随着高渗透分布式光伏电站接入低压配电网,其并网点过电压现象日益严重。以两级式单相NPC三电平并网逆变器为例,提出了一种基于有限控制集模型预测控制(finite control set model predictive control,FCS-MPC)的过电压抑制方法。采用改...随着高渗透分布式光伏电站接入低压配电网,其并网点过电压现象日益严重。以两级式单相NPC三电平并网逆变器为例,提出了一种基于有限控制集模型预测控制(finite control set model predictive control,FCS-MPC)的过电压抑制方法。采用改进的粒子群优化(particle swarm optimization,PSO)算法追踪光伏阵列的最大功率点(maximum power point,MPP),将其作为有功功率参考值。分析光伏电站接入配电网造成过电压现象的原因,利用逆变器的无功调压理论,得出逆变器的无功功率参考值。通过目标函数的设计,使得逆变器输出时变的有功功率和无功功率,从源侧解决了光伏并网的过电压问题。在Simulink仿真中进行验证,结果表明:相比扰动观测法和电导增量法,改进的PSO算法稳态震荡和跟踪误差最小,跟踪效率最高;当并网点出现过电压现象时,所提的过电压抑制策略可以使逆变器输出一定的无功功率,将并网点电压调节到安全范围以内。展开更多
In the last ten years, high-performance and massively parallel computing technology comes into a high speed developing phase and is used in all fields. The cluster computer systems are also being widely used for their...In the last ten years, high-performance and massively parallel computing technology comes into a high speed developing phase and is used in all fields. The cluster computer systems are also being widely used for their low cost and high performance. In bioinformatics research, solving a problem with computer usually takes hours even days. To speed up research, high-performance cluster computers are considered to be a good platform. Moving into the new MPP (massively parallel processing) system, the original algorithm should be parallelized in a proper way. In this paper, a new parallelizing method of useful sequence alignment algorithm (Smith-Waterman) is designed based on its optimizing algorithm already exists. The result is gratifying.展开更多
In recent years, many different techniques are applied in order to draw maximum power from photo- voltaic （PV） modules for changing solar irradiance and temperature conditions. Generally, the output power generation...In recent years, many different techniques are applied in order to draw maximum power from photo- voltaic （PV） modules for changing solar irradiance and temperature conditions. Generally, the output power generation of the PV system depends on the intermittent solar insolation, cell temperature, efficiency of the PV panel and its output voltage level. Consequently, it is essential to track the generated power of the PV system and utilize the collected solar energy optimally. The aim of this paper is to simulate and control a grid-connected PV source by using an adaptive neuro-fuzzy inference system （ANFIS） and genetic algorithm （GA） controller. The data are optimized by GA and then, these optimum values are used in network training. The simulation results indicate that the ANFIS-GA controller can meet the need of load easily with less fluctuation around the maximum power point （MPP） and can increase the convergence speed to achieve the MPP rather than the conventional method. Moreover, to control both line voltage and current, a grid side P/Q controller has been applied. A dynamic modeling, control and simulation study of the PV system is performed with the Matlab/Simulink program.展开更多
文摘随着高渗透分布式光伏电站接入低压配电网,其并网点过电压现象日益严重。以两级式单相NPC三电平并网逆变器为例,提出了一种基于有限控制集模型预测控制(finite control set model predictive control,FCS-MPC)的过电压抑制方法。采用改进的粒子群优化(particle swarm optimization,PSO)算法追踪光伏阵列的最大功率点(maximum power point,MPP),将其作为有功功率参考值。分析光伏电站接入配电网造成过电压现象的原因,利用逆变器的无功调压理论,得出逆变器的无功功率参考值。通过目标函数的设计,使得逆变器输出时变的有功功率和无功功率,从源侧解决了光伏并网的过电压问题。在Simulink仿真中进行验证,结果表明:相比扰动观测法和电导增量法,改进的PSO算法稳态震荡和跟踪误差最小,跟踪效率最高;当并网点出现过电压现象时,所提的过电压抑制策略可以使逆变器输出一定的无功功率,将并网点电压调节到安全范围以内。
文摘In the last ten years, high-performance and massively parallel computing technology comes into a high speed developing phase and is used in all fields. The cluster computer systems are also being widely used for their low cost and high performance. In bioinformatics research, solving a problem with computer usually takes hours even days. To speed up research, high-performance cluster computers are considered to be a good platform. Moving into the new MPP (massively parallel processing) system, the original algorithm should be parallelized in a proper way. In this paper, a new parallelizing method of useful sequence alignment algorithm (Smith-Waterman) is designed based on its optimizing algorithm already exists. The result is gratifying.
文摘In recent years, many different techniques are applied in order to draw maximum power from photo- voltaic （PV） modules for changing solar irradiance and temperature conditions. Generally, the output power generation of the PV system depends on the intermittent solar insolation, cell temperature, efficiency of the PV panel and its output voltage level. Consequently, it is essential to track the generated power of the PV system and utilize the collected solar energy optimally. The aim of this paper is to simulate and control a grid-connected PV source by using an adaptive neuro-fuzzy inference system （ANFIS） and genetic algorithm （GA） controller. The data are optimized by GA and then, these optimum values are used in network training. The simulation results indicate that the ANFIS-GA controller can meet the need of load easily with less fluctuation around the maximum power point （MPP） and can increase the convergence speed to achieve the MPP rather than the conventional method. Moreover, to control both line voltage and current, a grid side P/Q controller has been applied. A dynamic modeling, control and simulation study of the PV system is performed with the Matlab/Simulink program.
