基于广义回归神经网络的非侵入式负荷识别方法

非侵入式负荷识别可以实现电网和用户的灵活双向互动,对智能电网的发展具有重大意义,而神经网络因其自学习能力及计算复杂度低等优点越来越多地应用在非侵入式负荷识别中。针对现有BP神经网络方法容易陷入局部最优、收敛速度慢的问题,文章提出了一种基于广义回归神经网络(GRNN)的非侵入式负荷识别方法。该方法使用负荷投切过程的功率、谐波、投切时间等暂态特征作为输入,应用Parzen非参数估计方法搭建网络结构,利用模拟退火算法的全局搜索能力对光滑因子进行寻优,从而建立GRNN网络模型进行负荷识别。实验结果表明,相对于BP神经网络,文中方法具有更好的识别精度和训练速度。

Non-intrusive load identification method based on general regression neural network

Non-intrusive load identification can achieve the flexible bilateral interaction between power grid and users, which is of great significant in the development of smart grid. Neural network was frequently employed in non-intrusive load identification because of its self-learning ability and low computation complexity. In order to overcome the shortcomings that BP neural network traps into local optima easily and has a low convergence speed, this paper proposes a new method based on General Regression Neural Network (GRNN). Firstly, this method uses transient features such as power, harmonic and switch time as the inputs of GRNN. Secondly, neural network structure is constructed based on Parzen non-parametric estimation method. Thirdly, simulated annealing algorithm is adopted to get the best smoothing parameter. Finally, RGNN network model is built to identify the load. Experimental results have proved that the proposed method has higher identification accuracy and training speed than BP neural network.