eprintid: 6899 rev_number: 11 eprint_status: archive userid: 2 dir: disk0/00/00/68/99 datestamp: 2024-12-09 23:30:22 lastmod: 2024-12-09 23:30:24 status_changed: 2024-12-09 23:30:22 type: article metadata_visibility: show creators_name: Mengistu, Epaphros creators_name: Khan, Baseem creators_name: Qasaymeh, Yazeed creators_name: Alghamdi, Ali S. creators_name: Zubair, Muhammad creators_name: Awan, Ahmed Bilal creators_name: Ashiq, Muhammad Gul Bahar creators_name: Ali, Samia Gharib creators_name: Mazas Pérez-Oleaga, Cristina creators_id: creators_id: creators_id: creators_id: creators_id: creators_id: creators_id: creators_id: creators_id: cristina.mazas@uneatlantico.es title: Utilization of Stockwell Transform, Support Vector Machine and D-STATCOM for the Identification, Classification and Mitigation of Power Quality Problems ispublished: pub subjects: uneat_eng divisions: uninimx_produccion_cientifica divisions: uneatlantico_produccion_cientifica divisions: unic_produccion_cientifica full_text_status: public keywords: current distortion; distribution static compensator; stockwell transform; support vector machine 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. date: 2023-03 publication: Sustainability volume: 15 number: 7 pagerange: 6007 id_number: doi:10.3390/su15076007 refereed: TRUE issn: 2071-1050 official_url: http://doi.org/10.3390/su15076007 access: open language: en citation: Artículo Materias > Ingeniería Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Europea del Atlántico > Investigación > Artículos y libros Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés 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. metadata Mengistu, Epaphros; Khan, Baseem; Qasaymeh, Yazeed; Alghamdi, Ali S.; Zubair, Muhammad; Awan, Ahmed Bilal; Ashiq, Muhammad Gul Bahar; Ali, Samia Gharib y Mazas Pérez-Oleaga, Cristina mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, cristina.mazas@uneatlantico.es (2023) Utilization of Stockwell Transform, Support Vector Machine and D-STATCOM for the Identification, Classification and Mitigation of Power Quality Problems. Sustainability, 15 (7). p. 6007. ISSN 2071-1050 document_url: http://repositorio.uneatlantico.es/id/eprint/6899/1/sustainability-15-06007.pdf