基于神经网络逆控制的微网储能逆变器输出电压研究

Neural Network Inverse Control for the Output Voltage of Energy Storage Inverter in Micro-grid

为改善微网中储能逆变器的输出电压波形质量,提出了一种基于神经网络的逆模型控制方法。建立了储能逆变器的数学模型,分析了影响其输出电压的主要因素,利用前向神经网络建立了系统的扩展逆模型;针对BP训练算法容易陷入局部最优的问题,通过万有引力算法进行了网络初始参数优化;将神经网络逆模型与原模型串联后,采用PI控制器实施闭环控制。仿真结果表明,该方法可以有效的提高储能逆变器的动态响应速度,并降低输出电压的谐波含量。制作了10 k W储能逆变器样机进行试验,结果表明了所提方法的可行性与有效性。

In order to improve the output voltage waveform quality of energy storage inverter in micro-grid, an inverse control method was proposed based on BP neural network. Mathematical model of the energy storage inverter was established, and the main factors affecting the output voltage were analyzed, and then the expansion inverse model of the system was established based on BP neural network. In order to overcome the local optimum disadvantage in BP training algorithm, gravity algorithm was adopted to optimize the network initial parameters. The neural network inverse model was put in series with its original model to form a pseudo linear system, and then PI controller was selected to perform the single loop control The simulation results show that the proposed control method can effectively improve the dynamic response speed of the inverter output voltage and reduce the harmonic content. Experiment is performed on 10 kW inverter prototype, which proves the proposed method feasibility and effectiveness.