Abstract: Maximum Power Point Tracking (MPPT) has played a vital role to enhance the efficiency of solar photovoltaic (PV) power generation under varying atmospheric temperature and solar irradiation. However, it is hard to track the maximum power point using conventional linear controllers due to the natural inheritance of nonlinear I-V and P-V characteristics of solar PV systems. Fuzzy Logic Controller (FLC) is suitable for nonlinear system control applications and eliminating oscillations, circuit complexities present in the conventional perturb and observation and incremental conductance methods respectively. Hence, in this paper, FLC is proposed for tracking exact MPPT of solar PV power generation system under varying solar irradiation conditions. The effectiveness of the proposed FLC-based MPPT controller is validated through simulation and analysis using MATLAB/Simulink.
Abstract: This paper discusses the design and analysis of a
hybrid PV-Fuel cell energy system destined to power a DC load. The
system is composed of a photovoltaic array, a fuel cell, an
electrolyzer and a hydrogen tank. HOMER software is used in this
study to calculate the optimum capacities of the power system
components that their combination allows an efficient use of solar
resource to cover the hourly load needs. The optimal system sizing
allows establishing the right balance between the daily electrical
energy produced by the power system and the daily electrical energy
consumed by the DC load using a 28 KW PV array, a 7.5 KW fuel
cell, a 40KW electrolyzer and a 270 Kg hydrogen tank. The variation
of powers involved into the DC bus of the hybrid PV-fuel cell system
has been computed and analyzed for each hour over one year: the
output powers of the PV array and the fuel cell, the input power of
the elctrolyzer system and the DC primary load. Equally, the annual
variation of stored hydrogen produced by the electrolyzer has been
assessed. The PV array contributes in the power system with 82%
whereas the fuel cell produces 18%. 38% of the total energy
consumption belongs to the DC primary load while the rest goes to
the electrolyzer.
Abstract: The paper presents a practical three-phase PWM
inverter suitable for low voltage, low rating energy efficient systems.
The work in the paper is conducted with the view to establishing the
significance of the loss contribution from the PWM inverter in the
determination of the complete losses of a photovoltaic (PV) arraypowered
induction motor drive water pumping system. Losses
investigated include; conduction and switching loss of the devices
and gate drive losses. It is found that the PWM inverter operates at a
reasonable variable efficiency that does not fall below 92%
depending on the load. The results between the simulated and
experimental results for the system with or without a maximum
power tracker (MPT) compares very well, within an acceptable range
of 2% margin.
Abstract: This paper presents a study on Proportional Resonant
(PR) current control with additional PR harmonic compensators for
Grid Connected Photovoltaic (PV) Inverters. Both simulation and
experimental results will be presented. Testing was carried out on a
3kW Grid-Connected PV Inverter which was designed and
constructed for this research.
Abstract: Utilizing solar energy in producing electricity can minimize environmental pollution generated by fossil fuel in producing electricity. Our research was base on the extraction of dye from Roystonea regia fruit by using methanol as solvent. The dye extracts were used as sensitizers in Dye-sensitized solar cell (DSSCs). Study was done on the electrical properties from the extracts of Roystonea regia fruit as Dye-sensitized solar cell (DSSCs). The absorptions of the extracts and extracts with dye were determined at different wavelengths (350-1000nm). Absorption peak was observed at 1.339 at wavelength 400nm. The obtained values for methanol extract Roystonea regia extract are, Imp = 0.015mA, Vmp = 12.0mV, fill factor = 0.763, Isc= 0.018 mA and Voc = 13.1 mV and efficiency of 0.32%. .The phytochemical screening was taken and it was observed that Roystonea regia extract contained less of anthocyanin compared to flavonoids. The nanostructured dye sensitized solar cell (DSSC) will provide economically credible alternative to present day silicon p–n junction photovoltaic.
Abstract: In this paper, ANN controller for maximum power point tracking of photovoltaic (PV) systems is proposed and PV modeling is discussed. Maximum power point tracking (MPPT) methods are used to maximize the PV array output power by tracking continuously the maximum power point. ANN controller with hill-climbing algorithm offers fast and accurate converging to the maximum operating point during steady-state and varying weather conditions compared to conventional hill-climbing. The proposed algorithm gives a good maximum power operation of the PV system. Simulation results obtained are presented and compared with the conventional hill-climbing algorithm. Simulation results show the effectiveness of the proposed technique.
Abstract: This paper presents a comparison between Proportional Integral (PI) and Proportional Resonant (PR) current controllers used in Grid Connected Photovoltaic (PV) Inverters. Both simulation and experimental results will be presented. A 3kW Grid-Connected PV Inverter was designed and constructed for this research.
Abstract: Although lighting systems powered by Photovoltaic
(PV) cells have existed for many years, they are not widely used,
especially in lighting for buildings, due to their high initial cost and
low conversion efficiency. One of the technical challenges facing PV
powered lighting systems has been how to use dc power generated by
the PV module to energize common light sources that are designed to
operate efficiently under ac power. Usually, the efficiency of the dc
light sources is very poor compared to ac light sources. Rapid
developments in LED lighting systems have made this technology a
potential candidate for PV powered lighting systems. This study
analyzed the efficiency of each component of PV powered lighting
systems to identify optimum system configurations for different
applications.
Abstract: As known that efficiency of photovoltaic cells is not
high as desired level. Efficiency of PVs could be improved by
selecting convenient locations that have high solar irradiation,
sunshine duration, mild temperature, low level air pollution and dust
concentration. Additionally, some environmental parameters called
derating factors effect to decrease PV efficiencies such as cloud, high
temperature, aerosol optical depth, high dust concentration, shadow,
snow, humidity etc. In this paper, all parameters that effect PV
efficiency are considered in detail under climatic conditions of
Istanbul. A 750 Wp PV system with measurement devices is
constructed in Maslak campus of Istanbul Technical University.
Abstract: This paper presents a novel methodology for Maximum Power Point Tracking (MPPT) of a grid-connected 20 kW Photovoltaic (PV) system using neuro-fuzzy network. The proposed method predicts the reference PV voltage guarantying optimal power transfer between the PV generator and the main utility grid. The neuro-fuzzy network is composed of a fuzzy rule-based classifier and three Radial Basis Function Neural Networks (RBFNN). Inputs of the network (irradiance and temperature) are classified before they are fed into the appropriated RBFNN for either training or estimation process while the output is the reference voltage. The main advantage of the proposed methodology, comparing to a conventional single neural network-based approach, is the distinct generalization ability regarding to the nonlinear and dynamic behavior of a PV generator. In fact, the neuro-fuzzy network is a neural network based multi-model machine learning that defines a set of local models emulating the complex and non-linear behavior of a PV generator under a wide range of operating conditions. Simulation results under several rapid irradiance variations proved that the proposed MPPT method fulfilled the highest efficiency comparing to a conventional single neural network.