Non-Intrusive Load Decomposition Method Based on the Factor Hidden Markov Model

Liu Song, Wu Yao, Tian Jie
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引用次数: 1

Abstract

Aiming at the problems of demanding high frequency sampling devices and the factor hidden markov model algorithm without considering the correlation between the power equipments' running states, a novel non-intrusive load monitoring and decomposition (NILMD) method which based on factor hidden markov mode is proposed in this paper. This method considers appliances' current as load feature, and establishes a mathematical model between the total current and the currents of each appliance that considers the correlation between the running states of the equipment. Then, factor hidden markov algorithm is applied to search the running states of each appliance that realizes the decomposition of power load. The experiment shows that the resolution precision of each electric equipment is improved, and the principle of the method is simple. Meanwhile, the load data required in this method can be obtained directly by the universal smart meters on the market which reduces the cost input of the hardware.
基于因子隐马尔可夫模型的非侵入式负荷分解方法
针对电力设备对高频采样设备要求高,且未考虑设备运行状态相关性的因素隐马尔可夫模型算法存在的问题,提出了一种基于因素隐马尔可夫模型的非侵入式负荷监测与分解(NILMD)方法。该方法将电器的电流作为负载特征,建立了考虑设备运行状态相关性的电器总电流与各电器电流之间的数学模型。然后,利用因子隐马尔可夫算法搜索各设备的运行状态,实现电力负荷的分解。实验表明,该方法提高了各电气设备的分辨率精度,且原理简单。同时,该方法所需的负荷数据可由市场上的通用智能电表直接获取,减少了硬件的成本投入。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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