Research Publications

A nonlinear sliding mode controller is proposed for a single-stage grid-connected photovoltaic system with a novel nonlinear sliding surface. The proposed controller is designed to ensure maximum power extraction with unity power factor. The new surface results in a first-order sliding mode controller while achieving maximum power extraction by tracking the DC-link voltage (Vdc) of the PV system. The proposed controller is compared with existing sliding mode controllers through numerical simulation and experimental results. The proposed controller demonstrates superior performance in terms of power extraction and robustness when there are uncertainties and disturbances in the PV system.

This paper proposes a unique method for estimating three-diode photovoltaic (PV) model parameters using an enhanced Newton–Raphson (NR) method and the selective opposition-based grey wolf optimization (GWO) algorithm with variable weights. The selective opposition mechanism improves the GWO algorithm's exploration and exploitation capabilities, enabling efficient parameter space search. Variable weights modify the impact of various search operators, enhancing convergence rate and solution quality. The enhanced NR approach iteratively revises estimated parameter values from initial guesses. Experiments on a real-world PV dataset show superior robustness, convergence speed, and accuracy over state-of-the-art methods.

Electric vehicles are an increasing mode of transportation with rising sales and falling prices globally. This project provides a scheme for a futuristic scenario with large numbers of EV charging stations alongside homes and office buildings, and solar panels supplying considerable power to highly populated areas. By using various power converters, algorithms, and control techniques — considering different sources and loads — an optimal solution to the power demand is realised. This work deals with charging and discharging of an EV from both solar panels and the power grid using bidirectional converters and various controlling patterns, demonstrating the feasibility of future interconnected energy systems.

Greenhouse climate is a nonlinear and uncertain system consisting of several major environmental factors such as temperature, humidity, light intensity and CO2 concentration. A complex control system with several actuators is designed to control these factors while minimising energy consumption. The speed of single-phase induction motors in fans and ventilators — the most energy-consuming units in greenhouses — is varied in accordance with varying parameters inside the greenhouse. An efficient IoT-based remote monitoring platform enables real-time parameter monitoring and control. An application is developed to facilitate remote monitoring and control of greenhouse parameters including temperature and humidity.

Robust nonlinear PV-battery tied hybrid system equivalent circuit mathematical modelling is developed for optimised battery performance. The proposed nonlinear controllers are designed using sliding mode techniques. On the sliding manifold, the proposed control algorithm ensures maximum power extraction, battery management control, and load regulation. The reference current is generated to extract maximum power from the PV system. The proposed controller is optimised with different modes of operation based on pre-defined constraints of the battery charging and discharging threshold current limits and State-of-Charge (SoC) using an individual control algorithm. The designed controllers are validated through numerical simulation and experimental results under nominal and robust conditions.