A radial basis function neural network based approach for the electrical characteristics estimation of a photovoltaic module

The design process of photovoltaic (PV) modules can be greatly enhanced by using advanced and accurate models in order to predict accurately their electrical output behavior. The main aim of this paper is to investigate the application of an advanced neural network based model of a module to improve the accuracy of the predicted output I--V and P--V curves and to keep in account the change of all the parameters at different operating conditions. Radial basis function neural networks (RBFNN) are here utilized to predict the output characteristic of a commercial PV module, by reading only the data of solar irradiation and temperature. A lot of available experimental data were used for the training of the RBFNN, and a backpropagation algorithm was employed. Simulation and experimental validation is reported

Development of a Multi-Objective Evolutionary Algorithm for Strain-Enhanced Quantum Cascade Lasers

An automated design approach using an evolutionary algorithm for the development of quantum cascade lasers (QCLs) is presented. Our algorithmic approach merges computational intelligence techniques with the physics of device structures, representing a design methodology that reduces experimental effort and costs. The algorithm was developed to produce QCLs with a three-well, diagonal-transition active region and a five-well injector region. Specifically, we applied this technique to AlxGa1xAs/InyGa1yAs strained active region designs. The algorithmic approach is a non-dominated sorting method using four aggregate objectives: target wavelength, population inversion via longitudinal-optical (LO) phonon extraction, injector level coupling, and an optical gain metric. Analysis indicates that the most plausible device candidates are a result of the optical gain metric and a total aggregate of all objectives. However, design limitations exist in many of the resulting candidates, indicating need for additional objective criteria and parameter limits to improve the application of this and other evolutionary algorithm methods

Efficient dynamic modeling of reflective semiconductor optical amplifier

“Copyright © [2013] IEEE. Reprinted from IEEE Journal of Selected Topics in Quantum Electronics. ISSN: 1932-4553. This material is posted here with permission of the IEEE. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.”RSOA is considered a strong candidate to play an important role in realizing the next generation WDM PON, based on the wavelength reuse concept. Therefore, accurate and efficient modeling of RSOA is of significant importance. We present a time-domain wideband model for simulation of spatial and temporal distribution of photons and carriers in a bulk RSOA. A trade-off between the accuracy and the computational efficiency of the model is studied. Multi-objective genetic algorithm is utilized for parameter extraction. Experimental validation has been performed for continuous wave input, NRZ and QPSK signaling pulses up to 40 Gb/s of bit rate, in both amplification and remodulation regimes. Saturation, noise, chirp and signal broadening are successfully predicted, while reducing the computational time compared to other wideband models

Automated design of multi junction solar cells by genetic approach : reaching the >50% efficiency target

The proper design of the multi-junction solar cell (MJSC) requires the optimisation search through the vast parameter space, with parameters for the proper operation quite often being constrained, like the current matching throughout the cell. Due to high complexity number of MJSC device parameters might be huge, which makes it a demanding task for the most of the optimising strategies based on gradient algorithm. One way to overcome those difficulties is to employ the global optimisation algorithms based on the stochastic search. We present the procedure for the design of MJSC based on the heuristic method, the genetic algorithm, taking into account physical parameters of the solar cell as well as various relevant radiative and non-radiative losses. In the presented model, the number of optimising parameters is 5M + 1 for a series constrained M-junctions solar cell. Diffusion dark current, radiative and Auger recombinations are taken into account with actual ASTM G173-03 Global tilted solar spectra, while the absorption properties of individual SCs were calculated using the multi band k · p Hamiltonian. We predicted the efficiencies in case of M = 4 to be 50:8% and 55:2% when all losses are taken into account and with only radiative recombination, respectively. Keywords: Multi Junction Solar Cells, Current Matching, III-V semiconductors, Auger effect, Genetic Algorith

On the characterization of solar cells using advanced imaging techniques

Photovoltaic (PV) cells are devices capable of producing electricity - in particular, from the abundant resource of sunlight. Solar energy (from PV cells) provides a sustainable alternative to fossil fuel energy sources such as coal and oil. PV cells are typically strung in series in PV modules to generate the current and voltage required for commercial use. However, PV cell performance can be limited by defects and degradation. Under operational conditions due to mismatch and shading, individual cells within a PV module can be forced to operate in their reverse bias regime. Depending on the severity of the reverse bias and the defects present in the cell, the longevity of the cell and/or the module can be affected. Reverse bias (assuming bypass diodes are absent) can result in localised heating that can affect the encapsulant polymer’s longevity as well as degrade the cell’s performance over time. However, under more severe reverse bias, the cell could fail, drastically affecting the performance of the module. PV cells can be characterised using various opto-electronic non-destructive techniques, this provides a set of powerful tools which allow the application of multiple such techniques to the same sample. Furthermore, this allows for an in-depth study of the device. Dark Current-Voltage (I-V) measurements, Electroluminescence (EL), Infrared (IR) thermography, Light Beam Induced Current (LBIC) measurements, and the associated techniques are all examples of such tools and are used within this study. An experimental setup was developed to perform dark I-V measurements, EL imaging, IR thermography and LBIC measurements. Part of the development of the experimental setup was the design of an enclosure in which to perform all the measurements. The enclosure minimised internal reflection, and isolated the experiment from electromagnetic radiation. Due to the complex mathematical model applied to the I-V curve, an Evolutionary Algorithm was used to determine optimal parameter values for the equation. More specifically, a Genetic Algorithm was used in the Parameter Optimisation (or Extraction) of the dark I-V parameters based upon the two-diode model for PV cells. The resulting parameters give an indication of the material and device quality. However, to determine the spatial distribution of the defects that effect the I-V response of the device, various imaging techniques were utilised. LBIC is a technique that uses a focussed light beam to raster scan across the surface of a PV cell. The local photo-induced current/voltage can then be measured and compiled into a response map. LBIC was used to determine the local current response across the device. The intensity distribution of EL signal is related to the local junction voltage and the local quantum efficiency. EL intensity imaging with a Si CCD camera was used to determine the spatial distribution of features visible both in the forward bias and in the reverse bias. The experimental setup utilised had a micron scale resolution. A voltage dependent approach was utilised to further characterise features observed. In forward bias, the local junction varies across the device due to parasitic resistances such as series and shunt resistance. At higher forward bias conditions (in the vicinity of and higher than maximum power voltage), series resistance becomes a limiting factor. Therefore, utilising a voltage dependent approach allows for the determination of a series resistance map from voltage dependent EL images. In reverse bias, localised radiative processes can be imaged. These radiative processes are related to defects in the device, such as Al stains, FeSi2 needles and avalanche breakdown. The processes are related to highly localised current flow; this causes localised heating which degrades the device. The voltage dependent Reverse Bias EL (ReBEL) imaging was also used to determine the local breakdown voltage of radiative reverse features. Dark IR thermography is a technique used in the identification of high current sites that leads to localised Joule heating, particularly in reverse bias. In this study, thermography was used to identify breakdown sites and shunts. The results of this study allow for an in-depth analysis of defects found in multi-crystalline Si PV cells using the opto-electronic techniques mentioned above. The multi-pronged approach allowed from a comparison of the various opto-electronic techniques, as well as a more in-depth characterisation of the defects than if only one technique was used