基于模糊单神经元PI的微电网频率自适应控制

在采用下垂控制策略的传统微电网中存在母线频率随负载增大而下降的问题，为确保母线频率不偏移标准频率，需要采取频率恢复控制策略。目前实现频率恢复的常用方法是基于比例-积分(PI)的控制策略。由于微电网网络结构和系统参数存在变化，PI控制可能无法满足频率控制快速响应、恢复的需求。为解决该问题，文中提出在第二层控制微电网中央控制器中使用单神经元自适应PI控制算法作为频率恢复算法，实现频率无差控制。为进一步增强系统的鲁棒性、加快频率的恢复，使用模糊控制器对单神经元PI控制器的神经元比例系数进行在线优化，并通过仿真与固定神经元比例系数的单神经元自适应PI控制进行对比，证明了所提改进控制策略可改善频率恢复控制的暂态性能，加快微电网的频率恢复。

A microgrid frequency control method based on fuzzy single neuron adaptive PI control

In traditional microgrid with droop control,the bus frequency decreases with the increase of load,and a corresponding frequency regulation control strategy is taken to keep frequency around the rated value. Control strategies based on proportional integral (PI) are commonly used to recover the bus frequency at present. Because of the possibility of changes in structure and system parameters of microgrid,the quick response and quick restoration of frequency may not be fulfilled with PI control. To solve this problem,the single neuron adaptive PI control method is used as frequency recovery algorithm in microgrid central controller in the second control layer to realize non-error control. In order to accelerate frequency recovery and improve the robustness of microgrid,the proportionality coefficient of the single neuron PI controller is optimized online by fuzzy controller. By comparing this method with original single neuron adaptive PI control with fixed neuron proportionality coefficient in simulation. It is proved that this modified method can improve transient performance of frequency recovery control and accelerate frequency recovery of microgrid.