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摘要翻译：
许多高压应用是通过串联晶闸管实现的。串联晶闸管在稳态和瞬态过程中的电压不平衡是主要的关注点之一。采用动静平衡网络来缓解这种电压不平衡。动平衡网络通常是根据晶闸管关断时的反向恢复电荷来设计的，适用于许多场合。但对于撬棍应用来说，情况并非如此，在撬棍应用中，晶闸管的关断不是一个主要的电路限制。提出了考虑闸门开启延迟时间和平衡网络元件公差的动平衡网络设计方法。根据充放电循环，推导了动平衡网络的两种模型。强调了高di/dt运行时充放电循环在动平衡网络设计中的重要性。利用解析模型研究了动平衡电阻和撬棍限流电感对电压不平衡、充电电流和放电电流的影响。所提出的设计方法还提供了灵活性，可以考虑用于触发单个晶闸管的晶闸管驱动器之间传播延迟的差异。这种延迟不能直接并入传统的基于反向恢复的平衡网络设计方法中。此外，还分析了基于反向恢复电荷的动平衡网络的设计，使得动平衡网络在撬棍应用中存在损耗和体积大的问题。在一个由6个晶闸管串联的12kV、1kA撬棍上的仿真研究和实验结果证实了理论分析的正确性，并验证了所提出的方法在撬棍中的应用。
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英文标题：
《Thyristor Voltage Equalizing Network for Crowbar Application》
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作者：
Subhash Joshi T.G. and Vinod John
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最新提交年份：
2018
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分类信息：
一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Signal Processing 信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
Many high voltage applications are realized with series connected thyristors. Voltage imbalance among series connected thyristors during steady state as well as in transients is one of the major concerns. This voltage imbalance is mitigated by using static and dynamic balancing network. Dynamic balancing networks are typically designed based on reverse recovery charge of the thyristor during turn-off, which suits many applications. But this is not the case for a crowbar application, where turn-off of the thyristor is not a major circuit constraint. This paper proposes the design method for dynamic balancing network considering gate turn-on delay time and the balancing network component tolerances. The paper derives two models for the dynamic balancing network based on its charge-discharge cycle. The importance of charge-discharge cycle in the design of dynamic balancing network during high di/dt operation is emphasized. Influence of dynamic balancing resistance and crowbar current limiting inductance on voltage imbalance, charging current and discharging current is studied using the analytical model. The proposed design method also offers flexibility to incorporate differences in propagation delays among the thyristor drivers that are used to trigger individual thyristors. Such delays cannot be directly incorporated in the conventional balancing network design method based on reverse recovery. Further, it is also analytically shown that designing the dynamic balancing network based on reverse recovery charge makes the balancing network lossy and bulky for crowbar application. Simulation studies and experimental results on a 12kV , 1kA crowbar consisting of six series connected thyristors confirms the theoretical analysis and validates the proposed approach for crowbar applications.
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PDF链接：
https://arxiv.org/pdf/1802.06929
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