Deep Learning in Power Systems: A Bibliometric Analysis and Future Trends

Advancements in deep learning enable sophisticated algorithms to learn from vast data sets, driving innovation and efficiency in power systems. The global push for electrification has increased focus on deep learning, leading to numerous recent publications in this field. This paper presents a bibliometric analysis and future trends of deep learning in power systems, aiming to identify its fundamental characteristics and summarize the research hot topics and future trends. This study used the Scopus database with the keywords 'deep learning' and 'power systems', yielding 20,202 publications from 1970 to 2023. Over 37,600 authors contributed, averaging 12 citations per paper. Keyword trends show traditional deep learning techniques like LSTMs and CNNs are widely used in power systems, while newer methods like advanced reinforcement learning, graph neural networks, and physics-informed neural networks are emerging, promising advancements in optimal power flow, V2G integrations, and grid resilience. Further analysis highlights ubiquitous deep learning applications like load forecasting and power quality analysis in power systems. Emerging topics include microgrid optimization and electric vehicle charging demand prediction, with growing interest in IoT management, digital twins, and cybersecurity. Future research will focus on self-healing grids, optimal power flow, and energy trading for improved reliability and security.