Fault Identification of UHVDC Transmission Based on DF-AD and Ensemble Learning
High-resistance ground faults are difficult to detect with existing ultrahigh voltage direct current (UHVDC) transmission fault detection systems because of their low sensitivity. To address this challenge, a straightforward mathematical method has been proposed for fault detection in UHVDC system b...
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | International Transactions on Electrical Energy Systems |
| Online Access: | http://dx.doi.org/10.1155/2024/2900648 |
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| author | Qingsheng Zhao Xinyu Yang Xiaoqing Han Dingkang Liang Xuping Wang |
| author_facet | Qingsheng Zhao Xinyu Yang Xiaoqing Han Dingkang Liang Xuping Wang |
| author_sort | Qingsheng Zhao |
| collection | DOAJ |
| description | High-resistance ground faults are difficult to detect with existing ultrahigh voltage direct current (UHVDC) transmission fault detection systems because of their low sensitivity. To address this challenge, a straightforward mathematical method has been proposed for fault detection in UHVDC system based on the downsampling factor (DF) and approximation derivatives (AD). The signals at multiple sampling frequencies were analysed using the DF, and the AD approach was used to generate various levels of detail and approximation coefficients. Initially, the signals were processed with different DF values. The first, second, and third order derivatives of the generated signals were calculated by the AD method. Next, the entropy features of these signals were computed, and the Random Forest-Recursive feature elimination with cross-validation (RF-RFECV) algorithm was used to select a high-quality feature subset. Finally, an ensemble classifier consisting of Light Gradient Boosting Machine (LightGBM), K Nearest Neighbor (KNN), and Naive Bayes (NB) classifiers was utilized to identify UHVDC faults. The MATLAB/Simulink simulation software was used to develop a ±800 kV UHVDC transmission line model and perform simulation experiments with various fault locations and types. Based on the experiments, it has been established that the suggested approach is highly precise in detecting several faults on UHVDC transmission lines. The method is capable of accurately identifying low or high resistance faults, irrespective of their incidence, and is remarkably resistant to transitional resistance. Furthermore, it exhibits excellent performance in identifying faults using a small sample size and is highly reliable. |
| format | Article |
| id | doaj-art-f8b3efafd42a4700817131af47cf6581 |
| institution | Kabale University |
| issn | 2050-7038 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Transactions on Electrical Energy Systems |
| spelling | doaj-art-f8b3efafd42a4700817131af47cf65812025-02-03T01:31:53ZengWileyInternational Transactions on Electrical Energy Systems2050-70382024-01-01202410.1155/2024/2900648Fault Identification of UHVDC Transmission Based on DF-AD and Ensemble LearningQingsheng Zhao0Xinyu Yang1Xiaoqing Han2Dingkang Liang3Xuping Wang4College of Electrical and Power EngineeringCollege of Electrical and Power EngineeringCollege of Electrical and Power EngineeringCollege of Electrical and Power EngineeringCollege of Electrical and Power EngineeringHigh-resistance ground faults are difficult to detect with existing ultrahigh voltage direct current (UHVDC) transmission fault detection systems because of their low sensitivity. To address this challenge, a straightforward mathematical method has been proposed for fault detection in UHVDC system based on the downsampling factor (DF) and approximation derivatives (AD). The signals at multiple sampling frequencies were analysed using the DF, and the AD approach was used to generate various levels of detail and approximation coefficients. Initially, the signals were processed with different DF values. The first, second, and third order derivatives of the generated signals were calculated by the AD method. Next, the entropy features of these signals were computed, and the Random Forest-Recursive feature elimination with cross-validation (RF-RFECV) algorithm was used to select a high-quality feature subset. Finally, an ensemble classifier consisting of Light Gradient Boosting Machine (LightGBM), K Nearest Neighbor (KNN), and Naive Bayes (NB) classifiers was utilized to identify UHVDC faults. The MATLAB/Simulink simulation software was used to develop a ±800 kV UHVDC transmission line model and perform simulation experiments with various fault locations and types. Based on the experiments, it has been established that the suggested approach is highly precise in detecting several faults on UHVDC transmission lines. The method is capable of accurately identifying low or high resistance faults, irrespective of their incidence, and is remarkably resistant to transitional resistance. Furthermore, it exhibits excellent performance in identifying faults using a small sample size and is highly reliable.http://dx.doi.org/10.1155/2024/2900648 |
| spellingShingle | Qingsheng Zhao Xinyu Yang Xiaoqing Han Dingkang Liang Xuping Wang Fault Identification of UHVDC Transmission Based on DF-AD and Ensemble Learning International Transactions on Electrical Energy Systems |
| title | Fault Identification of UHVDC Transmission Based on DF-AD and Ensemble Learning |
| title_full | Fault Identification of UHVDC Transmission Based on DF-AD and Ensemble Learning |
| title_fullStr | Fault Identification of UHVDC Transmission Based on DF-AD and Ensemble Learning |
| title_full_unstemmed | Fault Identification of UHVDC Transmission Based on DF-AD and Ensemble Learning |
| title_short | Fault Identification of UHVDC Transmission Based on DF-AD and Ensemble Learning |
| title_sort | fault identification of uhvdc transmission based on df ad and ensemble learning |
| url | http://dx.doi.org/10.1155/2024/2900648 |
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