摘要
电力系统中负荷识别需要进行特征选择,而常见的人工方式特征选取比较困难。为解决负荷特征选择困难的问题,采用一种基于堆栈降噪自编码网络(SDAE)的电力负荷识别方法,能有效选取现场真实负荷波形的特征并加以识别,该方法是由一层后向传播的神经网络和多层降噪自编码器(DAE)构成。首先向数据信号中掺杂一定比例的噪声进行“破坏”,然后采用“破坏”后的信号重构原始信号,进而得到数据信号的波形特征,最后采用BP神经网络对整个数据处理网络进行有效监督和微调。经过现场实时采集的电力负荷波形数据验证,相较于BP神经网络算法,该方法的识别效果更佳。实验结果显示,采用SDAE方法在8类电力负荷的识别中辨析识别率超过96%。
In power system,load characteristics selection is needed for load identification,but it is difficult to select load characteristics by conventional manual method.In order to solve the problem,a load identification method based on SDAE is adopted,which can effectively select and identify the characteristics of real load waveform.SDAE is composed of a back propagation neural network and a multi-layer de-noising autoencoder(DAE).Firstly,it“destroys”the data signal by doping a certain proportion of noise,and then uses the“destroyed”signal to reconstruct the original signal,and then obtains the waveform characteristics of the data signal.Finally,BP neural network is used to effectively supervise and fine tune the whole data processing network.Verified by the real collected power load waveform data,this method is better than BP neural network.The experimental results show that the discrimination recognition rate of SDAE method is more than 96%.
作者
陈克绪
徐春华
刘玲
胡涛
CHEN Kexu;XU Chunhua;LIU Ling;HU Tao(Power Supply Service and Management Center,State Grid Jiangxi Electric Power Ltd.,Nanchang 330096,China;Nanchang Power Supply Branch of State Grid Jiangxi Electric Power Co.,Ltd.,Nanchang 330012,China)
出处
《中国测试》
CAS
北大核心
2022年第5期163-168,共6页
China Measurement & Test
基金
国网江西省电力有限公司科技项目资助(521820180014)。
