Enhancing Grid Reliability through AI-Powered Knowledge Sharing

Juan Carlos Sánchez OMICRON electronics

Florian Fink OMICRON electronics

Abstract

As the power grid evolves with renewable energy, the challenge of keeping it reliable is growing, especially as experienced engineers retire. This paper addresses the knowledge transfer challenge, emphasizing the need for systematic documentation and the adoption of digital tools to preserve expertise for new engineers. It also highlights the role of Artificial Intelligence (AI) and Cloud Storage in managing data and facilitating operational efficiency in this challenge. However, while these technological advancements are vital, the document emphasizes that the human element is equally important, placing people at the center of this transformation.

Introduction

The transition to decentralized and decarbonized energy sources creates a pressing need for changes within electricity grids. The retirement of seasoned professionals introduces a challenge: integrating renewables while addressing the knowledge gap left behind. New generations of engineers, often without support from retired experts, face managing aging infrastructures and embracing new technologies in a competitive job market. This situation highlights the critical need for effective knowledge transfer and the development of new skills to ensure the adaptability and reliability of electricity grids in the evolving energy sector.

Filling Knowledge Gaps

In the dynamic landscape of power grid protection, automation, and control, the industry faces the significant challenge of retiring seasoned engineers who take critical knowledge with them. This leaves substantial knowledge gaps. While this happens, younger engineers are tasked with managing aging grid assets, some of which have been operational for two to three decades. These assets need to remain reliable while the industry transforms, incorporating new substations to meet the energy transition's demands.

The main challenge is sharing knowledge about old systems with limited or outdated information. Data related to these older assets must be collected, centralized, and accessible to maintenance personnel responsible for various critical tasks, including testing, retrofitting, troubleshooting, and day-to-day operations.

Historically, much of the understanding of equipment operation and specialized configurations has resided in the minds of experienced engineers. This knowledge, often undocumented, includes knowing where to find information or whom to consult within the organization. To bridge the impending knowledge gap, it is essential to systematically document this expertise, thereby enabling its integration into digital tools.

Concurrently, the industry must ensure that the documentation of newly commissioned substations is stored. This is essential to facilitate seamless operations and maintenance activities in the future.

Digital tools play an important role in democratizing knowledge. They must ensure that it is preserved and made readily available to the next generation of engineers tasked with safeguarding the integrity and functionality of our power grid systems.

Data preparation

The availability of data is crucial for informed decision-making and operational efficiency. Centralized storage solutions, particularly cloud storage, have emerged as the leading method for managing data due to their numerous benefits. Cloud storage offers scalability to handle vast volumes of data, remote accessibility via the internet, cost-effectiveness, robust cybersecurity measures, and reliable options for disaster recovery.

Over recent years, cloud storage has evolved into the preferred technology for data management. However, establishing such an infrastructure is only the beginning. It is essential to systematically store data in the cloud, which requires the collaboration of all involved parties. Departments and team members must develop and follow structured processes for organizing and maintaining the data in a way that can be found, understood, and reused. Clear responsibilities must be assigned, and processes must be implemented to ensure seamless operation. As the volume of data in cloud storage expands, locating specific, relevant information quickly becomes increasingly challenging.

AI and Large Language Models (LLMs) have emerged as powerful tools to address these challenges. These technologies can assist in searching, summarizing, and synthesizing information, thereby enhancing the ability to extract actionable insights from the growing data repository.

Large Language Models

A LLM is a type of generative AI, a computer program trained to understand and generate human-like text. It learns from a massive collection of documents to identify language patterns and grammar. The term "large" reflects the extensive data it is trained on and its complex structure. These models can perform tasks like answering questions, writing content, and more, making them valuable for various applications from customer service to content creation.

These are some of the common applications:

• Content Creation: LLMs serve as creative partners, offering a springboard for ideas, initial drafts, and inspiration that content creators can refine and personalize. They're tools for enhancing creativity, not replacing human writers' unique voice and insight.
• Customer Support: LLMs provide the first line of support by powering chatbots and virtual assistants, handling routine inquiries, and freeing human agents to tackle more complex customer needs.
• Programming: Tools like GitHub Copilot, powered by LLMs, act as virtual collaborators for developers. They suggest code snippets and functions, speeding up the development process and allowing developers to focus on creative problem-solving and strategic tasks.
• Translation: LLMs enhance communication by breaking language barriers and providing increasingly accurate and fluent translations.
• Educational Tools: In education, LLMs personalize learning by adapting explanations, tutoring, and feedback to the learner's needs. They support educators by providing additional resources and tailored support, enriching the educational experience.

Retrieval Augmented Generation

Retrieval-augmented generation (RAG) enhances AI by integrating additional, relevant information into their response process. This is particularly beneficial when tapping into a company's internal knowledge base, including detailed product information, domain-specific expertise, and customer insights. Such information is vital for providing precise and helpful answers.

1.      Knowledge Indexing

This pre-processing phase is critical in preparing textual data for subsequent retrieval and generation tasks. This phase includes the encoding or tokenizing of documents and sources containing the knowledge, where text is segmented into chunks and then converted to tokens representing discrete linguistic units such as sub-word elements. These tokens are then transformed into vector embeddings, high-dimensional numerical representations that encode semantic and syntactic attributes of the language.

In conjunction with these embeddings, metadata is integrated to provide additional contextual cues like related products, thereby enriching the vector representations with domain-specific insights. Furthermore, a weighting mechanism can be applied to calibrate the significance of documents relative to the inquiry at hand, ensuring an informed retrieval process.

