基于深度前馈网络的电能质量复合扰动识别

针对电能质量复合扰动识别中识别准确率不高和泛化性能较差的问题,提出基于深度前馈网络(Deep Feedforward Network,DFN)的扰动识别方法。先在少数重要频率点上对扰动信号作不完全S变换,从得到的时频矩阵中提取多种识别特征,构建和训练三层DFN扰动分类器,并使用Dropout正则化来提高分类器的泛化性能。仿真实验和实测实验表明,文中的方法能够有效识别8种复合扰动在内的共17种扰动类型,并具有很好的抗噪性能和泛化性能。与CART决策树、极限学习机、随机森林等现有方法相比,方法识别准确率更高,鲁棒性更好,具有良好的应用前景。

Recognition of Complex Power Quality Disturbances Based on Deep Feedforward Network

In this paper, a new deep feedforward network (DFN)-based method for complex power quality disturbances recognition is proposed, aiming at solving the problem of low recognition accuracy and poor generalization performance. Firstly, original disturbance signals are processed by incomplete S-transform at several important frequency samples. Then,some distinctive features are extracted from the result of incomplete S-transform. Finally, a three-layer DFN classifier is constructed and trained, with Dropout regularization to improve the generalization and noise immunity. The simulation and experiment results show that the proposed method can effectively identify 17 types of disturbances, including 8 types of complex disturbances. The results in different noise levels indicate that the method also has commendable anti-noise and generalization performance. Compared with existing methods such as CART decision tree, extreme learning machine and random forest, the proposed method has higher recognition accuracy, better robustness and good application prospects.