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

Abstract

The global pursuit of sustainable development increasingly depends on integratingrenewable energy sources into power systems, with distributed generation (DG)playing a vital role. However, this integration presents technical challenges, particularlythe risk of unintentional islanding. Anti-islanding protections are essential for detectingand isolating such events, as required by IEEE 1547, within two seconds. Yet, calibratingthese protections to balance sensitivity and reliability remains a complex task, as evidencedby incidents like the UK power outage on 9 August 2019 and the Southwestern Utahevent on 10 April 2023. This study introduces the Bayesian Entropy Methodology (BEM),an innovative approach that employs entropy as a model for uncertainty in protectiondecision-making. By leveraging Bayesian inference, BEM identifies optimal calibrationsettings for time delay and pick-up thresholds, minimizing uncertainty and effectivelybalancing sensitivity and reliability. The methodology incorporates a modified entropysurface to enhance optimization outcomes. Applied to the IEEE 34-node test system, BEMdemonstrates the ability to determine optimal settings with a significantly reduced trainingdataset, leading to substantial computational savings. By enhancing the reliability of antiislandingprotections, BEM facilitates the secure integration of renewable DG, contributingto the sustainable development of modern power systems.