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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2512.13004 |
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| _version_ | 1866908711790837760 |
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| author | Sarwar, Muhammad Rizwan, Muhammad Aziz, Mubushra Sudais, Abdul Rehman |
| author_facet | Sarwar, Muhammad Rizwan, Muhammad Aziz, Mubushra Sudais, Abdul Rehman |
| contents | This comprehensive literature review examines the emerging applications of Large Language Models (LLMs) in power system engineering. Through a systematic analysis of recent research published between 2020 and 2025, we explore how LLMs are being integrated into various aspects of power system operations, planning, and management. The review covers key application areas including fault diagnosis, load forecasting, cybersecurity, control and optimization, system planning, simulation, and knowledge management. Our findings indicate that while LLMs show promising potential in enhancing power system operations through their advanced natural language processing and reasoning capabilities, significant challenges remain in their practical implementation. These challenges include limited domain-specific training data, concerns about reliability and safety in critical infrastructure, and the need for enhanced explainability. The review also highlights emerging trends such as the development of power system-specific LLMs and hybrid approaches combining LLMs with traditional power engineering methods. We identify crucial research directions for advancing the field, including the development of specialized architectures, improved security frameworks, and enhanced integration with existing power system tools. This survey provides power system researchers and practitioners with a comprehensive overview of the current state of LLM applications in the field and outlines future pathways for research and development. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_13004 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Large Language Models for Power System Applications: A Comprehensive Literature Survey Sarwar, Muhammad Rizwan, Muhammad Aziz, Mubushra Sudais, Abdul Rehman Systems and Control This comprehensive literature review examines the emerging applications of Large Language Models (LLMs) in power system engineering. Through a systematic analysis of recent research published between 2020 and 2025, we explore how LLMs are being integrated into various aspects of power system operations, planning, and management. The review covers key application areas including fault diagnosis, load forecasting, cybersecurity, control and optimization, system planning, simulation, and knowledge management. Our findings indicate that while LLMs show promising potential in enhancing power system operations through their advanced natural language processing and reasoning capabilities, significant challenges remain in their practical implementation. These challenges include limited domain-specific training data, concerns about reliability and safety in critical infrastructure, and the need for enhanced explainability. The review also highlights emerging trends such as the development of power system-specific LLMs and hybrid approaches combining LLMs with traditional power engineering methods. We identify crucial research directions for advancing the field, including the development of specialized architectures, improved security frameworks, and enhanced integration with existing power system tools. This survey provides power system researchers and practitioners with a comprehensive overview of the current state of LLM applications in the field and outlines future pathways for research and development. |
| title | Large Language Models for Power System Applications: A Comprehensive Literature Survey |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2512.13004 |