This study applies machine learning algorithms, revealing improved predictions of voltage stability in Ethiopian distribution networks, indicating essential monitoring points.
Key Points
The analysis found that machine learning can significantly improve voltage stability assessment, enhancing operational efficiency.
Random Forest and Gradient Boosting showed superior accuracy with R² values of 0.999 and 0.9998 respectively.
Critical instability risk points were identified, establishing the necessity for interventions in high-risk areas of the distribution system.
This approach underscores the potential of machine learning methods to enhance real-time applications in voltage stability assessment.