•Hybrid model for short-term power forecasting of a GCPV plant is designed.•Designed hybrid model performs better than both the SARIMA and the SVM model.•The model does not require any forecasted ...meteorological parameters.
In this work, a new hybrid model for short-term power forecasting of a grid-connected photovoltaic plant is introduced. The new model combines two well-known methods: the seasonal auto-regressive integrated moving average method (SARIMA) and the support vector machines method (SVMs). An experimental database of the power produced by a small-scale 20kWp GCPV plant is used to develop and verify the effectiveness of the proposed model in short-term forecasting. Hourly forecasts of the power produced by the plant were carried out for a few days showing a quite good accuracy. A comparative study has also been introduced showing that the developed hybrid model performs better than both the SARIMA and the SVM model.
•Effective ANN-based models for forecasting the power produced by a LS-GCPV plant is presented.•A simple but accurate analytical expressions have been developed to forecast the power output.•The ...effectiveness of the split of the dataset into three different ones has been shown.•The better ability of ANN-models to forecast the power produced has been verified experimentally.
In this paper, a simple but accurate approach for short-term forecasting of the power produced by a Large-Scale Grid Connected Photovoltaic Plant (LS-GCPV) is presented. A 1-year database of solar irradiance, cell temperature and power output produced by a 1-MWp photovoltaic plant located in Southern Italy is used for developing three distinct artificial neural network (ANN) models, to be applied to three typical types of day (sunny, partly cloudy and overcast). The possibility of obtaining accurate results by using solely the monitored data rather than knowing the actual architecture and details of the plant is a notable advantage; in particular, the method’s reliability gives to operation and maintenance and to grid operators excellent confidence in the evaluation of the performance of the plant and in the programming of the dispatching plans, respectively.
This work proposes a novel fault diagnostic technique for photovoltaic systems based on Artificial Neural Networks (ANN). For a given set of working conditions - solar irradiance and photovoltaic ...(PV) module's temperature - a number of attributes such as current, voltage, and number of peaks in the current–voltage (I–V) characteristics of the PV strings are calculated using a simulation model. The simulated attributes are then compared with the ones obtained from the field measurements, leading to the identification of possible faulty operating conditions. Two different algorithms are then developed in order to isolate and identify eight different types of faults. The method has been validated using an experimental database of climatic and electrical parameters from a PV string installed at the Renewable Energy Laboratory (REL) of the University of Jijel (Algeria). The obtained results show that the proposed technique can accurately detect and classify the different faults occurring in a PV array. This work also shows the implementation of the developed method into a Field Programmable Gate Array (FPGA) using a Xilinx System Generator (XSG) and an Integrated Software Environment (ISE).
This work aims to evaluate the effect of soiling on energy production for large-scale ground mounted photovoltaic plants in the countryside of southern Italy. Since the effect of pollution can ...seriously compromise the yield of solar parks, the results obtained in this study can help the operation and maintenance responsible in choosing the proper washing schedule and method for their plants and avoid wasting money. In order to determine the losses due to the dirt accumulated on photovoltaic modules, the performances at Standard Test Conditions (STC – Irradiance: 1000
W/m
2; Cell temperature: 25
°C; Solar spectrum: AM 1.5) of two 1
MW
p solar parks before and after a complete clean-up of their photovoltaic modules have been compared. The performances at STC of the two plants have been determined by using a well-known regression model that accepts as an input two climate data (the in-plane global irradiance and the photovoltaic module temperature), while the output results in one electrical parameter (the produced power). A regression model has been preferred to a common performance ratio analysis because this latter is too much influenced by the seasonal variation in temperature and by the plant availability. The results presented in this work show that both the soil type and the washing technique influence the losses due to the pollution. A 6.9% of losses for the plant built on a sandy soil and a 1.1% for the one built on a more compact soil have been found. Finally, these results have been used in order to compare the washing costs with the incomings due to the performance improvement.
A memory of errors in sensorimotor learning Herzfeld, David J.; Vaswani, Pavan A.; Marko, Mollie K. ...
Science (American Association for the Advancement of Science),
09/2014, Letnik:
345, Številka:
6202
Journal Article
Recenzirano
Odprti dostop
The current view of motor learning suggests that when we revisit a task, the brain recalls the motor commands it previously learned. In this view, motor memory is a memory of motor commands, acquired ...through trial-and-error and reinforcement. Here we show that the brain controls how much it is willing to learn from the current error through a principled mechanism that depends on the history of past errors. This suggests that the brain stores a previously unknown form of memory, a memory of errors. A mathematical formulation of this idea provides insights into a host of puzzling experimental data, including savings and meta-learning, demonstrating that when we are better at a motor task, it is partly because the brain recognizes the errors it experienced before.
