20.500.12556/DKUM-93896 Optimal ensemble-based framework for ground-fault protection in radial MV distribution networks with resonant grounding☆ Ground fault relays (GFRs) in resonant-grounded medium voltage distribution networks shall not operate during phase-to-ground (Ph-G) fault inception, allowing the Petersen coil to suppress self-extinguishing faults, but the designated GFR must operate during permanent faults. In order to enhance the performance of GFRs, particularly during high-impedance faults, the scope of this paper is to propose a straightforward, machine-learning-based protection framework. The enhanced GFR is modeled as a classification task. Depending on the GFR’s position and the Ph-G fault location in the network, fault samples are labeled as “no operation,” “primary,” “backup,” or “backup of backup,” forming two-class, three-class, and four-class GFR setups, respectively. This assures selective operation across three protection zones and improves the reliability of all GFRs. The proposed protection scheme employs backward optimal feature selection to identify the most relevant discrete features obtained from measured zero-sequence current and voltage waveforms. An ensemble of k-nearest neighbor classifiers is utilized for accurate classification, simulating the GFR operating conditions, with measurement errors and sensitivity incorporated in the preprocessing. A 20 kV case study network validates the proposed framework, achieving F1-scores exceeding 96 %. The maximum operation delay of the protection scheme for an enhanced GFR is 225 ms, accommodating the required time window (200 ms), prediction time (5 ms), and change detection time (20 ms), thus assuring safe operation. Compared to other machine-learning-based methods used for Ph-G fault protection in resonant-grounded radial networks, this framework is high-performing, fast, and easy to implement, utilizing a simpler structure than neural networks. resonant grounded networks ground-fault relay high-impedance faults ensemble-based learning optimal feature selection resonančno ozemljena omrežja rele za ozemljitveno napetost visokoimpedančne napake učenje v ansamblu izbira optimalne značilnosti true false true Elsevier Ltd. Angleški jezik Slovenski jezik https://www.sciencedirect.com/journal/international-journal-of-electrical-power-and-energy-systems © 2025 The Author(s) Članek v reviji 2025-07-25 08:03:25 2025-07-25 08:03:26 2025-07-26 03:14:15 0000-00-00 00:00:00 2025 0 0 17 str. [article no.] ǂ110881 Vol. 170 Sep. 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2025-03-12 2025-07-05 2025-07-12 621.31 243525635 10.1016/j.ijepes.2025.110881 1879-3517 1-s2.0-S0142061525004296-main.pdf 1-s2.0-S0142061525004296-main.pdf 1 2F3BA63C4043C07286DB93B1929D15A3 fe0651028d90b54ed01de6cfb7f69337acca21296379389d500384d8c6d0762c 76bdc01f-691d-11f0-b339-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=194127 Fakulteta za elektrotehniko, računalništvo in informatiko 0 0 0