In this paper, nonlinear sliding mode control (SMC) techniques formulated for extracting maximum power from a solar photovoltaic (PV) system under variable environmental conditions employing the perturb and observe (P and O) maximum power point tracking (MPPT) technique are discussed. The PV system is connected with load through the boost converter. A mathematical model of the boost converter is derived first, and based on the derived model, a SMC is formulated to control the gating pulses of the boost converter switch. The closed loop system stability is verified through the Lyapunov stability theorem. The presented control scheme along with the solar PV system is simulated in MATLAB (matric laboratory) (SMC controller and PWM (Pulse Width Modulation) part) and PSIM (Power electronics simulations) (solar PV and MPPT algorithm) environments using the Simcoupler tool. The simulation results of the proposed controller (SMC) are compared with the classical proportional integral derivative (PID) control scheme, keeping system parameters and environmental parameters the same.

Two technology opportunities, integration of renewable energy sources and the electrification of vehicles are being encouraged to reduce dependency on fossil fuels and pollution problems. Nevertheless, the huge increase of plug-in hybrid electric vehicles (PHEVs) on roads will cause an additional load in demand, especially at rush hours, and therefore, threatens the stability of existing power grids. Considering PHEV stay for several hours in the workplace, (i.e., university), this may provide an inimitable framework to charge PHEV from wind in the workplace. This paper introduces the possibility of introducing intelligent control of wind power and battery storage units as supplementary power sources for future PHEV charging demands during rush hours. The operation of the proposed algorithm is based on the priority levels of PHEVs charging, and fluctuations in DC link voltage levels due to the variation in wind speed. The priorities of PHEVs charging are developed according to their power requirements, maximum rating of distribution transformer and park duration of PHEVs in the workplace during wind speed. Various non-isolated proportional–integral controllers and improved intelligent fuzzy control are used to keep a minimum critical DC link voltage to permit the power conditioning system to operate a charging station uninterruptedly, even at low wind speed. The improved intelligent fuzzy controller also contributes to minimizing the stress on the DC bus and ensures quality output power. The performance of the proposed charging station is verified for the real PHEV under real-world record of wind speed. All the energy sources, electric charging station and their controllers are designed in MATLAB/Simulink. Finally, the feasibility of proposed charging station is checked experimentally in the laboratory.

Photovoltaic system is utilized to generate energy that relies upon the ecological conditions, for example, temperature, irradiance, and the load associated with it. Considering the non-linear component of photovoltaic (PV) array and the issue of low effectiveness because of the variable natural conditions, the Maximum Power Point Tracking (MPPT) method is required to extract the maximum power from the PV system. The adopted control is executed utilizing an Adaptive Sliding Mode Controller (ASMC) and the enhancement is actualized utilizing an Improved Pattern Search Method (IPSM). This work employs IPSM based optimization approach in order to command the underlying ASMC controller. The upper level decision determines the sliding surface for the adaptive controller. As a non-linear strategy, the stability of the adaptive controller is guaranteed by conducting a Liapunov analysis. On the practical side, MATLAB/Simulink is used as simulator for the controller implementation and coupling with PSIM in order to connect it with the PV system object of control. The simulation results validate that the proposed controller effectively improves the voltage tracking, system power with reduced chattering effect and steady-state error. The performance of the proposed control architectures is validated by comparing the proposals with that of the well-known and widely used Proportional Integral Derivative (PID) controller. That operated as a lower level controller for a Perturb & Observe (P&O) and Particle Swarm Optimization (PSO).

In this research work, an adaptive sliding mode controller (SMC) has been investigated to eliminate the chattering effect present in other classical sliding mode controller. The paper presents a photovoltaic (PV) system with an adaptive sliding mode controller based on Perturb & Observe (P&O) Maximum Power Point Tracking (MPPT) algorithm. A standalone PV based on a boost converter is realized. P&O algorithm is simulated in PSIM to generate the reference which is supposed to be tracked by the adaptive sliding mode controller. The proposed controller is designed and implemented in MATLAB/Simulink for tracking reference. Simulations have been carried out in MATLAB(Adaptive SMC and PWM(Pulse Width Modulation) part) and PSIM(solar PV and MPPT algorithm) environments using Simcoupler tools to verify the results. Simulation results prove the improved performance of adaptive sliding mode controller as compared to other classical sliding mode control method, while keeping same the system parameters and environmental parameters.