Based on State Difference Clustering},
        proceedings={Advanced Hybrid Information Processing. Third EAI International Conference, ADHIP 2019, Nanjing, China, September 21--22, 2019, Proceedings, Part II},
        proceedings_a={ADHIP PART 2},
        year={2019},
        month={11},
        keywords={Power grid Load State difference clustering Intelligent control},
        doi={10.1007/978-3-030-36405-2_7}
    }
    
  • Yan Zhao
    Pengfei Lang
    Year: 2019
    Optimal Method of Load Signal Control of Power Based on State Difference Clustering
    ADHIP PART 2
    Springer
    DOI: 10.1007/978-3-030-36405-2_7
Yan Zhao1,*, Pengfei Lang2
  • 1: School of Power Engineering, Nanjing Institute of Technology
  • 2: China Academy of Launch Vehicle Technology
*Contact email: langpf1988@163.com

Abstract

In order to improve the power grid load detection ability, an optimal method of load signal control of power based on state difference clustering is proposed, and the big data statistical analysis model of the power grid load is constructed. The clustering analysis and state mining of grid load are carried out by using the distributed detection method of association features, and the regression analysis model of grid load state difference is constructed to realize the state differential clustering of power grid load signal in high-dimensional phase space. Based on the classification and fusion of the extracted characteristic sets of grid load, big data analysis method is used to optimize the intelligent control of power grid load signal. The simulation results show that the proposed method has better accurate classification performance and lower misdivision rate, which improves the output stability of power grid load.

Keywords
Power grid Load State difference clustering Intelligent control
Published
2019-11-29
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-36405-2_7
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