Optimization of Calibration Settings for Passive Anti-Islanding Protections Using a Bayesian Entropy Methodology to Support the Sustainable Integration of Renewable Distributed Generation

Eduardo Marcelo Seguin Batadi, Marcelo Gustavo Molina () and Maximiliano Martínez
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Eduardo Marcelo Seguin Batadi: Instituto de Energía Eléctrica (IEE), Universidad Nacional de San Juan (UNSJ) and Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), San Juan J5400, Argentina
Marcelo Gustavo Molina: Instituto de Energía Eléctrica (IEE), Universidad Nacional de San Juan (UNSJ) and Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), San Juan J5400, Argentina
Maximiliano Martínez: Instituto de Energía Eléctrica (IEE), Universidad Nacional de San Juan (UNSJ) and Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), San Juan J5400, Argentina

Sustainability, 2025, vol. 17, issue 11, 1-27

Abstract: The global pursuit of sustainable development increasingly depends on integrating renewable energy sources into power systems, with distributed generation (DG) playing a vital role. However, this integration presents technical challenges, particularly the risk of unintentional islanding. Anti-islanding protections are essential for detecting and isolating such events, as required by IEEE 1547, within two seconds. Yet, calibrating these protections to balance sensitivity and reliability remains a complex task, as evidenced by incidents like the UK power outage on 9 August 2019 and the Southwestern Utah event on 10 April 2023. This study introduces the Bayesian Entropy Methodology (BEM), an innovative approach that employs entropy as a model for uncertainty in protection decision-making. By leveraging Bayesian inference, BEM identifies optimal calibration settings for time delay and pick-up thresholds, minimizing uncertainty and effectively balancing sensitivity and reliability. The methodology incorporates a modified entropy surface to enhance optimization outcomes. Applied to the IEEE 34-node test system, BEM demonstrates the ability to determine optimal settings with a significantly reduced training dataset, leading to substantial computational savings. By enhancing the reliability of anti-islanding protections, BEM facilitates the secure integration of renewable DG, contributing to the sustainable development of modern power systems.

Keywords: sustainability; renewable energy; anti-islanding; reliability; Bayesian entropy methodology (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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