Recognition of Power Loads Based on Deep Belief Network
Xu Chunhua1, Chen Kexu2, Ma Jian2, Liu Jiahan1, Wu Jianhua1
1. School of Information Engineering Nanchang University Nanchang 330031 China; 2. Institute of Electric Power Science State Grid Jiangxi Electric Power Co. Ltd Nanchang 330096 China
Abstract Aiming at the difficulty of manual feature selection in current automatic recognition of power loads (PLs) and to further heighten the identification accuracy, a deep belief network (DBN) based recognition method is proposed in this paper. DBN is a framework of deep neural network and has found a wide use in image recognition, speech recognition, power quality disturbances recognition, etc. A DBN consists of several restricted Boltzmann machines (RBMs) and one layer of back-propagation neural network. By using the contrastive divergence algorithm, the first RBM is fully trained with the training data to obtain initial features, then the next RBM is trained with the initial features as training data, etc. Finally, the whole DBN is fine-tuned in a manner of supervised training by back-propagation. Experimental results demonstrate that the proposed approach has a good performance on the recognition of eight types of PLs with an average accuracy over 98%.
Xu Chunhua,Chen Kexu,Ma Jian等. Recognition of Power Loads Based on Deep Belief Network[J]. Transactions of China Electrotechnical Society, 2019, 34(19): 4135-4142.
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2019, Vol. 34 
