Artificial neural network (ANN) is becoming an attractive guess and reversion method in many control use 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 use.
Adaptive Linear Neuron (ADALINE) and feed-forward multilayer neural network (MNN) are the most commonly used ANN method to extract fundamental and/or harmonic components present in the nonlinear currents. This paper aims to provide an in-depth understanding on manage ADALINE and feed-forward MNN-based control algorithms for shunt APF.
A step-by-step process to implement these ANN-based method in MATLAB/Simulink situation is supply. Furthermore, a detailed analysis on the work, limitation, and advantages of both methods is presented in the paper. The study is supported by conducting both simulation and experimental validations.
- Adaptive Linear Neuron (ADALINE)
- Artificial neural network (ANN)
- Feed-forward multilayer neural network (MNN)
- Shunt active power filter (APF)
Fig. 1. Shunt APF system configuration.
Fig. 2. ADALINE used to extract the fundamental active load current amplitude.
Fig. 3. Shunt APF control template using either MNN or ADALINE structures
Fig. 4. Dynamic performance of the feed-forward MNN shunt APF for a trained load scenario.
Fig. 5. Dynamic performance of the feed-forwardMNNshunt APF for untrained load scenario.
Fig. 6. Dynamic performance of the ADALINE shunt APF.
In this paper, two broadly used ANN-based shunt APF control method are investigated: 1) the ADALINE; and 2) the feed forward MNN. A simple step-by-step process is provided to implement each method in MATLAB/Simulink situation.
The ADALINE is trained online by the LMS algorithm, while the MNN is trained offline using the SCG back procreation algorithm to extract the fundamental load active current magnitude. The work of these ANN-based shunt APF controllers is decide through detailed simulation and experimental studies.
Based on the study manage in this paper, it is noticed 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 recompense from the dc-link PI regulator. On contrary, the online adaptiveness of ADALINE makes it applicable to any load condition.
 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.
 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.
 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.
 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 Proc. Conf. Rec. INDIN, 2012, pp. 869–874.
 A. Hamadi, S. Rahmani, and 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.