基于模糊神经网络风电混合储能系统优化控制

采用风电储能系统来平抑风电波动功率在当今是一个有效的措施,然而储能系统控制策略的好坏直接影响风电系统的技术性能和经济性能。根据超级电容器和蓄电池在功能上的互补性,将其应用在基于双馈电机的风电场中,风电场采用分布整流集中逆变拓扑控制结构,并对其设计模糊神经PID控制器,采用模糊神经网络算法对混合储能系统PID控制参数进行在线优化。基于Matlab/Simulink平台搭建控制系统仿真模型,并进行仿真分析,验证了混合储能系统能够提高储能装置的使用寿命。根据储能系统补偿功率和其荷电状态的波动范围,以及对风电波动功率的平滑程度,验证了该控制系统的有效性。

Optimal control in a wind power hybrid energy storage system based on fuzzy neural network

The use of wind power energy storage system is an effective measure to stabilize fluctuations in wind power today, however, the energy storage system control strategy has a direct impact on the wind power system's technical and economic performance. Based on the complementary in function of super-capacitor and battery, its application in wind farms based DFIG and the topology control structures for wind farm with distributed rectifier and centralized inverter are adopted, fuzzy neural PID controller is designed, and fuzzy neural network algorithm is used to optimize PID control parameters for hybrid energy storage system on line. Based on MATLAB / SIMULINK platform, the control system simulation model is established and analyzed. Simulation results show that the hybrid energy storage system can improve the life of the energy storage device. According to the compensate power and the fluctuation range of the state of charge of the energy storage systems, as well as wind power fluctuations in the degree of smoothing, the effectiveness of the control system is verified.