Optimization of Calibration Settings for Passive Anti-Islanding Protections Using a Bayesian Entropy Methodology to Support the Sustainable Integration of Renewable Distributed Generation
Year: 2025
Type: article
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.
Source: Sustainability
DOI: 10.3390/su17114859
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4410774425