Application of Artificial Neural Networks for Shunt APF Control
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This document discusses the application of artificial neural networks (ANN) for controlling shunt active power filters (APF), focusing on adaptive linear neuron (adaline) and feed-forward multilayer neural network (mnn) techniques. It presents a step-by-step implementation procedure in the MATLAB/Simulink environment and analyzes the performance of both methods, concluding that adaline offers superior adaptability compared to the mnn. The study is validated through simulations and experiments, highlighting the limitations and benefits of each approach.
![ELECTRICAL PROJECTS USING MATLAB/SIMULINK
Gmail: asokatechnologies@gmail.com, Website: http://www.asokatechnologies.in
0-9347143789/9949240245
For Simulation Results of the project Contact Us
Gmail: asokatechnologies@gmail.com, Website: http://www.asokatechnologies.in
0-9347143789/9949240245
the LMS algorithm, while the MNN is trained offline using the SCG backpropagation algorithm
to extract the fundamental load active current magnitude. The performance of these ANN based
shunt APF controllers is evaluated through detailed simulation and experimental studies. Based
on the study conducted in this paper, it is observed that the ADALINE based control technique
performs better than the feed-forward MNN. For untrained load scenario, the feed-forward MNN
fails to extract the fundamental component, resulting in overcompensation from the dc link PI
regulator. On contrary, the online adaptiveness of ADALINE makes it applicable to any load
condition.
REFERENCES:
[1] P. Kanjiya, V. Khadkikar, and H. H. Zeineldin, “A Noniterative Optimized Algorithm for
Shunt Active Power Filter Under Distorted and Unbalanced Supply Voltages,” IEEE Trans. Ind.
Electron., vol.60, no.12, pp.5376,5390, Dec. 2013.
[2] B. Singh, K. Al-Haddad, and A. Chandra, “A review of active filters for power quality
improvement,” IEEE Trans. Ind. Electron., vol.46, no.5, pp.960-971, Oct 1999.
[3] M. Popescu, A. Bitoleanu, and V. Suru, “A DSP-Based Implementation of the p-q Theory in
Active Power Filtering Under Nonideal Voltage Conditions,” IEEE Trans. Ind. Informat., vol.9,
no.2, pp.880,889, May 2013.
[4] V. Silva, J. G. Pinto, J. Cabral, J. L. Afonso, and A. Tavares, “Real time digital control
system for a single-phase shunt active power filter,” in Conf. Rec. INDIN, 2012, pp.869,874.
[5] A. Hamadi, S. Rahmani, K. Al-Haddad, “Digital Control of a Shunt Hybrid Power Filter
Adopting a Nonlinear Control Approach,” IEEE Trans. Ind. Informat., vol.9, no.4,
pp.2092,2104, Nov. 2013.](https://image.slidesharecdn.com/applicationofartificialneuralnetworksfor-210710111835/85/Application-of-Artificial-Neural-Networks-for-Shunt-APF-Control-2-320.jpg)
Application of Artificial Neural Networks for Shunt APF Control
- 1. ELECTRICAL PROJECTS USING MATLAB/SIMULINK Gmail: asokatechnologies@gmail.com, Website: http://www.asokatechnologies.in 0-9347143789/9949240245 For Simulation Results of the project Contact Us Gmail: asokatechnologies@gmail.com, Website: http://www.asokatechnologies.in 0-9347143789/9949240245 Application of Artificial Neural Networks for Shunt APF Control ABSTRACT: Artificial Neural Network (ANN) is becoming an attractive estimation and regression technique in many control applications due to its parallel computing nature and high learning capability. There has been a lot of effort in employing the ANN in shunt active power filter (APF) control applications. Adaptive Linear Neuron (ADALINE) and feed-forward Multilayer Neural Network (MNN) are the most commonly used ANN techniques to extract fundamental and/or harmonic components present in the non-linear currents. This paper aims to provide an in-depth understanding on realizing ADALINE and feed-forward MNN based control algorithms for shunt APF. A step-by-step procedure to implement these ANN based techniques, in Matlab/ Simulink environment, is provided. Furthermore, a detailed analysis on the performance, limitation and advantages of both methods is presented in the paper. The study is supported by conducting both simulation and experimental validations. KEYWORDS: 1. Shunt APF 2. ANN 3. ADALINE 4. Feed-forward MNN SOFTWARE: MATLAB/SIMULINK CONCLUSION: In this paper, two widely used ANN based shunt APF control strategies, namely the ADALINE and feed-forward MNN, are investigated. A simple step by step procedure is provided to implement each method in Matlab/Simulink environment. The ADALINE is trained online by
- 2. ELECTRICAL PROJECTS USING MATLAB/SIMULINK Gmail: asokatechnologies@gmail.com, Website: http://www.asokatechnologies.in 0-9347143789/9949240245 For Simulation Results of the project Contact Us Gmail: asokatechnologies@gmail.com, Website: http://www.asokatechnologies.in 0-9347143789/9949240245 the LMS algorithm, while the MNN is trained offline using the SCG backpropagation algorithm to extract the fundamental load active current magnitude. The performance of these ANN based shunt APF controllers is evaluated through detailed simulation and experimental studies. Based on the study conducted in this paper, it is observed that the ADALINE based control technique performs better than the feed-forward MNN. For untrained load scenario, the feed-forward MNN fails to extract the fundamental component, resulting in overcompensation from the dc link PI regulator. On contrary, the online adaptiveness of ADALINE makes it applicable to any load condition. REFERENCES: [1] P. Kanjiya, V. Khadkikar, and H. H. Zeineldin, “A Noniterative Optimized Algorithm for Shunt Active Power Filter Under Distorted and Unbalanced Supply Voltages,” IEEE Trans. Ind. Electron., vol.60, no.12, pp.5376,5390, Dec. 2013. [2] B. Singh, K. Al-Haddad, and A. Chandra, “A review of active filters for power quality improvement,” IEEE Trans. Ind. Electron., vol.46, no.5, pp.960-971, Oct 1999. [3] M. Popescu, A. Bitoleanu, and V. Suru, “A DSP-Based Implementation of the p-q Theory in Active Power Filtering Under Nonideal Voltage Conditions,” IEEE Trans. Ind. Informat., vol.9, no.2, pp.880,889, May 2013. [4] V. Silva, J. G. Pinto, J. Cabral, J. L. Afonso, and A. Tavares, “Real time digital control system for a single-phase shunt active power filter,” in Conf. Rec. INDIN, 2012, pp.869,874. [5] A. Hamadi, S. Rahmani, K. Al-Haddad, “Digital Control of a Shunt Hybrid Power Filter Adopting a Nonlinear Control Approach,” IEEE Trans. Ind. Informat., vol.9, no.4, pp.2092,2104, Nov. 2013.