Synchronous generators (SGs) are expensive and essential parts of power networks that need to be safeguarded from flaws and unusual operating conditions. The performance of the power system as a whole can be negatively impacted by internal and external problems that can seriously harm SGs. Electric utilities utilize numerical, solid-state, and electromagnetic relays constructed on a differential protection scheme to protect SGs. More machine models and analyses of how synchronous machines operate with intrinsic flaws are nonetheless required. Various synchronous generators have been successfully controlled using traditional control theory and nature-inspired metaheuristic stochastic optimization techniques like evolutionary algorithms (EAs), particle swarm optimization (PSO), differential evolution (DE), genetic algorithms (GA), firefly algorithms (FA), and artificial bee colonies (ABC) algorithm. defects. Artificial neural networks (ANNs), wavelet packet decomposition, and MFO-based FLC have all been used to analyze synchronous generators with internal and external ground faults.