Enhancing Fault Location in Modern Wind Farms: Performance Insights From Multiple Machine Learning Approaches

Abstract

The increasing integration of Inverter-Based Resources (IBRs) into modern power grids has introduced new challenges for conventional fault-location techniques, particularly for renewable sources such as wind and solar. To address this issue, this paper proposes a robust Machine Learning (ML)-based framework for accurately locating electrical faults in wind farm collector networks. The proposed methodology comprises multiple stages. Initially, 14 different regression models were implemented and evaluated using the Scikit-Learn library to assess their suitability for the task. Subsequently, a comprehensive hyperparameter optimization phase was conducted using Optuna, aiming not only to enhance model accuracy but also to reassess previously under-performing algorithms. The resulting models were then validated under a wide range of operating conditions, including variations in fault resistance, fault location, fault inception angle, and wind farm generation level. By exposing these models to 18,600 distinct fault scenarios, the simulations provide a comprehensive assessment of their generalization capability and demonstrate the effectiveness of the proposed approach. Among all models tested, the Multi-Layer Perceptron Regressor (MLP), Support Vector Regressor (SVR), and Kernel Ridge Regression (KRR) achieved the highest accuracy, with prediction errors not exceeding 2%. Conversely, ensemble-based methods such as Random Forest, AdaBoost, and Gradient Boosting exhibited noticeable limitations, struggling to capture the complex nonlinear relationships between fault characteristics and their corresponding locations in the context of IBRs.