Exploring Machine Learning-Based Solutions for Fault Classification and Region Identification in Onshore Wind Farm Collector Systems

Author(s)

Davi, Moises J. B. B. ; Cunha, Talita M. O. A. ; Oliveira, Emanuel P. G. ; Oleskovicz, Mario

• Document type: Journal article
• Source: 2024 21ST INTERNATIONAL CONFERENCE ON HARMONICS AND QUALITY OF POWER, ICHQP 2024; v. N/A, p. 6-pg., 2024-01-01.

Abstract

With the increasing expansion of renewable energy resources, wind farms are becoming predominant, especially with the progress of Inverter-Based Resources (IBRs). Although IBRs are operationally effective, it is recognized that this generation type has directly impacted the operation of protection and fault diagnosis functions. In this context, this paper investigates the potential of Machine Learning (ML)-based solutions for classifying faults and identifying faulted regions in onshore wind farm collector systems. For the studies, a realistic wind farm system topology is modeled in PSCAD software, and fault scenarios are represented in the collector systems with different fault parameters and wind farm generation levels. These studies highlight the best-performing ML methods for fault diagnosis within onshore wind farms and provide insights on the best measurement points for successful fault classification and fault region identification. (AU)

• FAPESP's process: 20/15230-5 - Research Centre for Greenhouse Gas Innovation - RCG2I
• Grantee: Julio Romano Meneghini
• Support Opportunities: Research Grants - Research Centers in Engineering Program
• FAPESP's process: 22/00483-0 - Methodology for faults diagnosis in transmission lines with high penetration of inverter-interfaced wind generators
• Grantee: Moisés Junior Batista Borges Davi
• Support Opportunities: Scholarships in Brazil - Doctorate