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The fast expansion of renewable energy sources, rising electricity demand, and the requirement for improved grid dependability have made it necessary to create cutting-edge technologies for electric power systems. The electric power business faces several issues, and deep learning, a branch of machine learning, has emerged as a possible solution. This study offers a thorough analysis of deep learning applications in electric power systems, including load forecasting, fault detection, and diagnosis, assessment of the security and stability of the power system, integration and management of renewable energy sources, and asset management and maintenance of the electric grid. Although deep learning techniques have enormous potential, several issues and constraints must be resolved. These include data quality and availability issues, computational complexity, resource requirements, model interpretability …