非侵入式负荷动态识别方法的研究及工程应用

Research and Engineering Application of Non-intrusive Load Dynamic Identification Method

随着非侵入式负荷监测技术应用场景不断扩展,负荷类型日趋多样化,基于单层特征的静态识别方法需要构造更加全面、复杂的特征,难以兼顾负荷识别的准确度及速度。提出一种基于多层特征组的动态识别方法,综合考虑不同负荷特征提取的采样频率、监测窗口宽度、计算复杂度和负荷特征存储量等构建分层特征组,针对负荷群中不同的负荷类型提取不同的特征组作为分类特征以降低特征的综合提取代价,最后基于支持向量机多分类算法实现按需识别负荷类型。BLUED数据库的仿真对比分析和实际某工厂的工程测试结果表明,基于多层特征组的动态识别方法不仅能够提高负荷的综合识别速度,也能提升相似负荷的识别准确度,在负荷相似及投切频繁的场景下能够兼顾负荷识别准确度和速度,具有较好的工程适用性。

As the application scenarios of non-intrusive load monitoring technology continue to expand and load types become increasingly diversified,static identification methods based on single-layer features need to construct more comprehensive and complex features,and it is difficult to give consideration to both the accuracy and speed of load identification.A dynamic identification method based on multi-layer feature groups is proposed,which takes into account the sampling frequency,monitoring window width,computational complexity and load characteristic storage of different load features to construct a hierarchical feature group,extracts different feature groups for different load types in the load group as classification features to reduce the comprehensive extraction cost of features,and finally realizes on-demand load type recognition based on support vector machine multi-classification algorithm.Results from BLUED database simulation and actual engineering test of a factory show that the dynamic identification method based on multi-layer feature groups can increase the identification speed of most loads,while accurately distinguish among similar loads.It can give consideration to the accuracy and speed of load identification under the scenario of similar load and frequent switching,and has good engineering applicability.