Artificial Neural-Network Architecture for Enhancing Power Transformers Security

Authors

  • Najmuddin Aamer Research Scholar, Computer Science and Engg. Dept. Sunrise University, Alwar- 301026, Rajasthan, India
  • Rajesh Banala Assistant Professor, Computer Science and Engg. Dept. Sunrise University, Alwar- 301026, Rajasthan, India

Keywords:

Artificial Neural Networks, Differential protection, Power Transformers, Particle-Swarm-Optimization, Wavelet Neural Networks

Abstract

As Artificial Intelligence (AI) advances, it promises to transform power transformer security. Machine learning, neural networks, and predictive analytics are driving this change. AI can help with predictive maintenance, fault diagnosis, real-time monitoring, and adaptive control systems. The goal is to make power grids more reliable and resilient. The use of AI in transformer security is increasing. The evolution of power system protection has been remarkable, shifting from fuses and electromechanical devices to advanced computer-based systems. These modern, expert-based solutions have proven to be the most effective and often necessary approach to addressing emerging protection challenges. This work explores the potential of AI in power transformer security by means of various architectures using different Artificial Neural Networks (ANN) architectures and its impact on power infrastructure.

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Published

24.03.2024

How to Cite

Aamer, N. ., & Banala, R. . (2024). Artificial Neural-Network Architecture for Enhancing Power Transformers Security. International Journal of Intelligent Systems and Applications in Engineering, 12(19s), 445–459. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5084

Issue

Vol. 12 No. 19s (2024)

Section

Research Article

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