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基于小波神经网络的大功率电器识别技术研究 被引量:2

Research on Identification Technology of High-Power Electrical Appliances Based on Wavelet Neural Network
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摘要 针对公寓用电中的大功率电器识别问题,提出利用小波神经网络对大功率电器进行识别。由于采集到的电网电流信号是基波信号和谐波信号的混合,因此需要进行信号分离。基于Mallat快速算法进行小波变换提取其中的谐波电流信号;将总电流的平均功率增量和谐波电流的平均功率增量经过归一化处理后作为大功率电器识别的特征向量,利用得到的特征向量对融合型小波神经网络进行基于BP算法的网络训练;利用训练好的小波神经网络对未知的电网电流数据进行识别,实现大功率电器的在线识别和预警。对比仿真实验表明:利用小波神经网络对大功率电器识别比传统的BP神经网络有更高的准确率。 In order to identify the high-power electrical appliances in apartment, a wavelet neural network is used to recognize high-power electrical appliances. Since the collected grid current signal is a mixture of the fundamental wave signal and the harmonic signal, the signal separation is required. The wavelet transform based on Mallat fast algorithm was used to extract harmonic current signal, and the average power increment of the total current and that of the harmonic current were normalized to be the feature vector for the recognition of the high-power electrical appliances, and the fusion type wavelet neural network was trained with the feature vectors based on BP algorithm. The trained wavelet neural network was used to identify the unknown data of the grid current, which could realize the online identification and early warning of high-power electrical appliances. The results of contrast simulation experiment show that the wavelet neural network has higher accuracy than the traditional BP neural network in the identification of high-power electrical appliances.
作者 史振江 SHI Zhen-jiang(Department of Electrical Engineering,Guangdong Polytechnic Institute,Guangzhou 510091,China)
出处 《测控技术》 CSCD 2018年第8期25-28,共4页 Measurement & Control Technology
关键词 小波神经网络 负荷识别 BP算法 谐波电流 wavelet neural network load identification BP algorithm harmonic current
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