DC-Link Voltage Regulation Using RPFNN-AMF for Three-Phase Active Power Filter

Due to the instantaneous power following into or out of the DC-link capacitor in a three-phase shunt active power filter (APF), the DC-link voltage regulation control plays an important role in the shunt APF especially under nonlinear load change. In this paper, for the purpose of improving the DC-link voltage regulation control in the shunt APF under nonlinear load variation and reducing the total harmonic distortion of the current effectively, a novel recurrent probabilistic fuzzy neural network with an asymmetric membership function (RPFNN-AMF) controller is developed to substitute for the conventional proportional-integral controller. Moreover, the network structure, the online learning algorithm, and the convergence analysis of the proposed RPFNN-AMF are detailedly introduced. Finally, the effectiveness and feasibility of the shunt APF using the proposed RPFNN-AMF controller for the DC-link voltage regulation control and the compensation of harmonic current are verified by some experimental results.

[1]  Jin Bae Park,et al.  Generalized predictive control based on self-recurrent wavelet neural network for stable path tracking of mobile robots: adaptive learning rates approach , 2006, IEEE Transactions on Circuits and Systems I: Regular Papers.

[2]  Yung-Ruei Chang,et al.  Intelligent-controlled doubly fed induction generator system using PFNN , 2012, Neural Computing and Applications.

[3]  Anup Kumar Panda,et al.  Types-1 and -2 fuzzy logic controllers-based shunt active filter I d -I q control strategy with different fuzzy membership functions for power quality improvement using RTDS hardware , 2013 .

[4]  Hideaki Fujita,et al.  Dynamic Control and Analysis of DC-Capacitor Voltage Fluctuations in Three-Phase Active Power Filters , 2013, IEEE Transactions on Power Electronics.

[5]  H. Akagi,et al.  A shunt active filter based on voltage detection for harmonic termination of a radial power distribution line , 1998, Conference Record of 1998 IEEE Industry Applications Conference. Thirty-Third IAS Annual Meeting (Cat. No.98CH36242).

[6]  Kuang-Hsiung Tan.  Squirrel-Cage Induction Generator System Using Wavelet Petri Fuzzy Neural Network Control for Wind Power Applications , 2016, IEEE Transactions on Power Electronics.

[7]  Cheng-Jian Lin,et al.  Prediction and identification using wavelet-based recurrent fuzzy neural networks , 2004, IEEE Trans. Syst. Man Cybern. Part B.

[8]  Engin Özdemir,et al.  Synchronous-Reference-Frame-Based Control Method for UPQC Under Unbalanced and Distorted Load Conditions , 2011, IEEE Transactions on Industrial Electronics.

[9]  Bing Chen,et al.  Global Stability Criterion for Delayed Complex-Valued Recurrent Neural Networks , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[10]  Faa-Jeng Lin,et al.  Adaptive hybrid control using a recurrent neural network for a linear synchronous motor servo-drive system , 2001 .

[11]  F. Blaabjerg,et al.  High Performance Current Controller for Selective Harmonic Compensation in Active Power Filters , 2007, IEEE Transactions on Power Electronics.

[12]  Faa-Jeng Lin,et al.  A Three-Phase Four-Leg Inverter-Based Active Power Filter for Unbalanced Current Compensation Using a Petri Probabilistic Fuzzy Neural Network , 2017 .

[13]  Faa-Jeng Lin,et al.  Fault-Tolerant Control for Six-Phase PMSM Drive System via Intelligent Complementary Sliding-Mode Control Using TSKFNN-AMF , 2013, IEEE Transactions on Industrial Electronics.

[14]  James R. McDonald,et al.  Using physics-based modeler outputs to train probabilistic neural networks for unexploded ordnance (UXO) classification in magnetometry surveys , 2001, IEEE Trans. Geosci. Remote. Sens..

[15]  Faa-Jeng Lin,et al.  DSP-Based Probabilistic Fuzzy Neural Network Control for Li-Ion Battery Charger , 2012, IEEE Transactions on Power Electronics.

[16]  Syuan-Yi Chen,et al.  Recurrent Functional-Link-Based Fuzzy Neural Network Controller With Improved Particle Swarm Optimization for a Linear Synchronous Motor Drive , 2009, IEEE Transactions on Magnetics.

[17]  Rong-Jong Wai,et al.  Design of Dynamic Petri Recurrent Fuzzy Neural Network and Its Application to Path-Tracking Control of Nonholonomic Mobile Robot , 2009, IEEE Transactions on Industrial Electronics.

[18]  Leonardo Bruno Garcia Campanhol,et al.  Application of shunt active power filter for harmonic reduction and reactive power compensation in three-phase four-wire systems , 2014 .

[19]  Yung-Ruei Chang,et al.  Reactive Power Control of Three-Phase Grid-Connected PV System During Grid Faults Using Takagi–Sugeno–Kang Probabilistic Fuzzy Neural Network Control , 2015, IEEE Transactions on Industrial Electronics.

[20]  Faa-Jeng Lin,et al.  Squirrel-cage induction generator system using probabilistic fuzzy neural network for wind power applications , 2015, 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[21]  Sangdo Park,et al.  Diagnosis of Electric Vehicle Batteries Using Recurrent Neural Networks , 2017, IEEE Transactions on Industrial Electronics.

[22]  Norman Mariun,et al.  DC-link capacitor voltage control for single-phase shunt active power filter with step size error cancellation in self-charging algorithm , 2016 .

[23]  V. Khadkikar,et al.  Enhancing Electric Power Quality Using UPQC: A Comprehensive Overview , 2012, IEEE Transactions on Power Electronics.

[24]  Xin Jin,et al.  Classification of freeway traffic patterns for incident detection using constructive probabilistic neural networks , 2001, IEEE Trans. Neural Networks.

[25]  Bhaskar D. Rao,et al.  On-line learning algorithms for locally recurrent neural networks , 1999, IEEE Trans. Neural Networks.

[26]  Robert Babuska,et al.  Railway Track Circuit Fault Diagnosis Using Recurrent Neural Networks , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[27]  Kai-Jie Yang,et al.  Recurrent Fuzzy Neural Cerebellar Model Articulation Network Fault-Tolerant Control of Six-Phase Permanent Magnet Synchronous Motor Position Servo Drive , 2016, IEEE Transactions on Fuzzy Systems.