Variational Mode Decomposition as Trusted Data Augmentation in ML-based Power System Stability Assessment
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910485538930688 |
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| author | Bogodorova, Tetiana Osipov, Denis Vanfretti, Luigi |
| author_facet | Bogodorova, Tetiana Osipov, Denis Vanfretti, Luigi |
| contents | Balanced data is required for deep neural networks (DNNs) when learning to perform power system stability assessment. However, power system measurement data contains relatively few events from where power system dynamics can be learnt. To mitigate this imbalance, we propose a novel data augmentation strategy preserving the dynamic characteristics to be learnt. The augmentation is performed using Variational Mode Decomposition. The detrended and the augmented data are tested for distributions similarity using Kernel Maximum Mean Discrepancy test. In addition, the effectiveness of the augmentation methodology is validated via training an Encoder DNN utilizing original data, testing using the augmented data, and evaluating the Encoder's performance employing several metrics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_09235 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Variational Mode Decomposition as Trusted Data Augmentation in ML-based Power System Stability Assessment Bogodorova, Tetiana Osipov, Denis Vanfretti, Luigi Signal Processing Balanced data is required for deep neural networks (DNNs) when learning to perform power system stability assessment. However, power system measurement data contains relatively few events from where power system dynamics can be learnt. To mitigate this imbalance, we propose a novel data augmentation strategy preserving the dynamic characteristics to be learnt. The augmentation is performed using Variational Mode Decomposition. The detrended and the augmented data are tested for distributions similarity using Kernel Maximum Mean Discrepancy test. In addition, the effectiveness of the augmentation methodology is validated via training an Encoder DNN utilizing original data, testing using the augmented data, and evaluating the Encoder's performance employing several metrics. |
| title | Variational Mode Decomposition as Trusted Data Augmentation in ML-based Power System Stability Assessment |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2406.09235 |