Recently, switched inductor and switched capacitor techniques in DC-DC converter are recommended to achieve high voltage by using the principle of parallel charging and series discharging of reactive elements. It is noteworthy that four diodes, one high voltage rating switch, two inductors are required to design classical Switched Inductor Boost Converter (classical SIBC). Moreover, in classical SIBC, the switch voltage stress is equal to the output voltage. In this paper, modified SIBC (mSIBC) is proposed with reduced voltage stress across active switches. The proposed mSIBC configuration is transformer-less and simply derived by replacing the one diode of the classical switched inductor structure with an active switch. As a result, mSIBC required low voltage rating active switches, since the total output voltage is shared into two active switches. Moreover, the proposed mSIBC required the same number of components compared to the classical SIBC. The Continuous Conduction Mode (CCM) and Discontinuous Conduction Mode (DCM) analysis, the effect of non-idealities on voltage gain, design methodology, and comparison of mSIBC are presented in detail. The operation and performance of the designed 500W mSIBC are validated through simulation and experimental work.

The solar PV based power generation systems are growing faster due to the depletion of fossil fuels and environmental concerns. Combining PV panels and energy buffers such as battery through multi-port converter is one of the viable solutions to deal with the intermittency of PV power. The goal of this paper is to design and analyze the proposed triple port DC-DC buck-boost converter for high step-up/step-down applications. It has two unidirectional ports (port-1 and port-3) and one bi-directional port (port-2) for harnessing photovoltaic energy and charging the battery. At port-1, the combined structure of buck and buck-boost converter is used with a particular arrangement of switches and inductors. The step-up/step-down voltage conversion ratio is higher than the conventional buck-boost converter, and the polarity of the output voltage is maintained positive. The battery is added at the bi-directional port, for the storage of energy through the bi-directional boost converter. The switches operate synchronously for most of the modes making the control strategy simple. The characteristics and modes of operation along with a switching strategy, are elaborated. Experimental results are presented which validate the agreement with the developed theoretical expectation.

This research study presents the fuzzy space vector pulse width modulation (FSVPWM) method of current control for three‐phase voltage source inverter. The hybrid fuzzy particle swarm optimisation‐based maximum power point (MPP) tracking algorithm has been employed to obtain high tracking efficiency as well as optimal MPP under adverse operating states. The FSVPWM technique provides less current harmonic content, fixed switching pattern, protection from over current, low switching losses and able to handle the non‐linearities and uncertainties of the photovoltaic‐wind grid integrated system. Grid synchronisation with sinusoidal current injection is achieved using the inverter controller. Fuzzy logic controller‐based SVPWM controller compensates current error and provides DC‐link utilisation with high efficiency. The experimental responses have been validated using MATLAB/Simulink interfaced real‐time dSPACE DS 1104 controller. Irrespective of solar irradiance and wind velocity, the proposed hybrid system obeys MPP accurately with high performance.

Global climatic changes and increased carbon footprints provided the main impetus for the decrease in the use of fossil fuels for electricity generation and transportation. Matured manufacturing technologies of solar PV panels and on-shore and off-shore windmills have brought down the cost of generation of electricity using solar energy on par with conventional fossil fuel. Initially, solar and wind power generation was envisioned for microgrids, serving small local communities. However, advancements in power electronics have now facilitated large solar and wind farms to be integrated with main power grids. In this context, hosting capacity, which is the amount of distributed energy resources a grid can accommodate, without significant infrastructure up-gradation, has gained importance. In determining the hosting capacity at a particular location, the uncertainties of wind and solar power generation play a role. Effective forecasting models using time-series weather data can be built to predict wind and solar power generation. This forecast is essential to ensure proper grid operation and control when renewable energy sources are already installed. The forecast is also useful in the planning stages for investment decisions and distribution system planning. While long-term forecasts are rarely needed for the operation of integrated grids, accurate short-term predictive models are necessary for scheduling. This paper presents an extensive review of various forecast models available in the literature. The study mainly focuses on the short-term forecast, providing a critical review of the duration of data used in each model and a synoptic comparison of their performance indices.

This research work presents a modified sine-cosine optimized maximum power point tracking (MPPT) algorithm for grid integration. The developed algorithm provides the maximum power extraction from a photovoltaic (PV) panel and simplified implementation with a benefit of high convergence velocity. Moreover, the performance and ability of the modified sine-cosine optimized (MSCO) algorithm is equated with recent particle swarm optimization and artificial bee colony algorithms for comparative observation. Practical responses is analyzed under steady state, dynamic, and partial shading conditions by using dSPACE real controlling board laboratory scale hardware implementation. The MSCO-based MPPT algorithm always shows fast convergence rate, easy implementation, less computational burden and the accuracy to track the optimal PV power under varying weather conditions. The experimental results provided in this paper clearly show the validation of the proposed algorithm.

This paper introduces a novel control system with maximum power point tracker (MPPT) for the photovoltaic system with grid integration. Hybrid adaptive neuro-fuzzy inference system (ANFIS) and artificial bee colony (ABC) algorithm employed to optimize the membership function. Hence, for minimizing the root mean square error (RMSE), this controls the SEPIC-based MPPT algorithm to achieve rapid PV power tracking. The system performance is improved by fuzzy logic control (FLC), which generates the switching signal to the power switches of the inverter. A dSPACE (DS1104) control board employed for experimental validation of MPPT and inverter control strategies. The novelty of the proposed hybrid MPPT controller is the optimal tuning of ANFIS membership function with the ABC algorithm and been neither discussed before for PV power applications. The experimental responses completely validate the reliability of the PV grid integration with anti-islanding protection. The recentness of this research work is PV MPPT functioning using the hybrid ANFIS-ABC-based algorithm, been not described practically by any researchers in the past works.