PulseTrendingJournal ClubResearchersJournalsExplore
Instagram
HomeTrendingJournal ClubExplore
Synapse
⌘+K
Synapse
February 21, 2026IET conference proceedings.

Power grid-connected photovoltaic system voltage stability evaluation using machine learning

View Full Paper
Ask AI
Bookmark
Share

Authors

MDMahamat Defallah DjamaladineASAdekunlé Akim SalamiAGAgbassou Guenoukpati

Discussion

Loading...

Member takes

Overview

Evaluates voltage stability in photovoltaic systems using machine learning models, suggesting effective integration approaches.

Key Points

  • The aim is to predict the voltage stability margin of a grid network based on Q-V analysis in photovoltaic systems.
  • Utilized Q-V curves to evaluate voltage stability margins.
  • Applied four machine learning models: support vector machines, decision trees, random forests, and artificial neural networks.
  • Conducted tests on the IEEE 14-bus system with varying photovoltaic penetration rates.
  • Artificial neural networks showed the highest accuracy in predicting voltage stability.
  • Evaluations were made across training, testing, and validation datasets.
  • Integration of photovoltaic sources influenced reactive power margins at load nodes.

Cite This Study

Djamaladine et al. (2026) studied this question.

synapsesocial.com/papers/69994c01873532290d020328https://doi.org/10.1049/icp.2025.3853
View Full Paper
Ask AI
Bookmark
Share