基于TensorFlow框架的有源配电网深度学习故障定位方法

随着大规模分布式电源(DG)接入配电网,配电网的结构由传统的辐射型变为多端电源结构,传统的故障定位方法不再完全满足含DG的配电网系统,对此提出一种基于深度学习的有源配电网故障定位方法。首先通过馈线监控终端采集过电流故障数据与节点电压数据,结合各电源出力数据,形成故障数据向量;然后使用Tensorflow构建基于全连接网络的深度神经网络模型,挖掘故障数据向量与故障支路之间的映射联系,形成故障定位模型;最后利用该模型在线定位故障并验证其有效性。模型测试结果表示,与反向传播神经网络、学习向量量化神经网络模型相比,深度学习模型收敛速度更快,故障定位准确率更高,同时在数据畸变或缺失时,模型具有较高的容错性。

A fault location method for active distribution network based on Tensorflow deep learning

With the high penetration of distributed generators, the radial structure of conventional distribution network system will change to a multi-terminal type, the traditional fault location method will be invalid. In this paper, a fault location method based on deep learning for active distribution network is proposed. This method firstly collects the current and voltage data through the feeder terminal unit. Combining the power output data, a fault data vector is formed; secondly, it uses tensorflow framework to build a deep neural network model based on fully connected network to mine the mapping relations between fault data vectors and fault sections and form the final fault location model through training. Finally, the fault location results demonstrate the effectiveness of the proposed method. Case studies show that compared with the traditional BP and learning vector quantification neural network model, the deep learning model has faster convergence speed and higher fault location accuracy. The final model has high fault tolerance to information distortion and loss.