在实现光伏电站低电压穿越(Low Voltage Ride Through,LVRT)的基础上,分析了光伏电站接入配电网LVRT对前加速自动重合闸的影响。根据故障发生的位置不同,基于时域分析对LVRT和前加速自动重合闸的延时进行整定,提出了光伏电站LVRT与前加...在实现光伏电站低电压穿越(Low Voltage Ride Through,LVRT)的基础上,分析了光伏电站接入配电网LVRT对前加速自动重合闸的影响。根据故障发生的位置不同,基于时域分析对LVRT和前加速自动重合闸的延时进行整定,提出了光伏电站LVRT与前加速自动重合闸的配合方案。该方法能够解决前加速重合闸重合时间与LVRT时限不匹配,导致并网点电压二次跌落和瞬时性故障发展成为永久性故障的问题。通过在电磁暂态仿真软件(Power Systems Computer Aided Design,PSCAD)中建立仿真模型,在10 k V配电网中验证了所提方法的有效性和正确性。展开更多
Different driving decisions will cause different processes of phase transition in traffic flow. To reveal the inner mechanism, this paper built a new cellular automaton （CA） model, based on the driving decision （DD...Different driving decisions will cause different processes of phase transition in traffic flow. To reveal the inner mechanism, this paper built a new cellular automaton （CA） model, based on the driving decision （DD）. In the DD model, a driver＇s decision is divided into three stages： decision-making, action, and result. The acceleration is taken as a decision variable and three core factors, i.e. distance between adjacent vehicles, their own velocity, and the preceding vehicle＇s velocity, are considered. Simulation results show that the DD model can simulate the synchronized flow effectively and describe the phase transition in traffic flow well. Further analyses illustrate that various density will cause the phase transition and the random probability will impact the process. Compared with the traditional NaSch model, the DD model considered the preceding vehicle＇s velocity, the deceleration limitation, and a safe distance, so it can depict closer to the driver preferences on pursuing safety, stability and fuel-saving and has strong theoretical innovation for future studies.展开更多
文摘在实现光伏电站低电压穿越(Low Voltage Ride Through,LVRT)的基础上,分析了光伏电站接入配电网LVRT对前加速自动重合闸的影响。根据故障发生的位置不同,基于时域分析对LVRT和前加速自动重合闸的延时进行整定,提出了光伏电站LVRT与前加速自动重合闸的配合方案。该方法能够解决前加速重合闸重合时间与LVRT时限不匹配,导致并网点电压二次跌落和瞬时性故障发展成为永久性故障的问题。通过在电磁暂态仿真软件(Power Systems Computer Aided Design,PSCAD)中建立仿真模型,在10 k V配电网中验证了所提方法的有效性和正确性。
基金Supported by the Program for National High-Tech Research and Development Program of China under Grant No 2007AA11Z233National Key Technology R & D Program under Grant No. 2009BAG13A06China Postdoctoral Science Foundation Funded Project under Grant No. 20090450395
文摘Different driving decisions will cause different processes of phase transition in traffic flow. To reveal the inner mechanism, this paper built a new cellular automaton （CA） model, based on the driving decision （DD）. In the DD model, a driver＇s decision is divided into three stages： decision-making, action, and result. The acceleration is taken as a decision variable and three core factors, i.e. distance between adjacent vehicles, their own velocity, and the preceding vehicle＇s velocity, are considered. Simulation results show that the DD model can simulate the synchronized flow effectively and describe the phase transition in traffic flow well. Further analyses illustrate that various density will cause the phase transition and the random probability will impact the process. Compared with the traditional NaSch model, the DD model considered the preceding vehicle＇s velocity, the deceleration limitation, and a safe distance, so it can depict closer to the driver preferences on pursuing safety, stability and fuel-saving and has strong theoretical innovation for future studies.
