摘要
针对当前配网故障类型人工判别工作量大且准确度不高的问题,提出了一种基于三遥信息处理和深度学习技术的配网故障类型识别方法.首先通过图像处理技术将遥测电流波形图转为电流数值组,提取遥信、遥控信号特征,共同组成特征向量,并对电流值进行归一化处理;然后以故障特征为基础,训练深度神经网络,实现故障类型识别;最后对隐含层层数及神经元数目进行调整,实现模型优化.实验结果表明,该方法可实现对配网故障类型的快速判断,准确率达到92.4%,具有实用价值.
The currently trip fault identification method for distribution networks is mainly based on manual discrimination,which causes the problem of larger workload and lower accuracy.By combining image processing and deep learning technology,automatic identification of distribution network fault types can be realized.First,the telemetry current waveform is converted to a current value group with time stamps by image processing technology.Then feature vectors are constructed with telecommunicating signal,telecontrol signal and normalized current value.Based on the fault type criterion,the deep neural network model is built and trained to realize fault type identification.Finally,the model is optimized by adjusting the number of hidden layers and neurons in the deep neural network.The experimental results show that the types of trip fault can be identified quickly with the proposed method,that and the accuracy is better than existing methods,which means the new method is practical and effective.
作者
杜炤鑫
谢海宁
宋杰
周德生
邹晓峰
陈冉
曾平
DU Zhaoxin;XIE Haining;SONG Jie;ZHOU Desheng;ZOU Xiaofeng;ZENG Ping(State Grid Shanghai Electric Power Research Institute, Shanghai 200437, China)
关键词
故障识别
图像处理
深度学习
故障判据
归一化
隐含层
fault identification
image processing
deep learning
fault type criterion
normalized processing
hidden layer
