Deep learning methods utilization in electric power systems

Abstract

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, and integration with current power system tools and infrastructure. Prospects and opportunities in the field are also covered in the study, emphasizing the creation of innovative deep learning algorithms and architectures, scalable and effective computational platforms, multidisciplinary research and collaboration, standardization, and benchmarking. Deep learning has the potential to completely transform the electric power industry by tackling these issues and seizing new opportunities. This would result in improved grid sustainability, resilience, dependability, and more effective use of renewable energy sources and asset management procedures. Researchers, business professionals, and politicians interested in learning more about the state, difficulties, and potential of deep learning applications in electric power systems will find this helpful research paper.

Keywords

• Load forecasting
• Deep reinforcement learning
• Neurons
• Perceptron
• Voltage stability
• Microgrids