2021 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2021 - Proceedings
For every researcher, uncertainties connected to the Smart Grid and Distribution Network are a common element. Thousands of sources are part of the renewable energy system and they are situated at varying distances. In due course, certain sources may be inefficient, or a natural disaster can destroy the source causing power supply failures or require routine maintenance to improve service. Some of these defects grow slowly and some have a strong impact. The systems, however, contain safety components to remove the problem, but impact the generation of electricity and conduct intelligent grid errors. An intelligent microgrid should feature an intelligent fault location detection and fault removal device to avoid unwanted loads and insecurities. This research shaped a smart grid system, together with a defective technique to identify uncertainty efficiently. The originality of this research is to model an SMG based on an isolated small area's SGC mechanism in conjunction with a defective detecting system to manage uncertainties in an efficient way. The FLD technique is focused upon wavelet trigger signal and mathematical morphology method. In this approach, the wavelet activator signal created by the equivalent current or voltage for a short period of time is simulated to go to both terminals to determine that the fault takes place in the short branch using the mathematical morphological technique. The SMG modeled system is separated into a few branches for the FLD system. A smart technique is presented via mixed human machine interface (HMI) for SMG performance and FLD monitoring. The integrated process of the SMG system modeling in the MATLAB performance validation simulation platform.