用于典型载荷特征提取的自适应事件检测
Adaptive Event Detection for Representative Load Signature Extraction
摘要：
事件检测是基于事件的非侵入式负载监控（NILM）的第一步，它可以提供有用的瞬态信息来识别设备。然而，现有的事件检测方法与固定的参数可能会失败的情况下，不可预测的和复杂的住宅负荷变化，如高波动，长过渡，和近同步。本文提出了一种动态的时间窗口的方法来处理这些高度复杂的负载变化。具体地，提出了具有自适应裕度的窗口、多时间尺度窗口筛选和自适应阈值（WAMMA）方法，以高采样率（> 1Hz）检测聚集的家电负载数据中的事件。该方法通过自适应调整窗口宽度、容限宽度和变化阈值等参数，准确捕捉瞬态过程。此外，有代表性的瞬态和稳态负载特征提取，并首次从检测到的事件分割的瞬态和稳态期间进行量化。对20Hz数据集、50Hz LIFTED数据集和60Hz BLUED数据集的案例研究表明，该方法可以稳健地优于其他最先进的事件检测方法。本文还表明，提取的负载签名可以提高NILM的准确性，并有助于开发其他应用程序，如负载重建，以产生现实的负载数据NILM研究。
英文摘要：
Event detection is the first step in event-based non-intrusive load monitoring (NILM) and it can provide useful transient information to identify appliances. However, existing event detection methods with fixed parameters may fail in case of unpredictable and complicated residential load changes such as high fluctuation, long transition, and near simultaneity. This paper proposes a dynamic time-window approach to deal with these highly complex load variations. Specifically, a window with adaptive margins, multi-timescale window screening, and adaptive threshold (WAMMA) method is proposed to detect events in aggregated home appliance load data with high sampling rate (>1Hz). The proposed method accurately captures the transient process by adaptively tuning parameters including window width, margin width, and change threshold. Furthermore, representative transient and steady-state load signatures are extracted and, for the first time, quantified from transient and steady periods segmented by detected events. Case studies on a 20Hz dataset, the 50Hz LIFTED dataset, and the 60Hz BLUED dataset show that the proposed method can robustly outperform other state-of-art event detection methods. This paper also shows that the extracted load signatures can improve NILM accuracy and help develop other applications such as load reconstruction to generate realistic load data for NILM research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
