Statistically Adaptive Differential Protection for AC Microgrids Based on Kullback-Leibler Divergence
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908620584648704 |
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| author | Torkashvand, Shahab Moradi Kharazi, Arina Sadeghi, Emad Sadeghi, Seyed Hossein Hesamedin Nasiri, Adel |
| author_facet | Torkashvand, Shahab Moradi Kharazi, Arina Sadeghi, Emad Sadeghi, Seyed Hossein Hesamedin Nasiri, Adel |
| contents | The proliferation of inverter-based resources challenges traditional microgrid protection by introducing variable fault currents and complex transients. This paper presents a statistically adaptive differential protection scheme based on Kullback-Leibler divergence, implemented via a Bartlett-corrected G-statistic computed on logarithm-transformed current magnitudes. The method is a multivariate fault detection engine that employs the Mahalanobis distance to distinguish healthy and faulty states, enabling robust detection even in noisy environments. Detection thresholds are statistically derived from a chi-squared distribution for precise control over the false alarm rate. Upon detection, a lightweight classifier identifies the fault type by assessing per-phase G-statistics against dedicated thresholds, enhanced by a temporal persistence filter for security. Extensive simulations on a modified CIGRE 14-bus microgrid show high efficacy: sub-cycle average detection delays, high detection and classification accuracy across operating modes, resilience to high-impedance faults up to 250 Ohms, tolerance to 10 ms communication delay, and noise levels down to a 20 dB signal-to-noise ratio. These findings demonstrate a reproducible and computationally efficient solution for next-generation AC microgrid protection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_26604 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Statistically Adaptive Differential Protection for AC Microgrids Based on Kullback-Leibler Divergence Torkashvand, Shahab Moradi Kharazi, Arina Sadeghi, Emad Sadeghi, Seyed Hossein Hesamedin Nasiri, Adel Signal Processing Systems and Control The proliferation of inverter-based resources challenges traditional microgrid protection by introducing variable fault currents and complex transients. This paper presents a statistically adaptive differential protection scheme based on Kullback-Leibler divergence, implemented via a Bartlett-corrected G-statistic computed on logarithm-transformed current magnitudes. The method is a multivariate fault detection engine that employs the Mahalanobis distance to distinguish healthy and faulty states, enabling robust detection even in noisy environments. Detection thresholds are statistically derived from a chi-squared distribution for precise control over the false alarm rate. Upon detection, a lightweight classifier identifies the fault type by assessing per-phase G-statistics against dedicated thresholds, enhanced by a temporal persistence filter for security. Extensive simulations on a modified CIGRE 14-bus microgrid show high efficacy: sub-cycle average detection delays, high detection and classification accuracy across operating modes, resilience to high-impedance faults up to 250 Ohms, tolerance to 10 ms communication delay, and noise levels down to a 20 dB signal-to-noise ratio. These findings demonstrate a reproducible and computationally efficient solution for next-generation AC microgrid protection. |
| title | Statistically Adaptive Differential Protection for AC Microgrids Based on Kullback-Leibler Divergence |
| topic | Signal Processing Systems and Control |
| url | https://arxiv.org/abs/2510.26604 |