{"ID":2848107,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.26604","arxiv_id":"2510.26604","title":"Statistically Adaptive Differential Protection for AC Microgrids Based on Kullback-Leibler Divergence","abstract":"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.","short_abstract":"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 comp...","url_abs":"https://arxiv.org/abs/2510.26604","url_pdf":"https://arxiv.org/pdf/2510.26604v1","authors":"[\"Shahab Moradi Torkashvand\",\"Arina Kharazi\",\"Emad Sadeghi\",\"Seyed Hossein Hesamedin Sadeghi\",\"Adel Nasiri\"]","published":"2025-10-30T15:34:09Z","proceeding":"eess.SP","tasks":"[\"eess.SP\",\"eess.SY\"]","methods":"[]","has_code":false}