基于DTW的多状态负荷的非侵入式负荷分解

Non-intrusive load decomposition of multi-state load based on DTW

中文关键词: 多状态负荷  非侵入式负荷监测  动态时间规整  稳态特征  滑动时间窗

英文关键词: multi-state load  NILM  DTW  steady state characteristics  sliding time window

中文摘要:

      非侵入式负荷监测（NILM）是大数据和人工智能的重要应用领域，能够显著提升电网的智能化水平和节能效果。长期以来在NILM中采用稳态特征进行负荷分解时，优点是可识别功率近似的负荷，但是不能处理多状态负荷。为此，采用滑动时间窗作为事件探测算法，提出一种基于动态时间规整（DTW）的多状态特征的NILM模型。该模型首先对多状态负荷进行特征提取，并建立多状态特征的稳态波形模板库；然后利用滑动时间窗算法提取待分解负荷的稳态波形特征，将提取的稳态波形运用DTW算法与稳态波形模板库中的负荷特征计算最小距离进行辨识。该方法能够显著提升稳态条件下多状态负荷的辨识效果。最后采用公共数据集REDD进行测试验证，证明了所提方法的有效性。

英文摘要:

      Nonintrusive load monitoring（NILM）is an important application area of big data and artificial intelligence, which can significantly improve the intelligence level and energy-saving effect of the power grid. For a long time in NILM, the advantage when using steady-state characteristics for load decomposition is that it can identify loads with similar power, but it cannot handle multi-state loads. To this end, a sliding time window is used as an event detection algorithm, and a NILM model based on dynamic time warping（DTW）with multi-state features is proposed. Firstly,the model extracts the characteristics of the multi-state load, and establishes a steady-state waveform template library of the multistate characteristics. Then, it uses the sliding time window algorithm to extract the steady- state waveform characteristics of the load to be decomposed, and applies the DTW algorithm to the steady-state waveforms. The load characteristics in the state waveform template library calculate the minimum distance for identification. The proposed load decomposition model can significantly improve the decomposition ability of multi-state loads under steadystate characteristics. Lastly, the test based on the REDD dataset verifies the effectiveness of the method.