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作 者:李君卫 汤亚芳[2] 张尚然 Li Junwei;Tang Yafang;Zhang Shangran(Qiannan Normal University for Nationalities,Duyun 558000,Guizhou,China;School of Electrical Engineering,Guizhou University,Guiyang 550025,China;Hebei Instrumentation Engineering Technology Research Center,Chengde 067000,Hebei,China)
机构地区:[1]黔南民族师范学院,贵州都匀558000 [2]贵州大学电气工程学院,贵阳550025 [3]河北省仪器仪表工程技术研究中心,河北承德067000
出 处:《电测与仪表》2023年第2期172-179,共8页Electrical Measurement & Instrumentation
基 金:国家自然科学基金资助项目(61802162);贵州省科技厅自然科学基金重点资助项目([2019]1447);贵州省教育厅自然科学基金项目([2019]071,[2019]205);黔南州自然科学基金项目([2018]05);黔南民族师范学院自然科学基金项目(QNSY2018BS012,QNSYRC201714)。
摘 要: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。但其自身的谐振问题不可忽略,电容电流反馈有源阻尼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,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控制器参数和有源阻尼系数,从而实现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。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 resonance of LCL filter cannot be ignored. On this basis, an adaptive resonance suppression method based on genetic algorithm is proposed to optimize the RBF neural network. 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 proposed method is verified by simulation experiments.
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