Improved shunt APF based on using adaptive RBF neural network and modified hysteresis current control

Masjedi, S.; Ahmadi, Mojtaba; Pashajavid, Ehsan

doi:10.1109/speedam.2010.5545054

Cited by 7 publications

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…What's more, Han et al [4] simplified the harmonic detection controller. Masjedi et al [5] put forward harmonic detection method based on ANN and built adaptive hysteresis current controller. In addition, Liu et al [6] proposed both fuzzy harmonic detection control method and fuzzy current tracking control method.…”

Section: Introductionmentioning

confidence: 99%

Model reference adaptive fuzzy control of a shunt active power filter

Fei

2014

2014 9th IEEE Conference on Industrial Electronics and Applications

View full text Add to dashboard Cite

Model reference adaptive fuzzy control (MRAFC) is proposed to control the active power filter (APF) and model the APF system caused by nonlinear characteristics, variable parameters and external interferences. T-S fuzzy theory is utilized on the approximation of nonlinear model and the current tracking control. APF system based on MRAFC can be guaranteed to be asymptotical stable. Simulation results are included to confirm the effectiveness of the proposed controller and comparisons with APF system based on model reference adaptive control (MRAC) demonstrate that MRAFC method is capable of reducing the modeling error and tracking reference current signal accurately. Moreover, APF based on the proposed method shows the dynamic and static control performance of stronger robustness, better suitability and higher accuracy than the one based on MRAC modus.

show abstract

Section: Introductionmentioning

confidence: 99%

Model reference adaptive fuzzy control of a shunt active power filter

Fei

2014

2014 9th IEEE Conference on Industrial Electronics and Applications

View full text Add to dashboard Cite

show abstract

“…Dai et al [16] designed an adaptive neural network controller to compensate reactive power and reduce the unwanted harmonics of shunt APF. Masjedi et al [17] proposed an adaptive learning algorithm for RBF neural network to control shunt APF. Neural network controllers with application to APF have been investigated in [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Adaptive RBF Neural Network Control for Three-Phase Active Power Filter

Fei

Wang

2013

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

An adaptive radial basis function (RBF) neural network control system for three‐phase active power filter (APF) is proposed to eliminate harmonics. Compensation current is generated to track command current so as to eliminate the harmonic current of non‐linear load and improve the quality of the power system. The asymptotical stability of the APF system can be guaranteed with the proposed adaptive neural network strategy. The parameters of the neural network can be adaptively updated to achieve the desired tracking task. The simulation results demonstrate good performance, for example showing small current tracking error, reduced total harmonic distortion (THD), improved accuracy and strong robustness in the presence of parameters variation and nonlinear load. It is shown that the adaptive RBF neural network control system for three‐phase APF gives better control than hysteresis control

show abstract

Adaptive control of active power filter using RBF neural network

Fei

Wang

2013

2013 IEEE International Conference on Mechatronics and Automation

View full text Add to dashboard Cite

Improved shunt APF based on using adaptive RBF neural network and modified hysteresis current control

Cited by 7 publications

References 19 publications

Model reference adaptive fuzzy control of a shunt active power filter

Model reference adaptive fuzzy control of a shunt active power filter

Adaptive RBF Neural Network Control for Three-Phase Active Power Filter

Adaptive control of active power filter using RBF neural network

Contact Info

Product

Resources

About