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There is a need for efficient and optimized power distribution for the transformer system. This paper presents a hybrid AI-driven approach integrating Multi-Agent Deep Reinforcement Learning (MARL) and Anomaly Detection for transformer power system optimization. Three key components have been focused: (1) a Predictive Maintenance Model leveraging deep learning to estimate the Remaining Useful Life (RUL) of transformers, (2) an Autoencoder-based Fault Detection Model for anomaly identification, and (3) a Multi-Agent Deep Q-Network (DQN) system optimizing power distribution and balancing loads dynamically. The findings contribute to intelligent power system management, reduced downtime, increased transformer downtime and adaptive optimization framework for future smart grids. Such contributions are aligned with higher sustainability goals by minimizing wastage of resources and minimizing carbon footprints due to inefficient grid operations.

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Hybrid AI-Driven Optimization for Transformer Power Systems: Multi-agent Reinforcement Learning and Anomaly Detection

  • 会议论文

  • 发表日期：

    2026年

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  • Aneri Shah,
  • Krish Modi,
  • Ansh Soni,
  • Nishant Doshi

摘要

There is a need for efficient and optimized power distribution for the transformer system. This paper presents a hybrid AI-driven approach integrating Multi-Agent Deep Reinforcement Learning (MARL) and Anomaly Detection for transformer power system optimization. Three key components have been focused: (1) a Predictive Maintenance Model leveraging deep learning to estimate the Remaining Useful Life (RUL) of transformers, (2) an Autoencoder-based Fault Detection Model for anomaly identification, and (3) a Multi-Agent Deep Q-Network (DQN) system optimizing power distribution and balancing loads dynamically. The findings contribute to intelligent power system management, reduced downtime, increased transformer downtime and adaptive optimization framework for future smart grids. Such contributions are aligned with higher sustainability goals by minimizing wastage of resources and minimizing carbon footprints due to inefficient grid operations.