2.      Retrieval Augmented Generation

When a question or statement is given, it is transformed into an embedding, leading the system to search for the most semantically similar information snippets. The goal is to find a group of document chunks with the top 'k' related to the query, setting the context for an augmented response.

The cosine similarity is typically used to determine if a document is related to the query. Cosine similarity measures the cosine of the angle between two vectors, reflecting their directional similarity in multi-dimensional space. It's calculated by dividing the dot product of the vectors by the product of their magnitudes. At this stage, all necessary components for the Language Model's input are compiled, the next step is creating the input to be processed by the Language Model. This includes:

• Question or Statement: The initial user query.
• Context: Top 'k' chunks or document pages semantically related to the user query.
• The System Prompt: A system prompt is a pre-defined instruction or question to guide a language model's response or action. It sets the context and focus for the model's output, ensuring the generated content is relevant and aligned with the user's intent or task.
• Previous Conversation Messages: Incorporating previous exchanges maintains coherence and relevance in ongoing conversations.

Retrieval-augmented generation (RAG) combines advanced language generation with information retrieval, significantly enhancing conversational AI. It produces accurate, context-rich responses by integrating the query, related context, system prompts, and previous interactions. This approach improves the relevance and depth of responses and represents a significant advancement in making AI conversations more like those between humans.

Generative AI Applied to the Power Industry

Generative AI is an innovative solution for the power industry, crucial for managing domain knowledge and supporting new engineers as experienced professionals retire. This technology enhances access to crucial information, facilitating a smoother transition for new staff to understand complex systems and technologies.

RAG also supports content creation and customer support. Large language models (LLMs) offer a foundation for ideas and drafts, enriching the creative process for content creators without replacing their unique insights. For customer support, LLMs power efficient chatbots and virtual assistants, managing routine questions and allowing human agents to focus on more complicated issues.

Generative AI will be key in maintaining high operational standards and innovation in the power industry by streamlining knowledge transfer and improving customer support efficiency.

Generative AI chatbots are user-friendly and swiftly provide substantial information. Leveraging these chatbots enables efficient access to relevant knowledge from diverse sources, all neatly summarized for quick understanding.

Generative AI Acceptance

Generative AI presents a transformative opportunity for the power industry, especially regarding knowledge transfer and operational efficiency. However, its integration and acceptance come with unique challenges that must be navigated carefully.

1. Trust and Reliability: Building trust in AI's decisions and outputs is one of the foremost challenges. Engineers and operators accustomed to traditional methods might question the reliability of AI-generated insights, particularly when it comes to complex decision-making processes involving grid reliability and maintenance. To build trust in AI within the power industry, engineers and operators need to understand how these technologies work, including their limitations and how to interact with them effectively. This involves learning to craft precise prompts, following up on AI-generated responses appropriately, and recognizing the boundaries of what AI and Large Language Models can achieve. Such knowledge ensures that users can leverage AI tools judiciously, enhancing decision-making processes while maintaining critical human oversight.
2. Data Security and Privacy: The power industry is a critical infrastructure, so data security is paramount for AI systems. There are concerns about the potential for breaches and the misuse of sensitive information, which could have far-reaching consequences.
3. Integration with Existing Systems: The power industry relies on many legacy systems and technologies. Integrating AI solutions to complement these existing frameworks without causing disruptions is a significant challenge.
4. Skill Gap and Training: Adopting Generative AI requires a technically proficient workforce and adaptability to new tools and workflows. This necessitates comprehensive training programs and a shift in the industry’s approach to skill development. Moreover, when AI begins to augment the core aspects of one's job, the implications extend far beyond the conventional concerns of job security. This shift is not merely about the potential for unemployment, which is a significant issue, but deeper, creating existential questions of losing one's identity and purpose.

Prompt engineering is about formulating instructions for artificial intelligence and guidelines, including what to do and what not to do, greatly improving the chatbots' performance, which is crucial for utilizing these tools effectively.

Conclusions

The power industry is at a critical crossroads where the integration of AI technologies presents a transformative opportunity to bridge knowledge gaps and enhance operational efficiency as experienced engineers retire. AI promises to democratize access to institutional knowledge and streamline the transition for new engineers. However, realizing their full potential requires addressing challenges related to trust, data security, the skill gap, and integration with legacy systems.

The power industry can navigate these challenges by systematically documenting expertise, adopting cloud storage for centralized data access, and leveraging AI for training and knowledge transfer. This approach ensures the continued reliability of the energy grid and the rapidly evolving energy landscape, maintaining high operational standards and fostering a culture of continuous learning and adaptation.

In the industrial sector, exciting new features are on the horizon, such as the ability for AI chatbots to remember individual user preferences and working styles. This will transform chatbots into personalized assistants that can adapt to and support our unique needs.

The field of generative AI is advancing rapidly, and we can expect to see significant improvements soon. Some challenges exist, such as the technology's limited ability to recognize and interpret tables or images. But these capabilities are getting better, and soon, generative AI will be able to understand and utilize the information from pictures and tables just like it does with text.

Acknowledgments

We extend our heartfelt gratitude to the artificial intelligence team at OMICRON electronics for their invaluable contributions to the research presented in this paper and for all the knowledge we have gained over the past months.

Disclaimer: This paper was created with the help of AI tools to improve efficiency, required hours of dedicated writing, and contains the authors' experience.