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

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.