基于谐波发射水平的工业用户电力设备分类识别方法

随着电力物联网的快速发展，明确用户负荷状况对改善供电服务质量、电价决策、需求侧响应、有偿精准服务等方面起着重要作用。基于非侵入式方法进行工业电力设备的分类识别，存在工业负荷先验知识较少、用电采集系统数据采样频率低等问题。应用工业大用户的电能质量监测数据，提出一种基于谐波发射水平的工业用户电力设备分类识别方法。首先，提出基于双边累计和算法的工业设备投入/切除事件检测方法。其次，提取各次事件的典型特性，构建工业用户设备特征矩阵，提出高贡献率特征筛选方法，减少特征数量。提出基于k-means聚类算法和轮廓系数的工业用户电力设备分类识别方法。应用上海地区10 kV电压等级下某轧机用户14天的电能质量监测数据，验证了所提方法对于设备情况未知的工业用户设备分类具有较高的准确性，具有一定的实用价值和推广意义。

Classification and Identification Method of Power Equipment for Industrial Users Based on Harmonic Emission Level

With the rapid development of the power Internet of Things, clarification of user load status are significant for improving the quality of power supply service, power price decision-making, demand-side response, and paid precise services. The classification and identification of industrial power equipment based on non-intrusive methods is faced with the challenges of poor prior knowledge of industrial loads and low sampling frequency of the used data. Using the power quality monitoring data of large industrial users, this paper proposes a classification and identification method of power equipment for industrial users based on the level of harmonic emission. Firstly, a detection method is proposed for industrial equipment switching events based on bilateral cumulative sum algorithm. Secondly, the typical characteristics of each event is extracted to construct a feature matrix for industrial user equipment, and a feature screening method with high contribution rate is proposed to reduce the number of features. A classification and identification method of power equipment for industrial users based on k-means algorithm and silhouette coefficient is proposed. The 14-day power quality monitoring data of a rolling mill user under the 10 kV voltage level in Shanghai area was used to verify the proposed method. The result proves that the proposed method has high accuracy in classification of industrial user equipment with unknown equipment conditions, and has significant practical and promotion values.