【摘要】 非侵入式居民负荷识别技术的应用推动了家庭智能用电技术的发展,它能对居民用户的负荷组成成分进行深入分析,从而获取不同精细程度的用户用电信息,对于电力用户和电网公司都具有重要意义。负荷识别问题的数学模型较为复杂,导致数学优化算法在进行负荷识别时的求解效率较低。首先提出一种基于人工神经网络的非侵入式负荷识别模型,利用该模型训练好用于识别各类家用电器的神经网络并由此构成负荷特征识别库,用于判断家用电器的工作状态;然后通过仿真证明了所提方法的有效性;最后为了解决非侵入式居民负荷识别的应用问题,进一步探讨了非侵入式负荷识别的高级应用。更多还原

【Abstract】 The application of non-intrusive residential load identification technology promotes the development of household intelligent power technology. It can analyze the load components of residential users deeply, so as to obtain user’s electricity consumption information with different levels of precision, which is of great significance to both power users and power grid companies. The mathematical model of load identification problem is complex, which leads to the low efficiency of the mathematical optimization algorithm in load identification. Firstly, a non-intrusive load identification model based on artificial neural network was proposed. The model was used to train the neural network for identifying various kinds of household appliances and then to form a load feature recognition library for judging the working status of household appliances. Then the effectiveness of the non-intrusive load identification method was proved by simulation. Finally, in order to solve the application problem of non-intrusive residential load identification, this paper further discussed the non-intrusive load identification model.更多还原