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INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016 open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15 |
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| Paper Title: | Machine Learning-Based Model to Enhance Smart Grid Reliability |
| Authors Name: | Bhushan Prataprao Patil , Salunkhe Umakant Manohar , Marathe Sushant Sanjay , Patil Jayesh Navnit , Bhavsar Rushikesh Rajendra, Patil Ashwin Sanjay |
| Unique Id: | IJSDR2306063 |
| Published In: | Volume 8 Issue 6, June-2023 |
| Abstract: | The electrical power system is a complex network of interconnected components that can experience disturbances or electrical faults. One challenging task is to detect these faults and determine their underlying causes. In this paper, a new approach using semi-supervised machine learning techniques is presented for fault detection and classification on transmission lines. The approach employs Decision Tree (DT), Random Forest (RF), Logistic Regression and SVM classifiers, which are designed to analyze and classify different types of faults. To extract relevant information from the fault current and voltage signals, a technique called Discrete Wavelet Transform (DWT) is applied for feature extraction. The Decision Tree classifier makes decisions based on constructing an optimal classification tree. It uses a set of rules to determine the most appropriate fault classification. The Random Forest and Logistic Regression also contribute to the fault analysis process. To evaluate the performance of the proposed system, various datasets are used. The results obtained from the two classifiers are compared based on their accuracy. This helps identify the most suitable technique for fault analysis in the given context. This paper presents an innovative approach to classify faults in transmission lines using machine learning techniques. By applying the Decision Tree, Random Forest, and Logistic Regression, along with the feature extraction capabilities of the Discrete Wavelet Transform. The Random Forest and Decision Tree improve the accuracy and effectiveness of fault analysis in the electrical power system |
| Keywords: | - |
| Cite Article: | "Machine Learning-Based Model to Enhance Smart Grid Reliability", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.8, Issue 6, page no.433 - 436, June-2023, Available :http://www.ijsdr.org/papers/IJSDR2306063.pdf |
| Downloads: | 000346983 |
| Publication Details: | Published Paper ID: IJSDR2306063 Registration ID:207140 Published In: Volume 8 Issue 6, June-2023 DOI (Digital Object Identifier): Page No: 433 - 436 Publisher: IJSDR | www.ijsdr.org ISSN Number: 2455-2631 |
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