Yue Pan

To solve the problem that it is difficult to accurately identify the rotor eccentric fault, demagnetization fault and hybrid fault of a permanent magnet synchronous motor (PMSM) with a slot pole ratio of 3/2 and several times of it, this paper proposes a method to identify the rotor fault based on the combination of branch current analysis and a machine learning algorithm. First, the analysis of the electrical signal of the PMSM after various types of rotor faults shows that there are differences in the time domain of the electrical signal of the PMSM after three types of rotor faults. Considering the symmetry of the structure of the PMSM with a slot-pole ratio of 3/2 and its integer multiples, the changes in the time domain of the phase currents cancel each other after the fault, and the time domain fluctuations of the stator branch currents that do not cancel each other are chosen as the characteristics of the fault classification in this paper. Secondly, after signal preprocessing, feature factors are extracted and several fault feature factors with large differences are selected to construct feature vectors. Finally, a genetic algorithm is used to optimize the parameters of a support vector machine (SVM), and the GA-SVM model is constructed as a classifier for multifault classification of permanent magnet synchronous motors to classify these three types of faults. The fault classification results show that the proposed method using branch current signals combined with GA-SVM can effectively diagnose faulty PMSM.

When a permanent magnet synchronous motor (PMSM) is designed according to the traditional motor design theory, the performance of the motor is often challenging to achieve the desired goal, and further optimization of the motor design parameters is usually required. However, the motor is a strongly coupled, non-linear, multivariate complex system, and it is a challenge to optimize the motor by traditional optimization methods. It needs to rely on reliable surrogate models and optimization algorithms to improve the performance of the PMSM, which is one of the problematic aspects of motor optimization. Therefore, this paper proposes a strategy based on a combination of a high-precision combined surrogate model and the optimization method to optimize the stator and rotor structures of interior PMSM (IPMSM). First, the variables were classified into two layers with high and low sensitivity based on the comprehensive parameter sensitivity analysis. Then, Latin hypercube sampling (LHS) is used to obtain sample points for highly sensitive variables, and various methods are employed to construct surrogate models for variables. Each optimization target is based on the acquired sample points, from which the most accurate combined surrogate model is selected and combined with non-dominated ranking genetic algorithm-II (NSGA-II) to find the best. After optimizing the high-sensitivity variables, a new finite element model (FEM) is built, and the Taguchi method is used to optimize the low-sensitivity variables. Finally, finite element analysis (FEA) was adopted to compare the performance of the initial model and the optimized ones of the IPMSM. The results showed that the performance of the optimized motor is improved to prove the effectiveness and reliability of the proposed method.

A high-end permanent magnet (PM) synchronous motor’s cogging torque is a significant performance measure (PMSM). During the running of the motor, excessive cogging torque will amplify noise and vibration. Therefore, the cogging torque must be taken into account while optimizing the design of motors with precise motion control. In this research, we proposed a local optimization-seeking approach (RSM+NSGA-II-LR) based on Response Surface Methodology (RSM) and Non-Dominated Sorting Genetic Algorithm-II (NSGA-II), which reduced the cogging torque of a permanent magnet synchronous motor (SPMSM). To reduce the complexity of optimization and increase its efficiency, the sensitivity analysis method was utilized to identify the structural parameters that had a significant impact on the torque performance. Second, RSM was utilized to fit the functional relationship between the structural parameters and each optimization objective, and NSGA-II was integrated to provide the Pareto solution for each optimization objective. The solution with a greater average torque than the initial motor and the lowest cogging torque was chosen, and a new finite element model (FEM) was created. On the basis of the sensitivity analysis, the structural factors that had the highest influence on the cogging torque were selected, and the RSM is utilized for local optimization to lower the cogging torque as much as feasible. The numerical results demonstrated that the optimization strategy presented in this study effectively reduced the cogging torque of the motor without diminishing the motor’s average torque or increasing its torque ripple.