From Documents to Knowledge: Object-Oriented, Data-Centric Approaches Transforming Knowledge Management for PAC Systems

Automation, and Control (PAC) systems. The retirement of experienced professionals, combined with the onboarding of younger engineers, highlights gaps in traditional knowledge management practices that rely heavily on file-based documentation and tacit expertise.

This paper proposes a paradigm shift from conventional document-centric engineering toward a data-centric, object-oriented methodology based on Engineering Information Modeling

(EIM). Inspired by principles widely adopted in Building Information Modeling (BIM) for civil and electromechanical engineering. EIM provides an integrated approach to structure, manage, and share PAC engineering information consistently across the entire project and asset lifecycle.

EIM is grounded in three pillars: process, people, and technology. It emphasizes cultural transformation and governance to ensure consistent and reliable use of structured data, enabling organizations to manage not just documents, but interconnected, traceable engineering objects.

The technology and process pillars jointly support a top-down engineering methodology in which standardized and validated templates enable the automated generation of PAC projects, thereby improving consistency and significantly reducing engineering time and error rates.

The paper highlights the role of structured taxonomies and engineering ontologies implemented within database-driven platforms. These semantic structures enable the formal representation of PAC-specific concepts, such as protection functions, logical schemes, communication architectures, and device settings, ensuring consistency, traceability, and intelligent crossreferencing throughout the lifecycle.

To overcome the burden of legacy data, the paper presents the application of artificial intelligence techniques for the automated extraction of engineering metadata from unstructured sources, including legacy documents and engineering diagrams. By identifying elements such as equipment models, revision history, and diagram descriptions, AI-assisted extraction accelerates the population of structured databases and substantially reduces the effort required to establish a usable and centralized digital knowledge base. Extracted data undergoes humanin-the-loop validation to ensure accuracy, mitigating error risks.

The case studies demonstrate that the adoption of a centralized, object-oriented engineering platform leads to measurable improvements in efficiency, traceability, and reliability of PAC engineering activities. Tasks such as information retrieval, change identification, and project generation were executed significantly faster and with greater consistency when compared to traditional document-centric workflows. Results show substantial reductions in engineering time, while confirms improved accuracy, reduced ambiguity, and faster access to critical technical information, particularly in fault analysis and substation modernization contexts.

Beyond immediate productivity gains, the results indicate that EIM provides a robust foundation for systematic knowledge preservation and workforce transition. By combining structured data models, transparent object histories, guided engineering templates, and AIassisted tools, engineering knowledge becomes explicit, auditable, and reusable across projects and generations. This integrated paradigm supports scalable management of both legacy and new PAC data, enables reliable AI-assisted interaction with engineering information, and contributes to long-term consistency, traceability, and resilience in highly digitalized power system environments.