AI and asset management
in a digital world

Join the discussion on AI, asset management, and smart grid reliability

Explore how Hitachi Energy is driving advancements in reliability, cybersecurity, and asset performance management using advanced AI tools.

The latest research from Dr. Naser Hashemnia, Principal Solution Consultant, Grid Automation, will be shared on November 20 at EECON 2024 in Australia.

The paper – “Machine Learning Making Big Inroads in Smart Grid Reliability and Asset Management,” - introduces a Temporal Convolutional Network (TCN) model for detecting system faults and abnormalities in smart grids, validated through real-world data and simulations. With 99% detection accuracy, the model outperforms existing methods and enhances the resilience of smart grids against sophisticated threats.

Key benefits:

• Resilient and efficient grids: Integrate renewable energy sources and advanced infrastructures
• Increased security: Tackle cyberattacks, voltage instability, and power quality disturbances
• Machine learning techniques: Discover effective methodologies to quickly identify and detect issues

AI-driven innovation

The developed intrusion detection system will enable utilities and generators to significantly enhance network security, stability, and reliability. This enhanced AI model delivers high computational efficiency and improved accuracy, providing a robust solution for reducing false positives and strengthening cybersecurity across network assets.

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