Data-Driven Network Graph Theory for Controlling Dynamic Systematic Data Perspective of Smart Power

Abstract

To attain sustainable energy in the increasing climatic adaption and environmental defence, the predominant factor to attain is smart power generation which ensures the secured operation economically. The dynamic nature of the power in the system scale explicitly shows the limitations of the conventional first principle model. To tackle this issue, we propose a Network Graph theory (NGT)-based mathematical modelling incorporated with the data-driven control (DDC) and to attain the optimal output the NGT is optimized using the proposed Mayfly optimization algorithm (MO). This proposed technique is utilized to analyze the uncertainty with fault detection and diagnosis. The factors for controlling the smart power system are discussed and we presented the technique for the fault detection and diagnosis. Simulation demonstrates and analyses the different factors such as detection accuracy. Recall, and precision with the state-of-art techniques. The proposed techniques pave the way for protecting smart power generation along with dynamically controlled systematic data.