The paper deals with a new way of determining a fault location in an electric power distribution radial feeder using an artificial neural network (ANN) and presents a new centralized protection method based on ANN. The aim of the devised algorithm is to detect the presence of a fault in a radial feeder and to disconnect faulted laterals from the distribution network. The protection schemes that are currently in use cannot leave the whole feeder energized by disconnecting faulted laterals only without even short de-energization of the healthy part of the feeder. A simulation model is also made for training the ANN and for testing the results. Current values for faulty and healthy states are generated by the simulation model and are used as training and testing data for the algorithm. The purpose of the algorithm is to make a decision which feeder circuit breakers should trip. The simulation model and the ANN are modeled by using MATLAB tools. The results show that the tripping of circuit breakers initiated by the algorithm is correct for all states.

本文提出了一种利用人工神经网络（ANN）确定配电径向馈线故障位置的新方法，并提出了一种基于神经网络的集中保护新方法。所设计算法的目的是检测径向馈线中是否存在故障，并从配电网断开故障分支。当前使用的保护方案只有在短时间内断掉馈线健康部分的情况下，才能通过断开有故障的侧面来使整个馈线通电。还建立了用于训练ANN和测试结果的仿真模型。故障和健康状态的当前值由仿真模型生成，并用作算法的训练和测试数据。该算法的目的是确定哪个馈电断路器应跳闸。使用MATLAB工具对仿真模型和ANN进行建模。结果表明，该算法引发的断路器跳闸对于所有状态都是正确的。