弱电网下LCL换流器谐振的自适应抑制研究

LCL型换流器因其体积小,滤波性能好等优点广泛地应用于并网系统中。但其自身的谐振问题不可忽略,电容电流反馈的有源阻尼方法是常用的谐振抑制方法。在实际应用中,电网阻抗能够对LCL滤波器谐振产生影响。基于此,本文提出一种基于遗传算法优化RBF神经网络的自适应谐振抑制方法,该方法首先依据遗传算法对RBF神经网络进行初始参数的优化;利用RBF神经网络自身的辨识能力对PI控制器的参数进行识别,实时修正PI控制参数和有源阻尼系数,从而实现了LCL型换流器在电网阻抗变化时保持系统稳定。最后分别采用传统无参数优化方法、RBF神经网络优化方法以及本文所提方法进行实验,通过仿真结果的分析,验证了该方法的有效性。

Research on Adaptive Suppression of LCL Converter Resonance under Weak Power Grid

LCL type converters are widely used in grid-connected systems due to their small size and good filtering performance. However, the resonance suppression problem brought by its own application is also not negligible. The active damping method of capacitive current feedback is a commonly used resonance suppression method. In practical applications, the effect of the grid impedance on the LCL filter not be ignored, which will affect the resonance of the LCL filter. In this paper, an adaptive resonance suppression method based on genetic algorithm is proposed to optimize the RBF neural network. Firstly, the initial parameters of the RBF neural network are optimized according to the genetic algorithm. The parameters of the PI controller are used to identify the parameters of the RBF neural network. Identification, real-time correction of PI control parameters and active damping coefficient, thus achieving the LCL-type converter to maintain system stability when the grid impedance changes. The effectiveness of the method is verified by experiments.