Multi-terminal direct current(MTDC)grids provide the possibility of meshed interconnections between regional power systems and various renewable energy resources to boost supply reliability and economy.The modular mul...Multi-terminal direct current(MTDC)grids provide the possibility of meshed interconnections between regional power systems and various renewable energy resources to boost supply reliability and economy.The modular multilevel converter(MMC)has become the basic building block for MTDC and DC grids due to its salient features,i.e.,modularity and scalability.Therefore,the MMC-based MTDC systems should be pervasively embedded into the present power system to improve system performance.However,several technical challenges hamper their practical applications and deployment,including modeling,control,and protection of the MMC-MTDC grids.This paper presents a comprehensive investigation and reference in modeling,control,and protection of the MMC-MTDC grids.A general overview of state-of-the-art modeling techniques of the MMC along with their performance in simulation analysis for MTDC applications is provided.A review of control strategies of the MMC-MTDC grids which provide AC system support is presented.State-of-the art protection techniques of the MMCMTDC systems are also investigated.Finally,the associated research challenges and trends are highlighted.展开更多
Fault diagnosis is very important for development and maintenance of safe and reliable electronic circuits and systems. This paper describes an approach of soft fault diagnosis for analog circuits based on slope fault...Fault diagnosis is very important for development and maintenance of safe and reliable electronic circuits and systems. This paper describes an approach of soft fault diagnosis for analog circuits based on slope fault feature and back propagation neural networks (BPNN). The reported approach uses the voltage relation function between two nodes as fault features; and for linear analog circuits, the voltage relation function is a linear function, thus the slope is invariant as fault feature. Therefore, a unified fault feature for both hard fault (open or short fault) and soft fault (parametric fault) is extracted. Unlike other NN-based diagnosis methods which utilize node voltages or frequency response as fault features, the reported BPNN is trained by the extracted feature vectors, the slope features are calculated by just simulating once for each component, and the trained BPNN can achieve all the soft faults diagnosis of the component. Experiments show that our approach is promising.展开更多
基金funded by SGCC Science and Technology Program under project Research on Electromagnetic Transient Simulation Technology for Large-scale MMC-HVDC Systems.
文摘Multi-terminal direct current(MTDC)grids provide the possibility of meshed interconnections between regional power systems and various renewable energy resources to boost supply reliability and economy.The modular multilevel converter(MMC)has become the basic building block for MTDC and DC grids due to its salient features,i.e.,modularity and scalability.Therefore,the MMC-based MTDC systems should be pervasively embedded into the present power system to improve system performance.However,several technical challenges hamper their practical applications and deployment,including modeling,control,and protection of the MMC-MTDC grids.This paper presents a comprehensive investigation and reference in modeling,control,and protection of the MMC-MTDC grids.A general overview of state-of-the-art modeling techniques of the MMC along with their performance in simulation analysis for MTDC applications is provided.A review of control strategies of the MMC-MTDC grids which provide AC system support is presented.State-of-the art protection techniques of the MMCMTDC systems are also investigated.Finally,the associated research challenges and trends are highlighted.
基金the National Basic Research and Development (973) Program of China (No.2005cb321604)the National Natural Science Foundation of China (No. 60633060)
文摘Fault diagnosis is very important for development and maintenance of safe and reliable electronic circuits and systems. This paper describes an approach of soft fault diagnosis for analog circuits based on slope fault feature and back propagation neural networks (BPNN). The reported approach uses the voltage relation function between two nodes as fault features; and for linear analog circuits, the voltage relation function is a linear function, thus the slope is invariant as fault feature. Therefore, a unified fault feature for both hard fault (open or short fault) and soft fault (parametric fault) is extracted. Unlike other NN-based diagnosis methods which utilize node voltages or frequency response as fault features, the reported BPNN is trained by the extracted feature vectors, the slope features are calculated by just simulating once for each component, and the trained BPNN can achieve all the soft faults diagnosis of the component. Experiments show that our approach is promising.
