Leveraging Digital Twins and AI Chatbots for Knowledge Management and Workforce Development

Digital twins and artificial intelligence-powered chatbots are introduced to create an interactive learning and knowledge preservation environment. The digital twin of protection and automation systems provides a high-fidelity virtual representation, enabling real-time simulation of various operational scenarios and fault conditions. This virtual environment serves as a safe, cost-effective training ground for both newcomers and experienced professionals to experiment with different protection schemes and settings.

The AI chatbot system, with access to specialized repositories of protection relay manuals and expert knowledge documents, acts as an always-available virtual mentor. It provides instantaneous access to technical information, troubleshooting guides, and best practices accumulated over decades of industry experience.

The paper presents comprehensive findings from extensive implementation of digital twin technology across diverse stakeholder groups including educational institutions, product manufacturers and power utilities. A paradigm shift from conventional hardware-dependent training to virtualized instruction and practical exercises has demonstrated enhanced adaptability to specific user requirements. This transformation has proven particularly 1 beneficial for new entrants to the power industry, enabling accelerated learning curves and more efficient onboarding processes.

The digital platform serves as a dynamic testing ground where manufacturers, system integrators, and utility personnel can explore and evaluate cutting-edge protection and automation to maintain technological currency while adhering to evolving industry standards.

A standout advantage of the virtual environment is its capacity to replicate rare but critical system scenarios – situations that would be impractical or impossible to recreate in traditional trainings. The flexibility of a digital twin platform enables customized learning experiences, allowing users to focus on specific areas of interest while maintaining a comprehensive understanding of protection and automation systems.

This paper further examines the implementation and effectiveness of specialized AI chatbots designed specifically for the power automation sector, supporting various roles throughout project lifecycles. Unlike general-purpose AI models, these domain-specific chatbots demonstrate substantially higher accuracy in addressing technical queries related to power systems protection and automation. These assistants serve as valuable resources for engineering teams, technical sales professionals, and support personnel.

A key finding highlights how the chatbots' ability to reference and cite original documentation and technical manuals significantly enhances user trust and confidence in the provided solutions. This transparency in source attribution has proven to be a crucial factor in driving widespread adoption.

The paper addresses critical technical considerations in developing and maintaining such specialized AI systems. The research also emphasizes how these chatbots evolve through feedback loops, incorporating new documentation and user interactions while maintaining strict accuracy standards, making it an invaluable tool for ongoing professional development and technical support.

A digital transformation program based on digital twins and AI chatbots demonstrate significant improvements in knowledge retention, troubleshooting efficiency, and workforce readiness to ensure the continued reliable operation of electrical infrastructure in an increasingly complex environment. The paper concludes with recommendations for industry-wide adoption of digital knowledge management solutions.