Integration of Artificial Intelligence in Power System Operations: Enhancing Predictive Maintenance

Abstract

Since the depletion of fossil fuels, the world has become increasingly reliant on renewable energy sources. With each passing year, reliance on renewable energy sources increases significantly. As a result, complex and hybrid power generation systems are being designed and developed to meet the energy needs and ensure energy security in any country. Continuous technological improvements and efforts to provide uninterrupted power to end users depend heavily on an efficient and fault-tolerant operation and maintenance system. Therefore, innovative algorithms and techniques using artificial intelligence have been introduced to reduce equipment and plant downtime. Efforts are underway to develop robust diagnostic maintenance systems that can identify faults before they occur. To achieve this goal, AI techniques and tools are being used in power systems to increase the overall efficiency of these diagnostic maintenance systems. This research provides an overview of the frameworks for using AI techniques in power system operations, focusing on predictive maintenance, which contributes to reducing downtime and improving the quality and reliability of operational processes.  Research discusses the most important artificial intelligence technologies used in energy systems and reflects the ability of these devices to identify errors in errors and detect weak and deficient places before failure. It also reveals the benefits and benefits that these technologies receive and need to get global support to automate the energy sector with the aim of gaining stability and efficiency.