Priyanshu Gautam

The modern multi-microgrid (MMG) systems with high renewable penetration are subject to voltage instability, inefficiency in power sharing, and long transient recovery, which are drawbacks that the conventional fixed droop controllers cannot tackle. In this context, research presents the AI-Enhanced Adaptive Droop Control (AI-ADC) framework that combines rule-based tuning with a neural-network model that can predict the optimal droop coefficients in real time. The controller is validated through a high-fidelity MATLAB/Simulink model incorporating the dynamics of PV, wind, BESS, and a converter, and subjected to load steps, irradiance fall-off, DER failure, and communication delay situations. The controller's performance with respect to voltage undershoot has been better by over 60%, the voltage steady-state deviation has been under 0.25 V, the power-sharing error has been reduced to below 3%, and the settling time has been almost four times faster than that of the conventional methods. The AI-ADC, by allowing predictive and adaptive droop adjustments, presents a scalable, efficient, and highly resilient control solution for the next-generation MMG networks dominated by renewable energy sources.

Floods are really harmful surprise elements of nature, and as such, making short-term flood prediction very accurate is a major requirement of early-warning systems done in such a way as to prevent human and property losses, economic disruption, etc. Still, they are hard to guess due to the very intricate combination of hydrological, climatic, and other environmental factors. The present paper offers an interpretable ensemble machine learning framework for predicting the times of flood events around 1-3 days ahead via the use of hydrology, climate, and environmental indicators together. The method offers great help through data preprocessing, feature normalisation, and the application of various regression models to cost-continuous flood probabilities estimation. Random Forest and Gradient Boosting algorithms are used to find and improve prediction accuracy through capturing non-linear relationships, while a hybrid ensemble method combines the advantages of individual models. Decision-making is made simplistic by the conversion of probabilistic outputs into binary flood alerts at a predetermined fixed threshold. The framework is executed and tested in a MATLAB-Simulink setting, and the analysis confirms its readiness for real-time operations. The results from the experiments indicate that the learning through the ensemble approach has significantly improved the prediction reliability and interpretability as compared to single model techniques.