中文摘要：

针对逆传播神经网络（BP-NN）运算过程中易陷入局部极小值的不足。根据典型的经验公式对比,缩小了隐含层节点数范围,从而寻找最优的隐含层节点数。根据萤火虫优化（GSO）算法的特点,用GSO-BP-NN训练的初始权值阈值,能够很好地预测测试集,从而避免BP神经网络陷入局部极小值。采用以上方法的结合对电网进行故障诊断,实验证明：该方法可以准确有效地诊断出电网故障位置。

英文摘要：

Narrowing the scope of hidden nodes based on empirical formula to find the optimal number of hidden nodes in the small range is described, aiming at problems of easy trapping into smallest spot. According to the glowworm swarm optimized （ GSO ） features, using GSO optimize training initial weights and thresholds of back propagation neural network（BP-NN） can predict test set well, so as to avoid the problem of BP-NN falling into local minima. Using a combination of the two methods for grid fault diagnosis, experimental results show that the method can accurately and efficiently diagnose network fault location.