基于模糊神经网络的单相重合闸故障识别

Fault Recognition of Single-phase Reclosing Based on Fuzzy Neural Network

自动重合闸是提高电力系统供电可靠性、保证电力输电线路安全运行的重要装置,广泛应用于供电输送线中。针对自动重合闸电压判据可能出现误判的情况,文章提出了一种将模糊神经网络应用于单相自动重合闸故障识别的方法,构造了一个具有2个输入、1个输出的模糊神经网络模型,用于识别瞬时性故障与永久性故障,并采用从样本中获取模糊规则的方法和利用Matlab软件对该方法进行仿真实验。仿真结果验证了该方法的可行性和准确性。

Auto-reclosing is a very important equipment that can improve reliability of power supply system and guarantee security operation of power line,so the auto-reclosing is widely applied in transmission line.In allussion to the misjudgment of voltage criterion of auto-reclosing,the paper put forward a method of fault recognition which applied fuzzy neural network（FNN） to recognize the faults of single phase auto-reclosing.It built up a model of fuzzy neural network with two-input and one-output,which was used to recognize transient faults and permanent faults.Using the method of gaining fuzzy rules from samples and Matlab software,it simulated the method.The simulation result verified the feasibility and accuracy of the proposed method.