Performance Enhancement of a Series Active Power Filter using Kalman Filter based Neural Network Control Strategy

2018 ◽  
Author(s):  
Aravind Ravikumar ◽  
Neethu Mohan ◽  
K.P Soman
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Control Strategy ◽  
Performance Enhancement ◽  
Active Power Filter ◽  
Active Power ◽  
Network Control ◽  
Neural Network Control ◽  
Power Filter

scholarly journals Adaptive Neural Sliding Mode Control of Active Power Filter

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Juntao Fei ◽  
Zhe Wang
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Active Power Filter ◽  
Active Power ◽  
Network Control ◽  
Neural Network Control ◽  
Power Filter ◽  
Mode Control

A radial basis function (RBF) neural network adaptive sliding mode control system is developed for the current compensation control of three-phase active power filter (APF). The advantages of the adaptive control, neural network control, and sliding mode control are combined together to achieve the control task; that is, the harmonic current of nonlinear load can be eliminated and the quality of power system can be well improved. Sliding surface coordinate function and sliding mode controller are used as input and output of the RBF neural network, respectively. The neural network control parameters are online adjusted through gradient method and Lyapunov theory. Simulation results demonstrate that the adaptive RBF sliding mode control can compensate harmonic current effectively and has strong robustness to disturbance signals.

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