基于PSO-BP与组合矩的水电机组轴心轨迹识别

Research on the Identification of Axis Orbit in Hydro-generator Unit Base on PSO-BP and Combined Moment Invariants

引入了一种由Hu矩和仿射矩构成的组合不变矩作为水电机组轴心轨迹的反向传播(back propagation,简称BP)自识别神经网络的输入特征向量,在粒子群优化算法(particle swarm optimization,简称PSO)的基础上,融入粒子位置越界处理和全局最优位置未更新计数器,利用改进的粒子群算法求解BP网络连接权值,水电机组轴心轨迹的BP识别速度和精度得以显著提升,采用优化思想对初步识别结果进行量化分析,提取定量的轴心轨迹形状特征参数,可为水电机组故障定位提供指南。仿真实验和应用实例表明,组合不变矩的识别方法优于Hu矩或仿射矩方法,构建的PSO-BP具备较高的收敛速度和识别精度,所提出的轴心轨迹识别方法成功应用到了水电机组动不平衡故障诊断案例。

A new combined moment invariants is proposed to describe the features of a hydro-generator unit′s axis orbit based on Hu moment invariants and affine moment invariants and also act as the input characteristics of back propagation(BP)neural network.In order to overcome the problems of a slow convergence rate and a tendency to fall into a local minimum in the BP neural network algorithm,improved particle swarm optimization(PSO)is applied to train the weights of the BP neural network,so that the BP has enhanced convergence and precision.Quantitative feature parameters of the axis orbit shapes are solved with the optimization method to help operation personnel provide guidelines for the fault location of a hydro-generator unit.The experimental simulation and application results show that the axis orbit identification method with combined moments is better than that by the Hu moment invariants or affine moment invariants individually.The improved PSO-BP method has a faster rate of convergence and higher recognition precision than the classical BP during the identification of the axis orbit.The proposed method of axis orbit identification has been successfully applied to the imbalanced fault diagnosis of the hydro-generator unit.