Journal of Green Energy Research and Innovation
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Voltage Sag Reduction by ANFIS in Wind Turbine Generation Units

Document Type : Research article

Authors

Saman Darvish Kermani 1
Ali Morsagh Dezfuli 2
Abdolreza Behvandi 3
Mehrdad Kankanan 3

1 GHD Advisory, Melbourne VIC 3000, Australia.

2 Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz 61357-85311, Iran.

3 Department of Electrical Engineering, Ramhormoz Branch, Islamic Azad University, Ramhormoz, Iran.

https://doi.org/10.61186/jgeri.1.3.49
Abstract
The Power Quality (PQ) issue refers to the occurrence of irregular voltage, current, or frequency that leads to failure or incorrect functioning of equipment used by end users. The PQ meter is utilized to monitor a diverse range of power supply characteristics, all of which possess the capacity to impact the effectiveness of both operational procedures and machinery. The dynamic voltage restorer (DVR) performs the role of a specialized power device employed to mitigate the voltage drop experienced at the terminal of a sensitive load. DVR can be controlled by various control designs. This work conducts a comparative analysis on a normally managed voltage system and a medium-power DVR controlled by a neural network (NN), fuzzy logic (FL), or adaptive neuro-fuzzy inference system (ANFIS) by utilizing an output voltage regulator. The identification and rapid compensation of voltage perturbations, such as voltage sag, are essential elements in monitoring and controlling DVRs. The conventional PI controller is commonly employed in regulating DVRs. While the traditional controller possesses certain merits, it is not free of limitations. One such downside pertains to its utilization of constant gains, which can impede its capability to provide optimal control performance in instances where system parameters undergo fluctuations. Possible solutions have been proposed to effectively tackle this issue, such as the use of NNs, FL, or ANFIS controllers. Furthermore, to attain both rapid dynamic response and robustness, a modified d-q converted three-phase voltage regulator was adopted. Instead of employing a conventional three-phase regulator, this particular regulator is operated by means of an NN, FL, ANFIS, or PI controller. The suggested voltage regulator offers a prompt solution for rectifying voltage irregularities, such as voltage sag, by promptly restoring the voltage to the nominal magnitude. The primary source of power adopted in this study is a wind turbine unit.

Graphical Abstract

Voltage Sag Reduction by ANFIS in Wind Turbine Generation Units

Highlights

 

  • Adoption of a modified d-q converted three-phase voltage regulator
  • Operation using an NN, FL, ANFIS, or PI controller instead of a three-phase regulator
  • Rectifying voltage irregularities by promptly restoring the voltage to the nominal magnitude

Keywords

Dynamic voltage restorer
Power quality
Neural network
Fuzzy logic
Adaptive neuro-fuzzy inference system
Voltage sags
Voltage swells

 

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The ethical issues, including plagiarism, informed consent, misconduct, data fabrication and/or falsification, double publication and/or submission, redundancy, have been completely observed by the authors.

 

Credit Authorship Contribution Statement

Saman Darvish Kermani: Conceptualization, Formal analysis, Investigation, Methodology, Resources, Roles/Writing - original draft. Ali Morsagh Dezfuli: Methodology, Resources, Roles/Writing - original draft. Abdolreza Behvandi: Conceptualization, Methodology, Supervision, Roles/Writing - original draft. Mehrdad Kankanan: Resources, Validation, Visualization, Roles/Writing - original draft.

 

Bibliography

 Saman Darvish Kermani received his PhD degree in 2016 from Shahid Chamran University of Ahvaz, Ahvaz, Iran in the field of electrical engineering. He is currently working at GHD Advisory Melbourne VIC 3000 Australia in the field of renewable energy. His main research interests include optimization, nature-inspired metaheuristic algorithms, islanding, microgrid, smart grid, power quality, modeling of electrical networks, and distributed renewable resources.

 Ali Morsagh Dezfuli was born in Dezful, Iran in 1992. He received the B.Sc. degree from the Jundi-Shapur University of Technology of Dezful, Dezful, Iran in 2015, the M.Sc. degree from the Shahid Rajaee Teacher Training University of Tehran, Tehran, Iran in 2017 and he is a PhD candidate at Shahid Chamran University of Ahvaz, Ahvaz, Iran all in Power Electrical Engineering. His research interests are: Voltage and Frequency Control of Microgrid, Power Electronics, FACTs Devices and Power System Protection.

 Abdolreza Behvandi was born in 1987 in Iran. He received his B.Sc., M.Sc., and Ph.D. degrees all in Electrical Engineering (Power Systems) in 2010, 2012, and 2019 from Isfahan University of Technology, Isfahan University, and Shahid Chamran University of Ahvaz, respectively. Currently, he is an Assistant Professor at Department of Electrical Engineering, Ramhormoz Branch, Islamic Azad University, Ramhormoz, Iran. His special interests are power system studies, power system protection, renewable energy, and microgrids.

 Mehrdad Kankanan, born on June 25, 1986, is an accomplished author and researcher in the field of Electrical Engineering. He holds an MSc (2011) and a Ph.D. (2018) in Electrical Engineering from Shahid Chamran University of Ahvaz, Iran. As an Assistant Professor in the Electrical Engineering Department at Islamic Azad University of Ramhormouz, Mehrdad Kankanan focuses his expertise on Semiconductor Devices, with a special emphasis on Solar Cells, Digital Circuits, and Power Electronics.

 

Citation

S. D. Kermani, A. Morsagh Dezfuli, R. Behvandi, and M. Kankanan," Voltage Sag Reduction by ANFIS in Wind Turbine Generation Units," Journal of Green Energy Research and Innovation, vol. 1, no. 3, pp. 49-76, 2024.

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Journal of Green Energy Research and Innovation
Volume 1, Issue 3
Summer 2024
Pages 49-76

  • Receive Date 01 January 2024
  • Revise Date 15 February 2024
  • Accept Date 17 February 2024
  • Publish Date 01 September 2024

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