基于K-means聚类与PSO特征优选KNN的分级负荷识别方法

针对非侵入式负荷辨识中，单一V-I轨迹特征无法对相似的轨迹特征进行有效识别以及所提取特征易出现冗余甚至噪声特征的问题，提出了一种基于K-means聚类与PSO特征优选的分级非侵入式负荷识别方法。首先，利用K-means算法对负荷V-I轨迹的HOG特征进行初步分类，将轨迹相似的电器分为一类；然后，对每一类中的电器电流数据进行多维特征提取并采用PSO算法选取最优特征子集；最后，利用KNN模型进行二级负荷识别。实验结果表明，该方法有效提高了负荷识别准确率；提取V-I轨迹的HOG特征解决了同一电器V-I轨迹波动的问题；对一级分类后的每一大类单独进行PSO特征优选KNN二级分类，解决了部分电器对特征子集适应性差的问题。所提方法在一定程度上解决了冗余特征甚至噪声特征对辨识准确率的影响，为负荷特征的选取提供了新的思路，对负荷辨识的实际应用具有重要的参考意义。

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

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.