改进PSO-BP神经网络在变压器故障检测中的应用

粒子群优化（PSO）算法中的粒子根据全局最优粒子来移动自身位置进行搜索，但当某一粒子连续多次被选为全局最优粒子的时候，整个群体的粒子就会快速收敛于该最优粒子，陷入局部最优。为此，提出了变异动态粒子群优化（MDPSO）算法。采用惯性权重变异的思想，当某粒子连续被选为全局最优粒子时，就使一部分粒子的惯性权重以指数速度增长，使粒子跳出局部最小，继续全局寻优。并把改进的粒子群优化算法和BP神经网络相结合，应用于变压器故障检测中。实验表明，与常用的粒子群优化算法相比，用改进的粒子群优化算法优化BP神经网络具有更好的性能，在变压器故障检测中能够获得更高的检测精度。

Improved PSO-BP neural network for power transformer fault diagnosis

Particle Swarm Optimization(PSO)algorithm searches the best solution by making particles moving around the search space according to the global best particle.But when one particle is selected as the global best particle continuously,the other particles will converge at the global best particle repeatedly,which makes the particle swarm fall into local optimization.The authors presented Mutational Dynamic Particle Swarm Optimization(MDPSO)algorithm.A part of particles' inertia weight would mutate when one par...