Implementation of ANN Controller Based UPQC Integrated with Microgrid

Hina Mahar, Hafiz Mudasir Munir, Jahangir Badar Soomro, Faheem Akhtar, Rashid Hussain, Mohamed F. Elnaggar, Salah Kamel and Josep M. Guerrero
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Hina Mahar: Department of Electrical Engineering, Sukkur IBA University, Sukkur 65200, Pakistan
Hafiz Mudasir Munir: Department of Electrical Engineering, Sukkur IBA University, Sukkur 65200, Pakistan
Jahangir Badar Soomro: Department of Electrical Engineering, Sukkur IBA University, Sukkur 65200, Pakistan
Faheem Akhtar: Department of Electrical Engineering, Sukkur IBA University, Sukkur 65200, Pakistan
Rashid Hussain: Department of Electrical Engineering, Sukkur IBA University, Sukkur 65200, Pakistan
Mohamed F. Elnaggar: Department of Electrical Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, Al-Kharj 16273, Saudi Arabia
Salah Kamel: Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan 81542, Egypt
Josep M. Guerrero: The Villum Center for Research on Microgrids (CROM), AAU Energy, Aalborg University, 9220 Aalborg East, Denmark

Mathematics, 2022, vol. 10, issue 12, 1-24

Abstract: This study discusses how to increase power quality by integrating a unified power quality conditioner (UPQC) with a grid-connected microgrid for clean and efficient power generation. An Artificial Neural Network (ANN) controller for a voltage source converter-based UPQC is proposed to minimize the system’s cost and complexity by eliminating mathematical operations such as a-b-c to d-q-0 translation and the need for costly controllers such as DSPs and FPGAs. In this study, nonlinear unbalanced loads and harmonic supply voltage are used to assess the performance of PV-battery-UPQC using an ANN-based controller. Problems with voltage, such as sag and swell, are also considered. This work uses an ANN control system trained with the Levenberg-Marquardt backpropagation technique to provide effective reference signals and maintain the required dc-link capacitor voltage. In MATLAB/Simulink software, simulations of PV-battery-UPQC employing SRF-based control and ANN-control approaches are performed. The findings revealed that the proposed approach performed better, as presented in this paper. Furthermore, the influence of synchronous reference frame (SRF) and ANN controller-based UPQC on supply currents and the dc-link capacitor voltage response is studied. To demonstrate the superiority of the suggested controller, a comparison of percent THD in load voltage and supply current utilizing SRF-based control and ANN control methods is shown.

Keywords: maximum power point tracking; artificial neural network (ANN); UPQC; synchronous reference frame; total harmonic distortion (THD) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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