Research Projects

Digital Twin technology is a revolutionary tool that uses virtual replicas of physical objects, systems, and processes to simulate, analyze, and optimize real-world scenarios. It allows plants to gain insights into their operations and make informed decisions by monitoring assets in real-time and collecting data from multiple sources. This data can then be used to create predictive models, which can anticipate potential problems before they occur. By leveraging the power of digital twins, plants can improve efficiency, reduce costs, and increase customer satisfaction. AI-driven digital twins are the next step in the evolution of digital technology, allowing plants to monitor and optimize their operations in real-time. This enables engineers to make informed decisions about how to best operate the plant, leading to increased efficiency and cost savings. In this project, Digital Twin technology will be used for predictive maintenance of solar power plants, detecting potential problems before they occur and taking preventive action to avoid costly downtime or damage. The collected data can also be used to optimize the operation of the plant over time, leading to more efficient use of resources and improved energy output. Digital Twin technology allows for better decision-making and faster problem-solving, enabling solar power plants to be monitored in real-time and remotely. It provides insights into the performance of the plant, allowing for resource optimization and identifying potential problems before they occur. Additionally, it can simulate different situations to determine which one would work best for a specific solar power plant. A cutting-edge neural network-based control method with a nonlinear functional dynamics regulator will be developed to create digital twins for the grid connected inverter model.