Hierarchical load identification method based on K-means clustering and PSO feature optimization KNN
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    Abstract:

    In order to solve the problem that a single V-I track feature can not effectively identify similar track features and the extracted features are prone to redundacy or even noise features in non-invasive load identification,a hierarchical non-invasive load identification method based on K-means clustering and PSO feature optimization was proposed.Firstly,K-means algorithm was used to initially classify the HOG features of load V-I trajectories,and the appliances with similar trajectories were classified into one category.Then,multi-dimensional features are extracted from electrical current data in each category and the optimal feature subset is selected by PSO algorithm.Finally,KNN model was used for secondary load identification.The experimental results show that this method effectively improves the accuracy of load identification.Extracting the HOG feature of V-I trajectory solves the problem of fluctuation of the same electrical appliance.PSO feature optimized KNN secondary classification is carried out for each category after the first level classification,which solves the problem of poor adaptability of some electrical appliances to feature subset.The proposed method solves the influence of redundant features and even noise features on the identification accuracy to a certain extent,and provides a new idea for the selection of load features,which has important reference significance for the practical application of load identification.

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AN Qi, LIANG Yufei, WANG Yaoqiang, WANG Zhanbin, LI Zheng, LI Zheng, AN Guoqing. Hierarchical load identification method based on K-means clustering and PSO feature optimization KNN[J]. Journal of Hebei University of Science and Technology,2022,43(3):249-258

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History
  • Received:January 11,2022
  • Revised:March 01,2022
  • Adopted:
  • Online: July 08,2022
  • Published: