This Research Topic is Volume II of a series. The previous volume, which has attracted over 11k views can be found here: Advances in Artificial Intelligence Application in Data Analysis and Control of Smart Grid
Smart grid (SG) is considered a form of intelligent system that allows the electric grid to perform its functions efficiently. The SG is a network that allows for the flow of electrical energy and data, where the data is used to make intelligent decisions in the operation of the electric grid. Artificial intelligence (AI) techniques, such as expert systems (ES), Machine Learning (ML), and deep Learning (DL) have brought an advancing frontier in power electronics and power engineering with their powerful data processing capabilities. The SG relies on the flow of data to make its intelligent control; therefore, AI technology is a perfect fit for the SG. The application of AI technology in the SG has the potential to improve the intelligence of the SG.
This Research Topic is focused on ways of improving the data analysis and control of SG by leveraging technologies. Manuscripts with the progress made in solving a range of miscellaneous and critical problems in SG by leveraging AI methods such as ES, ML, and DL methods are welcome. Reviews and original research that describe the latest developments in this field are considered for publication in this research topic.
The scope of this Research Topic will include the following themes, but are not limited to:
• Data-driven and artificial intelligence approaches to enhancing SG flexibility and resilience.
• Application of expert system, machine learning and deep learning, reinforcement learning, and transfer learning for applications in SG.
• The development of artificial intelligence to ensure high reliability and stability of power systems.
• AI for studies in SG system operation protection, comprehensive planning, and control.
• Development of AI for SG diagnosis and diagnosis.
• Defect detection of modern power generation systems using adaptive neuro-fuzzy systems.
• Space vector fault pattern identification of a smart grid subsystem by neural mapping.
• Control techniques, mathematical programming methods, optimization techniques and metaheuristic algorithms applied in SG.
• AI and optimization techniques for green energy and carbon footprint.
• Novel applications of AI-based smart grids in smart cities, smart transportation, smart healthcare, and smart manufacturing.
• Application cases of image processing in SG.
• Application of natural language processing in SG.