基于模糊神经网络的光伏发电系统功率控制方法

Power control strategies for PV generation system based on fuzzy-neural networks

并网光伏发电系统的故障穿越是大规模新能源接入电网和灵活调控的技术难题,针对传统光伏发电系统在电网故障条件下穿越控制策略的不足,提出一种基于模糊神经网络的光伏发电系统功率控制方法。在电网电压突变和跌落情况下能够快速地调整光伏发电系统的工作模式,以适应光伏阵列最大输出功率和并网逆变器额定容量以及最大输出电流的限制,具有稳定性强、跟踪速度快等优点。给出了控制策略总体架构,详细阐述了电网故障控制器运行模式切换策略,建立了模糊神经网络算法的数学模型和实现流程。最后,在Matlab/Simulink平台下搭建了系统仿真模型,仿真结果验证了所提出控制策略的有效性。

Fault ride-through （FRT） techniques are crucial for the large-scale grid-integration and flexible control of the grid-connected PV generation systems. In order to overcome the drawbacks of conventional FRT solutions for the PV generation systems under grid fault conditions, a new power control strategy based on fuzzy-neural networks （FNN） has been proposed for the PV generation systems. The operation modes can be flexibly adjusted to adapt abrupt changes and voltage sag of grid voltage, thus the maximum output power of PV panels and maximum inverter power rating and current rating can be taken into consideration. The benefits of enhanced stability characteristics and tracking performance can be achieved. The overall controller architecture and the operation modes are presented, and the mathematical model and the flow-chart of the fuzzy-neural network algorithm are given in detail. Finally, the system simulation model is estab- lished by using Matlab/Simulink, and the effectiveness of the presented control strategy for PV system has been con- firmed by the simulation results.