Seismic Design of High Voltage Equipment through AI-Driven Engineering

Leveraging AI-driven generative design algorithms and digital twin technology, the proposed methodology enables quick performance prediction and adaptive design optimization in accordance with the latest seismic standards.

The paper will showcase studies where AI tools were applied to optimize the seismic performance and analysis of AIS instrument transformers (IT) and circuit breakers (CB). It will also discuss regulatory compliance and validation against IEEE 693, ETG 1.020, IEC 62271300, IEC 61869-1 standards using AI tools, as well as the integration of AI workflows into existing design processes.

The paper also emphasizes the importance of cross-disciplinary collaboration in implementing

AI-driven seismic design. Engineers, seismic experts and end-users must work together to ensure that AI models are not only technically sound but also transparent and useful tool. This collaboration is essential for building trust and reliability in the adoption of this approach. By fostering a culture of innovation and continuous learning, the power industry can harness the full potential of AI to develop smarter and more efficient tools to address the growing demand for seismic analysis.

Moreover, the adoption of AI in seismic design promotes a more sustainable and resourceefficient engineering process. By minimizing the need for repeated physical prototyping and manual calculations, AI tools significantly reduce time consumption during the design phase.

Additionally, the ability to simulate allows for designs that may offer improved seismic optimization with a lower environmental footprint. As utilities and manufacturers face increasing pressure to meet both safety and sustainability targets, AI-driven seismic design offers a powerful solution that addresses both imperatives simultaneously.

Key Contributions:

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• Introduction of AI-enhanced seismic modeling validation techniques for HV equipment.

Demonstration of digital twin applications for real-time seismic response analysis.

Framework for integrating AI tools into engineering and R&D workflows.

Time reduction and engineering efficiency Target Audience:

This paper is intended for utility engineers, equipment manufacturers, digital transformation leaders, and researchers focused on grid resilience and smart infrastructure.

Finaly, the integration of AI into seismic design workflows requires cultural and procedural transformation within utilities and manufacturers. The paper also outlines a roadmap for digital transformation, data collection, data governance, and cybersecurity considerations. It will also address the challenges of regulatory acceptance and model transparency, offering strategies to validate AI-generated designs against established engineering standards.