Three-Phase Shunt Active Power Filter for Power Improvement Quality using Sliding Mode Controller


In this paper, experimental study of Sliding Mode Controller (SMC) DC bus voltage of three phase shunt active power filter (APF), to improve power quality by compensating harmonics and reactive power required by nonlinear load is proposed. The algorithm used to identify the reference currents is based on the Self Tuning Filter (STF). For generation of the pulse switching of the IGBTs inverter the hysteresis current controller is used, implemented into an analogue card. Finally, various experimental results are presented under steady state and transient conditions.


  1. Shunt Active Power Filter (APF)
  2. Total Harmonic Distortion (THD)
  3. Sliding Mode Controller (SMC)
  4. Self Tuning Filter (STF)




Fig. 1: The basic compensation principle of the shunt APF.




Fig. 2. Experimental APF results: load current iL (A), filter current iF (A)

and source current iS (A). Ch1 to Ch4 scale: 5 A/div. Time scale: 20 ms/div.


Fig. 3. Experimental APF results: load current iL (A), filter current iF (A),

source current iS (A) and source voltage Vs (V). Ch1 and Ch3 scale: 5 A/div;

Ch2 scale: 100 V/div;Ch4 scale: 80 V/div; Time scale: 10 ms/div.

Figure 4. Experimental APF results : load current iL(A), filter current iF(A) ,

source current iS(A) and DC voltage Vdc(V). Ch1,Ch3 and Ch4 scale: 10

A/div. Ch2 scale: 100 V/div. Time scale: 20 ms/div.

Figure 5. Experimental APF results: DC voltage Vdc (V) and DC reference

voltage V*dc (V). Ch1 and Ch2 scale: 100 V/div. Time scale: 1s/div


The control of the shunt Active Power Filter was divided in three parts, the first one realized by the dSPACE system to generate the reference currents, the second one achieved by an analogue card for the switching pattern generation, implementing a hysteresis current controller and the third party use a sliding mode controller SMC. A SMC controlled shunt active power filter has been studied to improve the power quality by compensating both harmonics and reactive power requirement of the nonlinear load. The performance of the SMC controller has been developed in real time process and successfully tested in the laboratory The results of experiment study of APF control technique presented in this paper are found quite satisfactory to eliminate harmonics and reactive power components from utility current. The shunt APF presented in this paper for the compensation of harmonic current components in non-linear load was effective for harmonic isolation and keeping the utility supply line current sinusoidal. The validity of this technique was proved on the basis of experiment results. The APF is found effective to meet IEEE- 519-1992 standard recommendations on harmonics levels.


[1] Chaoui; J.P.Gaubert; F.Krim; G.Champenois, “PI Controlled Threephase Shunt Active Power Filter for Power Quality Improvement” A. “Electric Power Components and Systems, 1532-5016, Volume 35, Issue 12, 2007, Pages 1331 – 1344.

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Application of Artificial Neural Networks for Shunt Active Power Filter Control


 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.



  1. Adaptive Linear Neuron (ADALINE)
  2. Artificial neural network (ANN)
  3. Feed-forward multilayer neural network (MNN)
  4. 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.


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