Mr.V.Gnanith Hanny, Mr.B.Nagaraju 02 09 The growing penetration of renewable energy sources into isolated microgrid frameworks necessitates advanced control methodologies capable of ensuring dependable voltage stability and superior power quality This research introduces an adaptive fuzzyartificial neural network FuzzyANN control approach specifically designed for voltage regulation within a solarbattery based microgrid operating in remote settings devoid of auxiliary grid support The proposed strategy harmonises the interpretive nature of fuzzy logic with the adaptive learning faculties of artificial neural networks thereby enabling realtime adjustment of control parameters in response to dynamic fluctuations in load profiles solar irradiance and battery stateofcharge Through rigorous simulation analyses the adaptive FuzzyANN controller is demonstrated to effectively mitigate voltage instabilities such as sags and swells whilst concurrently reducing total harmonic distortion at the point of common coupling Moreover when subjected to comparative scrutiny against conventional proportionalintegral and standalone fuzzy schemes the proposed controller exhibits superior transient response diminished steadystate errors and heightened operational reliability The empirical evidence affirms the potential of this intelligent control architecture to enhance both resilience and stability within remote microgrid environments reliant upon inherently intermittent renewable energy sources thus contributing meaningfully towards the broader pursuit of sustainable and autonomous energy systems Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026