改进麻雀搜索算法在PMSM匝间短路中应用研究

Application of improved sparrow search algorithm in PMSM inter-turn short-circuit

针对麻雀搜索算法(SSA)存在收敛精度低和易陷入局部最优等问题,提出了一种改进麻雀搜索算法(ISSA),并应用于PMSM匝间短路故障诊断。首先,搭建了PMSM匝间短路仿真模型,模拟了不同短路匝数比的故障。其次,对故障进行分析,提取了3个故障识别特征量。接着,利用实验平台进行不同短路匝数比的故障测试。然后,介绍了麻雀搜索算法(SSA),并利用Tent混沌映射、自适应正余弦策略和Levy飞行策略对其进行优化,生成改进麻雀搜索算法(ISSA),同时将ISSA算法与SSA算法、粒子群算法(PSO)、灰狼算法(GWO)在测试函数上进行比较,验证其在寻优能力和稳定性等方面具有优越性。紧接着,介绍了随机森林(RF)算法,并搭建了ISSA-RF的故障诊断模型。最后,将4种算法分别对RF的基本参数进行优化并实现故障分类。结果表明,所提出的改进方法能够检测出匝间短路故障及其故障严重程度,ISSA-RF模型的准确率达到98.5%,验证了该算法的有效性和可靠性。

Aiming at the problems of low convergence accuracy and local optimality in sparrow search algorithm(SSA),an improved sparrow search algorithm(ISSA)is proposed and applied to the diagnosis of inter-turn short-circuit fault in PMSM.Firstly,the PMSM inter-turn short-circuit simulation model is built to simulate the fault of different short-circuit turns ratio.Secondly,the fault is analyzed,and three fault recognition features are extracted.Then,the experiment platform is used to test the fault of different short-circuit turns ratio.Then,the sparrow search algorithm(SSA)is introduced and optimized by using Tent chaotic mapping,adaptive sine-cosine strategy and Levy flight strategy to generate an improved sparrow search algorithm(ISSA).Meanwhile,ISSA algorithm is compared with SSA algorithm,particle swarm optimization algorithm(PSO)and grey wolf optimization(GWO)on the test function.It is proved that it has advantages in optimization ability and stability.Then,the random forest(RF)algorithm is introduced,and the fault diagnosis model of ISSA-RF is built.Finally,four algorithms are used to optimize the basic parameters of RF and achieve fault classification.The results show that the proposed improved method can detect the inter-turn short-circuit fault and its severity,and the accuracy of ISSA-RF model reaches 98.5%,which verifies the effectiveness and reliability of the algorithm.