定子
转子(电动)
磁铁
机械工程
材料科学
齿槽效应转矩
反电动势
扭矩
芯(光纤)
汽车工程
计算机科学
电压
工程类
电气工程
复合材料
物理
热力学
作者
Guan-Ming Chen,Ching-Chien Huang,Chien‐Ming Huang,Yu-Lin Chi
标识
DOI:10.1088/2053-1591/acc785
摘要
Abstract Traditional water pump use AC motor combines with gearbox to achieve high torque and low speed to protect fish form injury by blades, which will shorten the life of waterwheel. Besides, the efficiency of the gearbox is about 40 ∼ 50%, resulting in low efficiency of whole system, and translated into poor reliability. The waterwheel in this study uses surface permanent magnet (SPM) motor instead of AC motor. However, the design of conventional waterwheel PM motor has the following drawbacks: (a) the slot fill factor cannot be too low; (b) the spacing between the rotor magnet grooves is too large, which affect the efficiency of the motor. To improve the motor efficiency without changing the shape of the stator core, this study developed an assembled stator core to improve the slot fill rate of motor winding. However, if the stack length of the stator core is too high, there will be a problem of core breakage. Therefore, this study introduced the self-adhesive steel to develop a self-adhesive assembled stator core to improve the breakage situation, and also to take advantage of the higher slot fill rate of the assembled core. Furthermore, in order to solve the problem of large spacing between the rotor magnet grooves, we optimize the magnetic circuit design by using simulation to improve the motor performance, such as magnet size, slot opening size, etc, to make the back electromotive force waveform as a sine wave. In addition, this design also reduces the thickness of magnets to reduce the cogging torque of the motor, and effectively reduces magnet usage to reduce the cost. As a result, this study developed a high-efficiency waterwheel SPM motor about 86.32% at 123 rpm, by using self-adhesive assembled stator core to increase the slot fill factor. The overall efficiency of system reaches about 78.72%.
科研通智能强力驱动
Strongly Powered by AbleSci AI