| 引用本文: | 徐崇钧,于鹤洋,朱 琪,等.基于多元特征分析的居民非侵入式相似电器辨识算法[J].电力系统保护与控制,2023,51(13):111-121. |
| XU Chongjun,YU Heyang,ZHU Qi,et al.Non-intrusive identification algorithm of residents' similar electrical appliancesbased on multivariate feature analysis[J].Power System Protection and Control,2023,51(13):111-121 |
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| 摘要: |
| 在面向居民的非侵入式负荷辨识场景中，存在部分电路结构、功率相近的相似电器。对于这些电器，现有算法辨识成功率较低。为提高对居民相似电器辨识的准确率，提出了一种基于多元特征分析的非侵入式相似电器辨识算法。该算法使用一对多维的低频电器特征数据进行分析，先将特征规范化，计算两种电器特征间马哈拉诺比斯距离，用以判断两种电器是否相似，再对原始特征使用主成分分析，以提取相似电器的主特征，最后将主特征输入多元高斯模型，得到辨识结果，判断电器运行状态，并分项计量电器能耗。使用实测电器数据与居民实际用电数据进行验证，并与其他模型进行对比。结果显示，该算法可有效提高相似电器辨识的准确性。 |
| 关键词: 非侵入式负荷监测 马哈拉诺比斯距离 相似电器 主成分分析 多元高斯模型 |
| DOI：10.19783/j.cnki.pspc.221513 |
| 分类号: |
| 基金项目:浙江省自然科学基金项目资助“浙江省公益技术应用研究项目”(LGG21E070003)；中央高校基本科研业务费专项资金项目资助“浙江大学基本科研业务费专项”(2022QZJH19) |
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| Non-intrusive identification algorithm of residents' similar electrical appliancesbased on multivariate feature analysis |
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XU Chongjun, YU Heyang, ZHU Qi, GENG Guangchao, JIANG Quanyuan
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College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
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| Abstract: |
| In the context of non-intrusive load monitoring for residential users, there exist similar appliances with comparable power consumption and internal circuitry. However, the existing algorithms have a low success rate in identifying these appliances. In order to improve the accuracy, this paper proposes a non-intrusive appliance identification algorithm based on multivariate feature analysis. The method uses a pair of multidimensional low-frequency appliance feature data for analysis, which are first standardized, and then the Mahalanobis distance between the two appliance features is calculated to determine their similarity. The original features are then subjected to principal component analysis to extract the main features of this pair of similar appliances. These features are then input into a multivariate Gaussian model to obtain the identification results. This can judge the operating status of the appliance and measure its energy consumption. The proposed algorithm is validated using actual appliance data and residential electricity consumption data, and compared with other models. The results demonstrate that the algorithm can effectively improve the accuracy of identifying similar appliances. |
| Key words: non-intrusive load monitoring Mahalanobis distance similar appliance principal components analysis (PCA) multivariate Gauss |
