Support Vector Machine (SVM)-Based Synchronized Fault Detection for 100% Renewable Microgrids

Research output: NRELPresentation

Abstract

Traditional protection schemes face significant challenges when applied to microgrids with high penetrations of renewables with inverter-based resources (IBRs). The proliferation of advanced sensing and communication technologies has generated copious data, offering an opportunity to overcome these limitations using data-driven machine learning approaches. This work proposes a novel approach based on a support vector machine (SVM) for detecting faults within a 100% renewable microgrid. The approach encompasses a systematic offline training stage for the development of a linear SVM-based fault detection algorithm. This process covers offline data collection from the microgrid under study, the extraction of features such as positive- and negative-sequence components and the total harmonic distortion of the voltage and current measurements of the relays, and the design of the linear SVM-based classifier. During the online implementation, however, different classifiers can exhibit asynchronicity in detecting the fault inception at different subcycle-to-cycle period-level delays. To circumvent this asynchronicity issue, a separate algorithm is developed for each relay to estimate the fault inception time as close to the real fault time. The performance of the proposed SVM-based synchronized fault detection method is evaluated using online time-domain simulation studies on a microgrid test system. The results corroborate the reliability of the fault detection scheme when tested under various fault cases (fault types, locations, and impedances) and non-fault cases during both grid-tied and islanded operation modes.
Original languageAmerican English
Number of pages13
StatePublished - 2024

Publication series

NamePresented at the 50th Annual Conference of the IEEE Industrial Electronics Society (IECON), 3-6 November 2024, Chicago, Illinois

NREL Publication Number

  • NREL/PR-5D00-91883

Keywords

  • 100% renewable microgrid
  • data-driven protection
  • fault time estimation
  • support vector machine

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