基于图信号交替优化的居民用户非侵入式负荷监测方法

Non-Intrusive Load Monitoring Method for Resident Users Based on Alternating Optimization in Graph Signal

非侵入式负荷监测(non-intrusive load monitoring,NILM)是研究居民用户负荷信息的常用方法,但存在分解准确度低、算法泛化性能低等系列问题。为此,该文应用图信号处理(graph signal processing,GSP)理论,提出一种基于图信号交替优化的居民用户NILM方法。该方法根据总负荷数据构建图信号模型,并基于图信号模型得到关于功率损耗的约束条件,较好地解决了传统方法缺乏负荷数据相关性研究的问题。相比于传统方法需要对模型参数多次调整,交替优化法可以自动调整参数,提高了实时监测能力,降低了电网运营成本。仿真结果表明,在1min采样率下,基于图信号交替优化法的总负荷分解准确度比NILM-GSP提高了15%,计算时间降低了10%,充分体现了该文算法性能的优越性。

Non-intrusive load monitoring(NILM) is a common method to research load information of resident users.However, it has some problems, such as low disaggregation accuracy and lacks of generalization and etc. Therefore, an NILM-alternating optimization(NILM-AO) method based on graph signal processing(GSP) theory was proposed. A graph signal model was constructed based on the total load data, and a power loss constraint can be obtained by the graph signal model to solve the lack of load data correlation research in traditional methods. Compared with the traditional method that requires altering model parameters, NILM-AO finds an optimal model parameter automatically, which improves the capability of real-time monitoring and decreases the operation cost of power grid. Simulation results show that NILM-AO improves the accuracy for 15%, and decreases the calculation time for 10% under 1-min sampling rate, which indicates the superiority of NILM-AO.