The risk of unintentional islanding creation in distributed energy systems poses a significant security concern since unintentional islanding formation could lead to a supply of energy outside of the optimal quality limits. This constitutes a risk for users, maintenance personnel, infrastructure, and devices. To mitigate this problem, anti-islanding protections are widely used to prevent the distributed generator from feeding a portion of the radial distribution grid when a protection device trips upstream. However, the effectiveness of these protections heavily relies on properly tuning protection setting thresholds (such as time delay and pickup). This work proposes a novel approach that utilizes entropy as a model and metric of the uncertainty associated with a particular protection setting. By minimizing entropy, the proposed method aims to improve stability and sensitivity, consequently improving the overall performance of anti-islanding protection. Simulation results demonstrate that the Bayesian entropy methodology (BEM) approach achieves enhanced stability in various scenarios, including frequency transients, and demonstrates a notable reduction in the size of the dataset and computational burden, ranging between 91% and 98%, when compared to related works, with an improvement of the uncertainty achieved. The findings of this study contribute to the development of more robust and reliable anti-islanding protections.

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.