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 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.
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