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

Conference · Wed Jan 08 00:00:00 EST 2025

OSTI ID:2500367

Chakraborty, Soham; Chen, Yue; Zamzam, Ahmed; Wang, Jing

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.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE National Renewable Energy Laboratory (NREL), Laboratory Directed Research and Development (LDRD) Program
• DOE Contract Number: AC36-08GO28308
• OSTI ID: 2500367
• Report Number(s): NREL/PR-5D00-91883; MainId:93661; UUID:8f4743e9-0625-471f-9fd5-27818f253cc9; MainAdminId:75692
• Conference Information: Presented at the 50th Annual Conference of the IEEE Industrial Electronics Society (IECON), 3-6 November 2024, Chicago, Illinois
• Country of Publication: United States
• Language: English

Related Subjects

100% renewable microgrid
data-driven protection
fault time estimation
MATHEMATICS AND COMPUTING
POWER TRANSMISSION AND DISTRIBUTION
support vector machine