首页> 外文会议>Institute of Electrical and Electronics Engineers Energy Conversion Congress and Exposition >Quantitative Power Quality and Characteristic Analysis of Multilevel Pulse Width Modulation Methods in Medium Voltage High Power Industrial Drives
【24h】

Quantitative Power Quality and Characteristic Analysis of Multilevel Pulse Width Modulation Methods in Medium Voltage High Power Industrial Drives

机译:中电压大功率工业驱动中多级脉冲宽度调制方法的定量功率质量及特性分析

获取原文

摘要

The inherent low switching frequency in medium voltage (MV) alternating current (AC) industrial drives presents power quality and filter design challenges. In this paper, four multilevel pulse width modulation (PWM) methods: Phase Disposition (PD), Switching Loss Minimization (SLM), Selective Harmonic Elimination (SHE) up to 17th and 29th harmonics respectively are considered. The characteristics of long cable effects on common mode voltage (CMV) and differential mode voltage (DMV), inverter losses and efficiency, induction machine (IM) voltage and current harmonics are analyzed. Very little has been published in these quantitative comparisons. It is shown that the SHE method has reduced CMV as compared to the PD and SLM algorithms. Up to 29th harmonic elimination achieves the best harmonics performance without needing an output filter, at the expense that the losses are higher with a lower efficiency. Analytical and simulation results using PLECS for the power electronics circuits and Matlab/Simulink for control systems are verified experimentally with a 1000hp, 4160V neutral point clamped (NPC) adjustable-speed drive (ASD) system that includes a 24-pulse front-end voltage source converter.
机译:中压(MV)交流电流(AC)工业驱动器的固有的低开关频率呈现出电源质量和过滤器设计挑战。本文四种多级脉冲宽度调制(PWM)方法:相位置位(PD),切换损耗最小化(SLM),分别考虑最多第17和第29和第29次谐波的选择性谐波消除(SLM)。分析了对共模电压(CMV)和差模电压(DMV)的长电缆效应的特点,分析了逆变器损耗和效率,感应机(IM)电压和电流谐波。在这些定量比较中公布了很少。结果表明,与PD和SLM算法相比,SHE方法减少了CMV。最多29次谐波消除实现了最佳的谐波性能,而无需输出过滤器,以损耗较低的效率较高。使用电力电子电路和MATLAB / Simulink的PLEC用于控制系统的分析和仿真结果通过1000HP,4160V中性点钳位(NPC)可调速度驱动器(ASD)系统进行了实验验证,包括24脉冲前端电压源转换器。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号