Enhancing Power Grid Reliability With Machine Learning and Auxiliary Classifier Generative Adversarial Networks: A Study on Fault Detection Using the Georgia Electric System Load Dataset
Power networks are vital to society, yet service outages and faults can have devastating consequences. This study introduces a novel integration of machine learning and data augmentation techniques for fault detection and classification, addressing gaps in data diversity and imbalance. Unlike tradit...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10818412/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841557054972493824 |
|---|---|
| author | Hafeez Ur Rehman Siddiqui Robert Brown Adil Ali Saleem Muhammad Amjad Raza Sandra Dudley |
| author_facet | Hafeez Ur Rehman Siddiqui Robert Brown Adil Ali Saleem Muhammad Amjad Raza Sandra Dudley |
| author_sort | Hafeez Ur Rehman Siddiqui |
| collection | DOAJ |
| description | Power networks are vital to society, yet service outages and faults can have devastating consequences. This study introduces a novel integration of machine learning and data augmentation techniques for fault detection and classification, addressing gaps in data diversity and imbalance. Unlike traditional approaches, the research utilizes an Auxiliary Classifier Generative Adversarial Network (ACGAN) to generate synthetic data representative of underrepresented fault types, enhancing model training and performance. By extracting both spectral and statistical features from the Grid Event Signature Library (GESL) dataset, a comprehensive representation of power system signals is achieved. A comparative evaluation of models including Decision Trees (DT), Random Forest (RF), Extra Tree Classifier (ETC), Gradient Boosting Classifier (GBC), and K-Nearest Neighbors (KNN) revealed the Extra Tree Classifier achieved the highest testing accuracy of 93.85%. The methodologies demonstrated scalability by using a dataset augmented to 9,000 samples and validated robustness through 10-fold cross-validation with a standard deviation of 0.00659. These results highlight the proposed framework’s potential for real-world implementation in modern power grids, offering enhanced fault prediction and resilience. This research establishes a pathway for integrating advanced data augmentation and machine learning techniques into operational power grid systems, ensuring stability and reliability. |
| format | Article |
| id | doaj-art-e1ae38b544434592abd7764fdca0dc4f |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e1ae38b544434592abd7764fdca0dc4f2025-01-07T00:02:30ZengIEEEIEEE Access2169-35362025-01-01132463247310.1109/ACCESS.2024.352406110818412Enhancing Power Grid Reliability With Machine Learning and Auxiliary Classifier Generative Adversarial Networks: A Study on Fault Detection Using the Georgia Electric System Load DatasetHafeez Ur Rehman Siddiqui0https://orcid.org/0000-0003-0671-2060Robert Brown1Adil Ali Saleem2https://orcid.org/0000-0003-2468-8471Muhammad Amjad Raza3https://orcid.org/0000-0001-8881-1307Sandra Dudley4https://orcid.org/0000-0002-6431-5357Institute of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Punjab, PakistanBioengineering Research Centre, School of Engineering, London South Bank University, London, U.K.Institute of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Punjab, PakistanInstitute of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Punjab, PakistanBioengineering Research Centre, School of Engineering, London South Bank University, London, U.K.Power networks are vital to society, yet service outages and faults can have devastating consequences. This study introduces a novel integration of machine learning and data augmentation techniques for fault detection and classification, addressing gaps in data diversity and imbalance. Unlike traditional approaches, the research utilizes an Auxiliary Classifier Generative Adversarial Network (ACGAN) to generate synthetic data representative of underrepresented fault types, enhancing model training and performance. By extracting both spectral and statistical features from the Grid Event Signature Library (GESL) dataset, a comprehensive representation of power system signals is achieved. A comparative evaluation of models including Decision Trees (DT), Random Forest (RF), Extra Tree Classifier (ETC), Gradient Boosting Classifier (GBC), and K-Nearest Neighbors (KNN) revealed the Extra Tree Classifier achieved the highest testing accuracy of 93.85%. The methodologies demonstrated scalability by using a dataset augmented to 9,000 samples and validated robustness through 10-fold cross-validation with a standard deviation of 0.00659. These results highlight the proposed framework’s potential for real-world implementation in modern power grids, offering enhanced fault prediction and resilience. This research establishes a pathway for integrating advanced data augmentation and machine learning techniques into operational power grid systems, ensuring stability and reliability.https://ieeexplore.ieee.org/document/10818412/ACGANgrid event signature librarysmart gridfault toleranceMachine Learningspectral features |
| spellingShingle | Hafeez Ur Rehman Siddiqui Robert Brown Adil Ali Saleem Muhammad Amjad Raza Sandra Dudley Enhancing Power Grid Reliability With Machine Learning and Auxiliary Classifier Generative Adversarial Networks: A Study on Fault Detection Using the Georgia Electric System Load Dataset IEEE Access ACGAN grid event signature library smart grid fault tolerance Machine Learning spectral features |
| title | Enhancing Power Grid Reliability With Machine Learning and Auxiliary Classifier Generative Adversarial Networks: A Study on Fault Detection Using the Georgia Electric System Load Dataset |
| title_full | Enhancing Power Grid Reliability With Machine Learning and Auxiliary Classifier Generative Adversarial Networks: A Study on Fault Detection Using the Georgia Electric System Load Dataset |
| title_fullStr | Enhancing Power Grid Reliability With Machine Learning and Auxiliary Classifier Generative Adversarial Networks: A Study on Fault Detection Using the Georgia Electric System Load Dataset |
| title_full_unstemmed | Enhancing Power Grid Reliability With Machine Learning and Auxiliary Classifier Generative Adversarial Networks: A Study on Fault Detection Using the Georgia Electric System Load Dataset |
| title_short | Enhancing Power Grid Reliability With Machine Learning and Auxiliary Classifier Generative Adversarial Networks: A Study on Fault Detection Using the Georgia Electric System Load Dataset |
| title_sort | enhancing power grid reliability with machine learning and auxiliary classifier generative adversarial networks a study on fault detection using the georgia electric system load dataset |
| topic | ACGAN grid event signature library smart grid fault tolerance Machine Learning spectral features |
| url | https://ieeexplore.ieee.org/document/10818412/ |
| work_keys_str_mv | AT hafeezurrehmansiddiqui enhancingpowergridreliabilitywithmachinelearningandauxiliaryclassifiergenerativeadversarialnetworksastudyonfaultdetectionusingthegeorgiaelectricsystemloaddataset AT robertbrown enhancingpowergridreliabilitywithmachinelearningandauxiliaryclassifiergenerativeadversarialnetworksastudyonfaultdetectionusingthegeorgiaelectricsystemloaddataset AT adilalisaleem enhancingpowergridreliabilitywithmachinelearningandauxiliaryclassifiergenerativeadversarialnetworksastudyonfaultdetectionusingthegeorgiaelectricsystemloaddataset AT muhammadamjadraza enhancingpowergridreliabilitywithmachinelearningandauxiliaryclassifiergenerativeadversarialnetworksastudyonfaultdetectionusingthegeorgiaelectricsystemloaddataset AT sandradudley enhancingpowergridreliabilitywithmachinelearningandauxiliaryclassifiergenerativeadversarialnetworksastudyonfaultdetectionusingthegeorgiaelectricsystemloaddataset |