基于深度学习SDAE-BP的暂降类型识别方法

Method of Sag Type Recognition Based on Deep Learning SDAE-BP

不同短路故障引起的暂降类型不同,对用户造成的影响也不相同,准确地识别暂降类型可针对实际的电压暂降情况进行分析、补偿和抑制,对于电压暂降的治理具有重要的意义,同时还可作为电力供应部门和用户之间协调纠纷的依据。考虑到实际系统发生短路故障时可能存在相位跳变,在原有文献短路故障引起的暂降分类基础上,推导了系统阻抗与线路阻抗的阻抗角不相等情况下短路故障引起的电压暂降类型的表达式并分析了其特征;为准确识别电压暂降类型,并避免人为特征提取过程中信息丢失的问题,提出了一种基于堆栈降噪自编码器-神经网络(Stacked denoised autoencoder-back propagation,SDAE-BP)的暂降类型识别方法,在输入信号中加入一定概率的噪声,再通过构建多层降噪自编码网络(Stacked denoised autoencoder,SDAE)逐层训练,以最小的误差实现信号的特征提取,并采用BP(Back propagation,BP)神经网络对暂降类型进行识别,通过Matlab仿真验证了上述传播特性及电压暂降类型识别方法的正确性。

The types of sags caused by different short-circuit faults are different,and the impact on users is different.Accurately identifying the types of sags can analyze,compensate and suppress the actual voltage sags,which is of great significance to the management of voltage sags.At the same time,it can also be used as a basis for the coordination of disputes between power supply departments and users.Considering that there may be a phase jump when a short-circuit fault occurs in the actual system,based on the classification of the sag caused by the short-circuit fault in the original literature,the type of the voltage sag caused by the short-circuit fault is derived when the impedance angle of the system impedance and the line impedance are not equal.The expression and its characteristics are analyzed.In order to accurately identify the type of voltage sag and avoid the problem of information loss in the process of artificial feature extraction,a sag type recognition method based on stacked denoised autoencoder-back propagation(SDAE-BP)is proposed.A certain probability of noise is added to the input signal,and then a multi-layer noise reduction self-encoding network is trained layer by layer by constructing to achieve signal feature extraction with the smallest error,and BP neural network is used to identify the type of sag.The correctness of the above-mentioned propagation characteristics and voltage sag type identification method is verified by Matlab simulation.