IN740H, a Ni-based superalloy comprising a low-volume fraction of γ′, is a candidate material to be used over long periods at high temperatures and stresses, as conditions prevalent in advanced ...ultra-supercritical (AUSC) power plants. In this study, IN740H has been tested at temperatures above 700 °C to gain mechanistic insights into its high-temperature creep behavior. The material showed classic signatures of the presence of threshold stress, marked by observation of a high apparent creep stress exponent, n, (e.g., 10 at 750 °C) and a high apparent activation energy for creep, Qc (e.g., ∼545 kJ/mol), along with a rapid increase in n at lower stresses. Accounting for the threshold stress led to a decrease in the values of n and Qc to 4 and 280 kJ/mol, respectively. Furthermore, the transmission electron microscopy and atomic scale compositional analysis reveal the pinning of dislocations at γ-γ′ interface and segregation of Co, Cr and Mo atoms in the regions of γ-γ′ interface rich in dislocations. The above combination of n and Qc and the observation of dislocation pinning at the γ-γ′ interface indicate the dislocation climb over γ′ as the dominant creep mechanism in this γ′-lean Ni-based superalloy, with the detachment of dislocations from the γ-γ′ interface, augmented by the segregation of Co, Cr and Mo, as the mechanism responsible for the realization of the threshold stress. This work, thus, provides a new impetus to research on the long-term structural integrity of γ′-lean Ni-based superalloys exposed to extreme service conditions.
Effective use of photovoltaic (PV) modules requires reliable models for a number of applications, such as monitoring the performance of PV systems, estimating the produced power and plant design, ...etc. Development of accurate and simple models for different PV technologies remains a big challenge. In this paper, a comparative study of seven implicit and explicit models, published in the literature, is presented. The predicted current-voltage characteristics of the main commercial PV module technologies (multi-crystalline Silicon, Copper Indium Gallium Selenide, and Cadmium Telluride), have been compared both with the ones from the datasheet and with the ones obtained experimentally. Moreover, the investigated models have also been evaluated in terms of accuracy, required parameters, generalisation capability and complexity.
•Seven implicit and explicit models for PV cells and modules have been presented and compared.•The measured and simulated I-V curves have been compared for three PV modules technologies (m-Si, CIGS and CdTe).•Explicit models accurately describe the behaviour of PV modules for different technologies.
•The soiling effect can have a significant impact on the PV plant performance.•Bayesian neural network performs better than polynomial regression model.•Grid operators can benefit from the proposed ...technique.
This paper presents a comparison between two different techniques for the determination of the effect of soiling on large scale photovoltaic plants. Four Bayesian Neural Network (BNN) models have been developed in order to calculate the performance at Standard Test Conditions (STCs) of two plants installed in Southern Italy before and after a complete clean-up of their modules. The differences between the STC power before and after the clean-up represent the losses due to the soiling effect. The results obtained with the BNN models are compared with the ones calculated with a well known regression model. Although the soiling effect can have a significant impact on the PV system performance and specific models developed are applicable only to the specific location in which the testing was conducted, this study is of great importance because it suggests a procedure to be used in order to give the necessary confidence to operation and maintenance personnel in applying the right schedule of clean-ups by making the right compromise between washing cost and losses in energy production.
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•Co, Ni, Cu and Zn are successfully used in the preparation of activated carbons by microwave radiation.•The metal atoms have an impact on physicochemical and adsorptive ...properties.•SBET and pore volumes increase with atomic number and decrease with melting point.•Cu-MC, Co-MC and Ni-MC exhibit high hydrophobic properties compared to Zn-MC.•O-nitro phenol is the most adsorbed molecule from aqueous solution.
First-row transition metals (Co, Ni, Cu and Zn) were successfully used in the preparation of activated carbons from wood biomass via microwave-assisted irradiation. Physical-chemical properties of the produced materials (MWAC) were studied by nitrogen adsorption–desorption curves, SEM, FTIR, UV–vis DRS and synchronous fluorescence spectroscopy, CHN elemental analysis, TGA/DTG, pHzpc, hydrophobic properties, and total acidity and basicity groups. Results showed that the metals were bound successfully in different amounts with surface functional groups of the wood biomass through ion exchange and surface complexation interaction during the impregnation step. Zn2+ and Cu2+ formed the most complexes. MWAC impregnated with Zn2+ showed higher pore volumes and surface areas, followed by Cu2+, Co2+ and Ni2+, independently of the ratio used. As the metal : biomass ratio was increased from 0.5 to 2, the surface area of MWAC increased from 300 to 620m2g−1 for Co-MC, 260 to 381m2g−1 for Ni-MC, 449 to 765m2g−1 for Cu-MC and from 572 to 1780m2g−1 for Zn-MC. The samples showed high values of carbon contents and oxygen-containing groups. An adsorption experiment revealed that samples prepared using ZnCl2 showed the highest sorption capacities (qe) for the tested adsorbates, followed by CuCl2, CoCl2 and NiCl2. These results matched with the surface areas and pore volumes trends, which were found to follow atomic number and melting point trends–Ni(II)<Co(II)<Cu(II)<Zn(II), rather than the Irving-Williams Series. The sorption capacities (qe) of molecules followed this order: 2-nitro phenol>bisphenol A>hydroquinone>4-nitro phenol>2-naphtol>paracetamol>caffeine>resorcinol.
► FL-MPPT controller is implemented on FPGA. ► Results obtained with ModelSim show a satisfactory performance. ► Results will be useful for future development in PV.
This paper describes the hardware ...implementation of a two-inputs one-output digital Fuzzy Logic Controller (FLC) on a Xilinx reconfigurable Field-Programmable Gate Array (FPGA) using VHDL Hardware Description Language. The FLC is designed for seeking the maximum power point deliverable by a photovoltaic module using the measures of the photovoltaic current and voltage. The simulation results obtained with ModelSim Xilinx Edition-III show a satisfactory performance with a good agreement between the expected and the obtained values.
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