基于FWA-PSO-MSVM的船舶区域配电电力系统故障诊断

Fault Diagnosis of Shipboard Zonal Distribution Power System Based on FWA-PSO-MSVM

故障发生会极大地影响船舶区域配电电力系统运行的安全性。为了保证船舶的安全运行,针对船舶区域配电电力系统中的10种短路故障,用MATLAB/Simulink建立船舶区域配电电力系统的电力系统仿真模型,利用SMOTE过采样对故障数据进行预处理。利用主成分分析(Principal Components Snalysis,PCA)提取出故障数据中的特征向量,并将其作为多分类支持向量机(Multiclass Support Vector Machine,MSVM)的输入进行故障诊断。为了优化诊断结果,提出烟花粒子群优化算法来优化MSVM的惩罚因子C和核函数参数γ,再与仅粒子群优化算法寻优后MSVM故障分类的结果进行对比。仿真验证结果表明,所提算法具有更高的故障分类准确率和精度。

The occurrence of faults will greatly affect the safety of the shipboard zonal distribution power system.In order to ensure the safe operation of ships,10types of short-circuit faults in the shipboard zonal distribution power system are focused in this paper.MATLAB/Simulink is used to establish the power system simulation model of the shipboard zonal distribution power system,SMOTE oversampling is adopted to preprocess the fault data.Taking the feature vector extracted by principal component analysis(PCA)in fault data as input of multi-class support vector machine(MSVM)for fault diagnosis.In order to optimize the diagnosis results,a firework particle swarm optimization algorithm is presented to optimize the penalty factor C and the kernel function parameterγof MSVM,which is compared with the results of MSVM fault classification optimized only by the particle swarm optimization algorithm.Simulation results show that the proposed algorithm has higher fault classification accuracy and precision.