In recent years, with the advent of globalization, the world is witnessing a steep rise in its energy consumption. The world is transforming itself into an industrial and knowledge society from an agricultural one which in turn makes the growth, energy intensive resulting in emissions. Energy modeling and energy planning is vital for the future economic prosperity and environmental security. Soft computing techniques such as fuzzy logic, neural networks, genetic algorithms are being adopted in energy modeling to precisely map the energy systems. In this paper, an attempt has been made to review the applications of fuzzy logic based models in renewable energy systems namely solar, wind, bio-energy, micro-grid and hybrid applications. It is found that fuzzy based models are extensively used in recent years for site assessment, for installing of photovoltaic/wind farms, power point tracking in solar photovoltaic/wind, optimization among conflicting criteria. The review indicates that fuzzy based models provide realistic estimates.

Applications of fuzzy logic in renewable energy systems – A review

Grid integrated renewable DG systems: A review of power quality challenges and state-of-the-art mitigation techniques

Solar energy, at the present time is considered as an important source in electricity generation. Electricity from the solar energy can be generated using solar photovoltaic (PV) modules. The maximization of solar power extracted from a PV module is of special concern as its efficiency is very low. The output power of a PV module is highly dependent on the geographical location and weather conditions such as solar irradiation, shading and temperature. To obtain maximum power from PV module, photovoltaic power system usually requires maximum power point tracking (MPPT) controller. In this paper, an adaptive neuro-fuzzy inference system (ANFIS) based maximum power point tracker for PV module has been presented. To extract maximum power, a DC–DC boost converter is connected between the PV module and the load. The duty cycle of DC–DC boost converter is modified with the help of the ANFIS reference model, so that maximum power is transferred to load. Due to the complexity of the tracker mechanism and non-linear nature of photovoltaic system, the artificial intelligence based technique, especially the ANFIS method, is used in this paper. In order to observe the maximum available power of PV module, the ANFIS reference model directly takes in operating temperature and irradiance level as input. The response of proposed ANFIS based control system shows accuracy and fast response. The simulation result reveals that the maximum power point is tracked satisfactorily for varying irradiance and temperature of PV module. Simulation results are provided to validate the concept.

Modeling of solar PV module and maximum power point tracking using ANFIS

Novel seven-parameter model for photovoltaic modules

Adaptive fuzzy gain scheduling PID controller for maximum power point tracking of photovoltaic system

A B ST R A C T Maximum power point trackers (MPPTs) play a vital role in photovoltaic (PV) systems because they increase the efficiency of the solar photovoltaic system by increasing the power output. MPPT algorithms are necessary because PV arrays have a non linear voltage-current characteristic with a unique point where the power produced is maximum. The output power from the solar panel varies with solar irradiance, temperature and so on. To increase the power extracted from the solar panel, it is necessary to operate the photovoltaic (PV) system at the maximum power point (MPP). This paper presents the Matlab/simulink arrangement of perturb & observe (P&O) and incremental conductance (INC) MPPT algorithm which is responsible for driving the dc-dc boost converter to track maximum power point (MPP). This paper also presents the theoretical analysis of variable step size (VSS) of INC MPPT which can effectively improve the tracking speed and accuracy of maximum power.

S. Thangavel

Papers

Design and implementation of Maximum Power Point Tracking (MPPT) algorithm for a standalone PV system

Experimental verification of PV based Dynamic Voltage Restorer (PV-DVR) with significant energy conservation

Photovoltaic based dynamic voltage restorer with power saver capability using PI controller

Digital twin for oil pipeline risk estimation using prognostic and machine learning techniques

Instantaneous reference current scheme based power management system for a solar/wind/fuel cell fed hybrid power supply