The Floating nuclear power plant grid is composed of power generation,in-station power supply and external power delivery.To ensure the safety of the nuclear island,the in-station system adopts a special power supply ...The Floating nuclear power plant grid is composed of power generation,in-station power supply and external power delivery.To ensure the safety of the nuclear island,the in-station system adopts a special power supply mode,while the external power supply needs to be adapted to different types of external systems.Because of frequent single phase-ground faults and various fault forms,the fault line selection protection should be accurate,sensitive and adaptive.This paper presents a fault line selection method in cooperation with multi-mode grounding control.Based on the maximum united energy entropy ratio(MUEER),the optimal wavelet basis function and decomposition scale are adaptively chosen,while the fault line is selected by wavelet transform modulus maxima(WTMM).For high-impedance faults(HIFs),to enlarge the fault feature,the system grounding mode can be switched by the multi-mode grounding control.Based on the characteristic of HIFs,the fault line can be selected by comparing phase differences of zero-sequence current mutation and fault phase voltage mutation before and after the fault.Simulation results using MATLAB/Simulink show the effectiveness of the proposed method in solving the protection problems.展开更多
Cryogenic ground support equipment (CGSE) is an important part of a famous particle physics experiment - AMS-02. In this paper a design method which optimizes PID parameters of CGSE control system via the particle swa...Cryogenic ground support equipment (CGSE) is an important part of a famous particle physics experiment - AMS-02. In this paper a design method which optimizes PID parameters of CGSE control system via the particle swarm optimization (PSO) algorithm is presented. Firstly, an improved version of the original PSO, cooperative random learning particle swarm optimization (CRPSO), is put forward to enhance the performance of the conventional PSO. Secondly, the way of finding PID coefficient will be studied by using this algorithm. Finally, the experimental results and practical works demonstrate that the CRPSO-PID controller achieves a good performance.展开更多
基金Project Supported by National Natural Science Foundation of China(No.51877089).Research on the mechanism and fault ride-through integrated strategies of an active power router in hybrid AC and DC distribution grids.
文摘The Floating nuclear power plant grid is composed of power generation,in-station power supply and external power delivery.To ensure the safety of the nuclear island,the in-station system adopts a special power supply mode,while the external power supply needs to be adapted to different types of external systems.Because of frequent single phase-ground faults and various fault forms,the fault line selection protection should be accurate,sensitive and adaptive.This paper presents a fault line selection method in cooperation with multi-mode grounding control.Based on the maximum united energy entropy ratio(MUEER),the optimal wavelet basis function and decomposition scale are adaptively chosen,while the fault line is selected by wavelet transform modulus maxima(WTMM).For high-impedance faults(HIFs),to enlarge the fault feature,the system grounding mode can be switched by the multi-mode grounding control.Based on the characteristic of HIFs,the fault line can be selected by comparing phase differences of zero-sequence current mutation and fault phase voltage mutation before and after the fault.Simulation results using MATLAB/Simulink show the effectiveness of the proposed method in solving the protection problems.
基金the National Basic Research Program (973) of China (No. 2004CB720703)
文摘Cryogenic ground support equipment (CGSE) is an important part of a famous particle physics experiment - AMS-02. In this paper a design method which optimizes PID parameters of CGSE control system via the particle swarm optimization (PSO) algorithm is presented. Firstly, an improved version of the original PSO, cooperative random learning particle swarm optimization (CRPSO), is put forward to enhance the performance of the conventional PSO. Secondly, the way of finding PID coefficient will be studied by using this algorithm. Finally, the experimental results and practical works demonstrate that the CRPSO-PID controller achieves a good performance.
