High‐Impedance Fault Detection in Distribution Networks Based on Support Vector Machine and Wavelet Transform Approach (Case Study: Markazi Province of Iran)
ABSTRACT High impedance faults (HIFs) can lead to crucial damage to the utility grid, such as the risk of fire in material assets, electricity supply interruptions, and long service restoration times. Due to their low current magnitude, conventional protective equipment, such as overcurrent relays,...
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Wiley
2025-03-01
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| Series: | Energy Science & Engineering |
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| Online Access: | https://doi.org/10.1002/ese3.2056 |
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| author | Mohammad Sadegh Attar Mohammad Reza Miveh |
| author_facet | Mohammad Sadegh Attar Mohammad Reza Miveh |
| author_sort | Mohammad Sadegh Attar |
| collection | DOAJ |
| description | ABSTRACT High impedance faults (HIFs) can lead to crucial damage to the utility grid, such as the risk of fire in material assets, electricity supply interruptions, and long service restoration times. Due to their low current magnitude, conventional protective equipment, such as overcurrent relays, cannot detect these faults. Alternatively, the waveform and variation range of current in HIFs are similar to other phenomena, such as linear and nonlinear load changes and capacitor banks. This paper employs a support vector machine (SVM) classification algorithm that demonstrates reliable accuracy and discrete wavelet transform (DWT) in HIF detection. First, the data set containing measured current signals of HIFs is collected to implement this approach. Then, DWT decomposes it to extract the features of each sample in the data set. The extracted features from this part are used as input to the SVM classification algorithm. The proposed idea is initially implemented on the IEEE 34‐bus distribution test network. The proposed method achieves high capability and accuracy in detecting high‐impedance faults. The proposed method is also applied to a real power distribution network in Markazi Province of Iran, yielding satisfactory results. EMTP‐RV simulation software is used to simulate and evaluate the proposed method for power network modeling. Moreover, MATLAB software is used for feature extraction, and Python programming language in Google Colab and Spyder environment is applied to implement the SVM algorithm. The simulation results confirm the high accuracy of the suggested method. The main criteria obtained by the proposed method include accuracy, sensitivity, specificity, precision, F‐score, and Dice, which are 99.581%, 98.684%, 100%, 100%, 99.338%, and 99.338%, respectively, for the test network, and 97.94%, 93.45%, 100%, 100%, 96.614%, and 96.618%, respectively, for the real power distribution network. |
| format | Article |
| id | doaj-art-4ad75c5b3d684f2e82c86653b7c6444a |
| institution | OA Journals |
| issn | 2050-0505 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Energy Science & Engineering |
| spelling | doaj-art-4ad75c5b3d684f2e82c86653b7c6444a2025-08-20T02:04:37ZengWileyEnergy Science & Engineering2050-05052025-03-011331171118310.1002/ese3.2056High‐Impedance Fault Detection in Distribution Networks Based on Support Vector Machine and Wavelet Transform Approach (Case Study: Markazi Province of Iran)Mohammad Sadegh Attar0Mohammad Reza Miveh1Department of Electrical Engineering Tafresh University Tafresh IranDepartment of Electrical Engineering Tafresh University Tafresh IranABSTRACT High impedance faults (HIFs) can lead to crucial damage to the utility grid, such as the risk of fire in material assets, electricity supply interruptions, and long service restoration times. Due to their low current magnitude, conventional protective equipment, such as overcurrent relays, cannot detect these faults. Alternatively, the waveform and variation range of current in HIFs are similar to other phenomena, such as linear and nonlinear load changes and capacitor banks. This paper employs a support vector machine (SVM) classification algorithm that demonstrates reliable accuracy and discrete wavelet transform (DWT) in HIF detection. First, the data set containing measured current signals of HIFs is collected to implement this approach. Then, DWT decomposes it to extract the features of each sample in the data set. The extracted features from this part are used as input to the SVM classification algorithm. The proposed idea is initially implemented on the IEEE 34‐bus distribution test network. The proposed method achieves high capability and accuracy in detecting high‐impedance faults. The proposed method is also applied to a real power distribution network in Markazi Province of Iran, yielding satisfactory results. EMTP‐RV simulation software is used to simulate and evaluate the proposed method for power network modeling. Moreover, MATLAB software is used for feature extraction, and Python programming language in Google Colab and Spyder environment is applied to implement the SVM algorithm. The simulation results confirm the high accuracy of the suggested method. The main criteria obtained by the proposed method include accuracy, sensitivity, specificity, precision, F‐score, and Dice, which are 99.581%, 98.684%, 100%, 100%, 99.338%, and 99.338%, respectively, for the test network, and 97.94%, 93.45%, 100%, 100%, 96.614%, and 96.618%, respectively, for the real power distribution network.https://doi.org/10.1002/ese3.2056discrete wavelet transformdistribution networkshigh impedance faultmachine learningsupport vector machine |
| spellingShingle | Mohammad Sadegh Attar Mohammad Reza Miveh High‐Impedance Fault Detection in Distribution Networks Based on Support Vector Machine and Wavelet Transform Approach (Case Study: Markazi Province of Iran) Energy Science & Engineering discrete wavelet transform distribution networks high impedance fault machine learning support vector machine |
| title | High‐Impedance Fault Detection in Distribution Networks Based on Support Vector Machine and Wavelet Transform Approach (Case Study: Markazi Province of Iran) |
| title_full | High‐Impedance Fault Detection in Distribution Networks Based on Support Vector Machine and Wavelet Transform Approach (Case Study: Markazi Province of Iran) |
| title_fullStr | High‐Impedance Fault Detection in Distribution Networks Based on Support Vector Machine and Wavelet Transform Approach (Case Study: Markazi Province of Iran) |
| title_full_unstemmed | High‐Impedance Fault Detection in Distribution Networks Based on Support Vector Machine and Wavelet Transform Approach (Case Study: Markazi Province of Iran) |
| title_short | High‐Impedance Fault Detection in Distribution Networks Based on Support Vector Machine and Wavelet Transform Approach (Case Study: Markazi Province of Iran) |
| title_sort | high impedance fault detection in distribution networks based on support vector machine and wavelet transform approach case study markazi province of iran |
| topic | discrete wavelet transform distribution networks high impedance fault machine learning support vector machine |
| url | https://doi.org/10.1002/ese3.2056 |
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