欠定分离机制下基于特征滤波的居民负荷非侵入辨识算法

摘要

The load decomposition is realized by the underdetermined solution of the current signal in non-intrusive acquisition mode.The complete current of each independent load is obtained,and state identification is realized on the basis of load decomposition.An optimization model is developed for the underdetermined issues difficult to solve by utilizing the load operation habit of residential users,thus transforming them into one-dimensional underdetermined issues,and the solving model is thus established just by separating two lines of signals from the signals collected in a unitary time interval.According to the sparsity of current frequency domain signals,the two-step iterative shrinkage threshold algorithm is used to get the optimal solution,so that each load put into operation can be separately decomposed.A priori approach is used to obtain the characteristic filter set for the characteristic current of various loads on the power network,enabling frequency domain filtering of the decomposed current,and the load identification is thus achieved by quantitatively determining the frequency components after filtering.The power consumption data acquired in the real world is used to prove that the algorithm is able to effectively achieve the load decomposition,and accurately recognize the status of loads.%通过非侵入采集模式下电流信号的欠定求解实现了负荷分解,获取了各独立负荷的完整电流,在负荷分解基础上实现了状态辨识.利用居民用户的负荷操作习惯将难以求解的欠定问题优化建模,转化为一维欠定问题,将求解模型建立为单位时间间隔仅从采集信号中分离两路信号.依据电流频域信号的稀疏性通过两步迭代收缩阈值算法得到最优解,使每个投入运行的负荷均可独立分解.通过先验方式获取用电网络各负荷的特征电流形成特征滤波器组,对分解电流进行频域滤波,通过对滤波后频率分量的量化判决实现负荷辨识.利用实际采集的用电数据验证了算法的有效性,能够有效实现负荷分解,并准确判断负荷状态.