Event-Type Identification in Power Grids Using a Spectral Correlation Function-Aided Convolutional Neural Network
Rapid and accurate identification of events in power grids is critical to ensuring system reliability and security. This study introduces a novel event-type identification method, utilizing a Spectral Correlation Function (SCF)-aided Convolutional Neural Network (CNN). The proposed method employs a...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Access Journal of Power and Energy |
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| Online Access: | https://ieeexplore.ieee.org/document/10789217/ |
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| author | Ozgur Alaca Ali Riza Ekti Jhi-Young Joo Nils Stenvig |
| author_facet | Ozgur Alaca Ali Riza Ekti Jhi-Young Joo Nils Stenvig |
| author_sort | Ozgur Alaca |
| collection | DOAJ |
| description | Rapid and accurate identification of events in power grids is critical to ensuring system reliability and security. This study introduces a novel event-type identification method, utilizing a Spectral Correlation Function (SCF)-aided Convolutional Neural Network (CNN). The proposed method employs a six-stage cascaded structure consisting of: (1) data collection, (2) clipping, (3) augmentation, (4) feature extraction (FE), (5) training, and (6) testing. Real-world power grid signals sourced from the Grid Event Signature Library are used for both training and testing. To improve robustness, additive white Gaussian noise (AWGN) is introduced at various signal-to-noise ratio (SNR) levels to augment the dataset. The SCF-based FE method captures distinctive event-type characteristics by exploiting the spectral correlation of signals, allowing the CNN architecture to effectively learn and generalize event patterns. The proposed method is benchmarked against seven conventional techniques, using real-world power grid signals representing four distinct event types: blown fuse, line switching, low amplitude arcing, and transformer energization. Key performance metrics-prediction accuracy, mean absolute error (MAE), precision, recall, F1-score, and confusion matrix—are employed to evaluate the performance. Results demonstrate that the SCF-CNN method outperforms traditional approaches across all metrics and SNR levels, achieving over 99% prediction accuracy and nearly zero error for SNR values above 6 dB. This signifies its efficacy in reliable event-type identification for power grid applications. |
| format | Article |
| id | doaj-art-24da2543a1c04d6bba690dc3efac1ac1 |
| institution | Kabale University |
| issn | 2687-7910 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Access Journal of Power and Energy |
| spelling | doaj-art-24da2543a1c04d6bba690dc3efac1ac12025-01-21T00:03:20ZengIEEEIEEE Open Access Journal of Power and Energy2687-79102024-01-011165366410.1109/OAJPE.2024.351377610789217Event-Type Identification in Power Grids Using a Spectral Correlation Function-Aided Convolutional Neural NetworkOzgur Alaca0https://orcid.org/0000-0001-5371-3758Ali Riza Ekti1https://orcid.org/0000-0003-0368-0374Jhi-Young Joo2https://orcid.org/0009-0008-5989-843XNils Stenvig3https://orcid.org/0000-0001-5484-045XElectrification and Energy Infrastructure Division, Oak Ridge National Laboratory, Oak Ridge, TN, USAElectrification and Energy Infrastructure Division, Oak Ridge National Laboratory, Oak Ridge, TN, USAComputational Engineering Division, Lawrence Livermore National Laboratory, Livermore, CA, USAElectrification and Energy Infrastructure Division, Oak Ridge National Laboratory, Oak Ridge, TN, USARapid and accurate identification of events in power grids is critical to ensuring system reliability and security. This study introduces a novel event-type identification method, utilizing a Spectral Correlation Function (SCF)-aided Convolutional Neural Network (CNN). The proposed method employs a six-stage cascaded structure consisting of: (1) data collection, (2) clipping, (3) augmentation, (4) feature extraction (FE), (5) training, and (6) testing. Real-world power grid signals sourced from the Grid Event Signature Library are used for both training and testing. To improve robustness, additive white Gaussian noise (AWGN) is introduced at various signal-to-noise ratio (SNR) levels to augment the dataset. The SCF-based FE method captures distinctive event-type characteristics by exploiting the spectral correlation of signals, allowing the CNN architecture to effectively learn and generalize event patterns. The proposed method is benchmarked against seven conventional techniques, using real-world power grid signals representing four distinct event types: blown fuse, line switching, low amplitude arcing, and transformer energization. Key performance metrics-prediction accuracy, mean absolute error (MAE), precision, recall, F1-score, and confusion matrix—are employed to evaluate the performance. Results demonstrate that the SCF-CNN method outperforms traditional approaches across all metrics and SNR levels, achieving over 99% prediction accuracy and nearly zero error for SNR values above 6 dB. This signifies its efficacy in reliable event-type identification for power grid applications.https://ieeexplore.ieee.org/document/10789217/Data augmentationevent and fault classificationgrid event signature library (GESL)low amplitude arcingmachine learningspectral correlation function (SCF) |
| spellingShingle | Ozgur Alaca Ali Riza Ekti Jhi-Young Joo Nils Stenvig Event-Type Identification in Power Grids Using a Spectral Correlation Function-Aided Convolutional Neural Network IEEE Open Access Journal of Power and Energy Data augmentation event and fault classification grid event signature library (GESL) low amplitude arcing machine learning spectral correlation function (SCF) |
| title | Event-Type Identification in Power Grids Using a Spectral Correlation Function-Aided Convolutional Neural Network |
| title_full | Event-Type Identification in Power Grids Using a Spectral Correlation Function-Aided Convolutional Neural Network |
| title_fullStr | Event-Type Identification in Power Grids Using a Spectral Correlation Function-Aided Convolutional Neural Network |
| title_full_unstemmed | Event-Type Identification in Power Grids Using a Spectral Correlation Function-Aided Convolutional Neural Network |
| title_short | Event-Type Identification in Power Grids Using a Spectral Correlation Function-Aided Convolutional Neural Network |
| title_sort | event type identification in power grids using a spectral correlation function aided convolutional neural network |
| topic | Data augmentation event and fault classification grid event signature library (GESL) low amplitude arcing machine learning spectral correlation function (SCF) |
| url | https://ieeexplore.ieee.org/document/10789217/ |
| work_keys_str_mv | AT ozguralaca eventtypeidentificationinpowergridsusingaspectralcorrelationfunctionaidedconvolutionalneuralnetwork AT alirizaekti eventtypeidentificationinpowergridsusingaspectralcorrelationfunctionaidedconvolutionalneuralnetwork AT jhiyoungjoo eventtypeidentificationinpowergridsusingaspectralcorrelationfunctionaidedconvolutionalneuralnetwork AT nilsstenvig eventtypeidentificationinpowergridsusingaspectralcorrelationfunctionaidedconvolutionalneuralnetwork |