基于粒子群优化算法的永磁电机热网络参数识别

监测永磁电机的永磁体温度对于保证电机的使用寿命至关重要,因为过高的温度会产生永磁体不可逆失磁现象。提出了一种基于粒子群优化算法的永磁电机热网络参数识别方法,实现用热网络监测永磁体的温度。该方法首先建立永磁电机的热网络模型,利用粒子群优化算法结合电机温升试验所得温度数据对热网络模型的主要热力参数进行识别;然后利用该热网络模型进行在线温度识别,识别过程能够快速收敛,具备良好的辨识精度;最后,通过对比仿真识别温度和电机温升试验数据,验证了该方法的准确性。

Thermal Network Temperature Identification of Permanent MagnetMotor Based on Particle Swarm Optimization Algorithm

Monitoring the permanent magnet temperature of permanent magnet motors was critical to the life of the motor, because the excessive permanent magnet temperature increased the risk of irreversible demagnetization. A method based on particle swarm optimization (PSO) algorithm for permanent magnet motor thermal network temperature identification was proposed to monitor the temperature of permanent magnets. In this method, the thermal network model of the permanent magnet motor was established, and the PSO algorithm was combined with the temperature data obtained by the motor temperature rise experiment to identify the main thermal parameters of the model. This method was used to identify the temperature online, and the identification process could be quickly converged with good accuracy. Finally, the feasibility and accuracy of the method were verified by comparing the temperature of the simulation with the experimental data.