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

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
BLOCK DIAGRAM:
Fig. 1. Shunt APF system configuration.
CONTROL BLOCK DIAGRAM:
Fig. 2. Shunt APF control template using either MNN or ADALINE structures

3. 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
EXPECTED SIMULATION RESULTS:
Fig. 3. Simulation result for the desired signal (fundamental) extraction using feed-forward MNN and ADALINE for
two input patterns.
Fig. 4. The dynamic performance of the feed-forward MNN shunt APF for a trained load scenario.

4. 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
Fig. 8. The dynamic performance of the feed-forward MNN shunt APF for untrained load scenario.
Fig. 9. The dynamic performance of the ADALINE shunt APF.

5. 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
Fig. 10. ADALINE implementation in DS 1103: plot of the variables, _!, _, __E _
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
the LMS algorithm, while the MNN is trained offline using the SCG back propagation 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.

6. 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
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.