Analysis, Detection, and Location of Open-Switch Submodule Failures in a Modular Multilevel Converter

作     者：Yang, Qichen Qin, Jiangchao Saeedifard, Maryam 

作者机构：Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85287 USA 

出 版 物：《IEEE TRANSACTIONS ON POWER DELIVERY》 (IEEE Trans Power Delivery)

年 卷 期：2016年第31卷第1期

页      面：155-164页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 

基　　金：National Science Foundation Div Of Electrical, Commun & Cyber Sys Directorate For Engineering Funding Source: National Science Foundation 

主　　题：Calculated capacitance clustering algorithm failure detection and location modular multilevel converter (MMC) 

摘      要：The modular multilevel converter (MMC) has become one of the most promising converter topologies for medium/high-power applications. Since the MMC is structured based upon stacking up a number of series-connected identical submodules (SMs), to improve its fault tolerance and reliability, SM failure detection and location is of significant importance. In this paper, the impact of open-circuit switch failures of the SMs on the operation of the MMC is analyzed. Based on the analysis under SM failure conditions, two SM failure detection and location methods are proposed, that is, a clustering algorithm (CA)-based method and a calculated capacitance (CC)-based method. In the proposed CA-based method, a pattern-recognition-based fault diagnosis approach, which employs the clustering algorithm to detect and locate the faulty SMs with open-switch failures through identifying the pattern of 2-D trajectories of the SM characteristic variables, is developed. The proposed CC-based method is based on the calculation and comparison of a physical component parameter, that is, the nominal SM capacitance, and is capable of failure detection, location, and classification within one stage. The performance of the proposed failure detection methods for an MMC system is evaluated based on time-domain simulation studies in the PSCAD/EMTDC software environment. The reported study results demonstrate the capabilities of the two proposed methods in detecting and locating any SM failure under various conditions accurately and efficiently.