神经网络算法的改进及其在有源电力滤波器中的应用
针对有源电力滤波器的电流跟踪控制问题,设计了一种基于改进梯度算法的BP神经网络自适应PI控制器。该控制器将神经网络技术与PI参数设计相结合,与传统的PI控制器相比,该控制器具有结构简单、易于在线调整等优点。同时,为了克服采用神经网络算法修正权值系数时,会存在局部极小、收敛速度慢的问题,对BP神经网络采用的梯度算法进行改进。利用代数法代替梯度下降法,从而解决了易出现局部极小问题,且使收敛速度更快。仿真实验表明,改进后的神经网络自适应PI控制器较传统的PI控制器有更快的响应速度和更高的补偿精度,从而使系统更稳定,而且电网电流的谐波畸变率更低。...
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| Published in | 电力系统保护与控制 Vol. 43; no. 24; pp. 142 - 148 |
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| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
中国矿业大学信电学院,江苏 徐州,221008
2015
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1674-3415 |
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| Summary: | 针对有源电力滤波器的电流跟踪控制问题,设计了一种基于改进梯度算法的BP神经网络自适应PI控制器。该控制器将神经网络技术与PI参数设计相结合,与传统的PI控制器相比,该控制器具有结构简单、易于在线调整等优点。同时,为了克服采用神经网络算法修正权值系数时,会存在局部极小、收敛速度慢的问题,对BP神经网络采用的梯度算法进行改进。利用代数法代替梯度下降法,从而解决了易出现局部极小问题,且使收敛速度更快。仿真实验表明,改进后的神经网络自适应PI控制器较传统的PI控制器有更快的响应速度和更高的补偿精度,从而使系统更稳定,而且电网电流的谐波畸变率更低。 |
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| Bibliography: | For current tracking control problems in active power filter（APF）, a BP neural network adaptive PI controller based on improved gradient algorithm is designed. It combines the neural network technology with PI controller structure.Compared with the traditional PI controller, it has a simple structure, and easy to on-line adjustment. Meanwhile, in order to overcome the local minimum and slow convergence problem when using neural network algorithm to weight correction coefficient, the gradient algorithm is improved and the algebraic method instead of gradient descent method is used to solve the problem of the local minimum arise, and makes convergence faster. Simulation experiments show that the improved adaptive neural network PI controller has faster response and higher compensation accuracy, thus to make the system more stable, and the harmonic distortion of grid current is lower. active power filter; current tracking control; BP neural network; algebraic algorithm; gradient algorithm MA Caoyuan,SUN Fuhua,ZHU |
| ISSN: | 1674-3415 |