基于自适应神经网络的光伏发电系统并网控制策略

Grid-connected Control Strategies for PV Power System using Adaptive Neural Networks

针对传统并网光伏发电系统在电网故障条件下穿越控制策略的不足,提出一种基于自适应模糊神经网络的光伏发电系统并网控制方法。该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,阐述了电网故障控制器运行模式切换策略,建立了自适应模糊神经网络算法的数学模型。在Matlab/Simulink软件平台下搭建了仿真模型,验证了控制策略的有效性。

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