Utilization of Stockwell Transform, Support Vector Machine and D-STATCOM for the Identification, Classification and Mitigation of Power Quality Problems

Epaphros Mengistu, Baseem Khan, Yazeed Qasaymeh (), Ali S. Alghamdi, Muhammad Zubair, Ahmed Bilal Awan, Muhammad Gul Bahar Ashiq, Samia Gharib Ali and Cristina Mazas Pérez-Oleaga
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Epaphros Mengistu: Department of Electrical and Computer Engineering, Hawassa University, Hawassa 1530, Ethiopia
Baseem Khan: Department of Electrical and Computer Engineering, Hawassa University, Hawassa 1530, Ethiopia
Yazeed Qasaymeh: Department of Electrical Engineering, College of Engineering, Majmaah University, Al-Majmaah 11952, Saudi Arabia
Ali S. Alghamdi: Department of Electrical Engineering, College of Engineering, Majmaah University, Al-Majmaah 11952, Saudi Arabia
Muhammad Zubair: Department of Electrical Engineering, College of Engineering, Majmaah University, Al-Majmaah 11952, Saudi Arabia
Ahmed Bilal Awan: Department of Electrical and Computer Engineering, College of Engineering and Information Technology, Ajman University, Ajman 20550, United Arab Emirates
Muhammad Gul Bahar Ashiq: Department of Physics, College of Science, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
Samia Gharib Ali: Department of Electrical Power and Machines, Kafrelsheikh University, Kafrelsheikh 33516, Egypt
Cristina Mazas Pérez-Oleaga: Department of Project Management, Universidad Internacional Iberoamericana, Campeche 24560, Mexico

Sustainability, 2023, vol. 15, issue 7, 1-21

Abstract: Power Quality (PQ) has become a significant issue in power networks. Power quality disturbances must be precisely and appropriately identified. This activity involves identifying, classifying, and mitigating power quality problems. A case study of the Awada industrial zone in Ethiopia is taken into consideration to show the practical applicability of the proposed work. It is found that the current harmonic distortion levels exceed the restrictions with a maximum percentage Total Harmonic Distortion of Current (THDI) value of up to 23.09%. The signal processing technique, i.e., Stockwell Transform (ST) is utilized for the identification of power quality issues, and it covers the most important and common power quality issues. The Support Vector Machine (SVM) method is used to categorize power quality issues, which enhances the classification procedure. The ST scored better in terms of accuracy than the Wavelet Transform (WT), Fourier Transform (FT), and Hilbert Transform (HT), obtaining 97.1%, as compared to 91.08%, 88.91%, and 86.8%, respectively. The maximum classification accuracy of SVM was 98.3%. To lower the current level of harmonic distortion in the industrial sector, a Distribution Static Compensator (D-STATCOM) is developed in the current control mode. To evaluate the performance of the D-STATCOM, the performance of the distribution network with and without D-STATCOM is simulated. The simulation results show that THDI is reduced to 4.36% when the suggested D-STATCOM is applied in the system.

Keywords: current distortion; distribution static compensator; stockwell transform; support vector machine (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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