A comparative study of harmonic currents extraction by simulation and implementation

The aim of the present work is to obtain a perfect compensation by extracting accurate harmonic currents. The objective is to avoid the consequences due to the presence of disturbances in the power system. A comparative study of harmonic currents extraction by simulation and implementation is carried out for two different techniques. The first technique is based on the instantaneous powers, taking advantage of the relationship between current and the power transformed from the supply source to the loads. The second is based on ADALINE neural network. The neural method can estimate the harmonic terms individually and online, therefore, the APF can realise a selective compensation. The developed architectures are validated by computer simulation and experimental tests. The algorithms are implemented in the dSPACE Board in order to show the effectiveness and capability of each technique. The results have demonstrated that the speed and the accuracy of the ADALINE can improve greatly the performances of active power filters.

[1]  Patrice Wira,et al.  Distortions identification and compensation based on artificial neural networks using symmetrical components of the voltages and the currents , 2009 .

[2]  Mahmood Joorabian,et al.  Harmonic estimation in a power system using a novel hybrid Least Squares-Adaline algorithm , 2009 .

[3]  Naimish Zaveri,et al.  Control strategies for harmonic mitigation and power factor correction using shunt active filter under various source voltage conditions , 2012 .

[4]  Bernard Widrow,et al.  30 years of adaptive neural networks: perceptron, Madaline, and backpropagation , 1990, Proc. IEEE.

[5]  Guan-Chyun Hsieh,et al.  Phase-locked loop techniques. A survey , 1996, IEEE Trans. Ind. Electron..

[6]  E. Akpinar,et al.  Evaluation of reference current extraction methods for DSP implementation in active power filters , 2009 .

[7]  Qiaofu Chen,et al.  A novel active power filter with fundamental magnetic flux compensation , 2004, IEEE Transactions on Power Delivery.

[8]  S. Moorthi,et al.  Implementation of hybrid ANN-PSO algorithm on FPGA for harmonic estimation , 2012, Eng. Appl. Artif. Intell..

[9]  A. Karami,et al.  Power system transient stability margin estimation using neural networks , 2011 .

[10]  Samarjit Sengupta,et al.  A self-synchronized ADALINE network for on-line tracking of power system harmonics , 2011 .

[11]  Mukesh Kumar Pathak,et al.  Comparison of adaptive Neuro-Fuzzy-based space-vector modulation for two-level inverter , 2012 .

[12]  Salah Saad,et al.  Fuzzy logic controller for three-level shunt active filter compensating harmonics and reactive power , 2009 .

[13]  Xu Wei-hua ADALINE and Its Application in Power Quality Disturbances Detection and Frequency Tracking , 2005 .

[14]  Swapan Kumar Goswami,et al.  A wavelet based novel method for the detection of harmonic sources in power systems , 2012 .

[15]  B. Widrow,et al.  Adaptive inverse control , 1987, Proceedings of 8th IEEE International Symposium on Intelligent Control.

[16]  Hirofumi Akagi,et al.  Active Harmonic Filters , 2005, Proceedings of the IEEE.

[17]  Anup Kumar Panda,et al.  Real-time implementation of PI and fuzzy logic controllers based shunt active filter control strategies for power quality improvement , 2012 .

[18]  M. Joorabian,et al.  A novel frequency tracking method based on complex adaptive linear neural network state vector in power systems , 2009 .

[19]  Patrice Wira,et al.  A Unified Artificial Neural Network Architecture for Active Power Filters , 2007, IEEE Transactions on Industrial Electronics.

[20]  Bimal K. Bose,et al.  Neural Network Applications in Power Electronics and Motor Drives—An Introduction and Perspective , 2007, IEEE Transactions on Industrial Electronics.

[21]  Hirofumi Akagi,et al.  Instantaneous power theory and applications to power conditioning , 2007 .

[22]  K. Al-Haddad,et al.  Sliding mode control of 3-phase 3-wire shunt active filter in the dq frame , 2001, Canadian Conference on Electrical and Computer Engineering 2001. Conference Proceedings (Cat. No.01TH8555).

[23]  P. Dash,et al.  An adaptive neural network approach for the estimation of power system frequency , 1997 .

[24]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .