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[IEEE 2020 International Conference on Emerging Trends in Smart Technologies (ICETST) - Karachi, Pakistan (2020.3.26-2020.3.27)] 2020 International Conference on Emerging Trends in Smart Technologies (ICETST) - Soft Computing Technique based Nonlinear Sliding Mode Control for Stand-Alone Photovoltaic System
摘要： Energy production capability of a photovoltaic (PV) system is extensively depends upon the ambient temperature (T) and solar irradiance (Ee). In order to adapt the ever increasing interest in energy, the PV array must be operated at the maximum power point (MPP). However, due to varying climatic conditions, there is a low energy ef?ciency problem. In this research article, a robust and ef?cient nonlinear sliding mode control (SMC) based maximum power point tracking (MPPT) technique is designed to extract maximum power from the PV array. This study uses arti?cial feed-forward neural network (AFNN) to generate the reference voltage for MPPT using non-inverting DC-DC Buck-Boost converter. Asymptotically convergence is ensures using Lyapunov stability criteria. The MATLAB/SIMULINK platform is used to design, simulate and test the performance of the proposed technique. To further validate the proposed control technique in terms of ef?ciency, tracking speed and robustness, results are compared with the non-linear backstepping (B) technique under continuous conditions of environment, faults and parametric uncertainties.
关键词： Buck-Boost converter,Neural Network,MPPT,Photovoltaic,SMC
更新于2025-09-23 15:21:01
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[IEEE 2019 7th International Renewable and Sustainable Energy Conference (IRSEC) - Agadir, Morocco (2019.11.27-2019.11.30)] 2019 7th International Renewable and Sustainable Energy Conference (IRSEC) - Elaboration and Characterization of (Ce,Sm) Doped Lanthanum Oxychloride for Photovoltaic Solar Cell
摘要： The spectral incoherence due to the emission of sunlight and the absorption of silicon solar cells cause large solar energy losses, leading to a low photovoltaic efficiency. So that low energy photons cannot be used efficiently, while the majority of high energy photons are dissipate. Doped rare earth ions (RE) are expected to solve this problem, thanks to their spectral modification characteristics and luminescence properties. In this work we present the synthesis of LaOCl co-doped by trivalent rare earth with the couple of Ce3+/Sm3+ employed in photovoltaic cell down conversion. LaOCl: Ce3+/Sm3+ phosphors were prepared by the solid-state method, and their structures properties were investigated by using X-ray diffraction, Infrared Spectroscopy and MEB analysis. The study shows that tetragonal LaOCl: Ce3+/Sm3+ can be synthesized by the solid-state reaction at 700 °C for 4 h.
关键词： Down conversion,Lanthanum oxychloride,Solid-state,Photovoltaic,Rare Earth
更新于2025-09-23 15:21:01
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Snow Loss Prediction for Photovoltaic Farms Using Computational Intelligence Techniques
摘要： With the recent widespread deployment of Photovoltaic (PV) panels in the northern snow-prone areas, performance analysis of these panels is getting much more importance. Partial or full reduction in energy yield due to snow accumulation on the surface of PV panels, which is referred to as snow loss, reduces their operational efficiency. Developing intelligent algorithms to accurately predict the future snow loss of PV farms is addressed in this article for the first time. The article proposes daily snow loss prediction models using machine learning algorithms solely based on meteorological data. The algorithms include regression trees, gradient boosted trees, random forest, feed-forward and recurrent artificial neural networks, and support vector machines. The prediction models are built based on the snow loss of a PV farm located in Ontario, Canada which is calculated using a 3-stage model and hourly data records over a 4-year period. The stages of the aforementioned model consist of: stage I: yield determination, stage II: power loss calculation, and stage III: snow loss extraction. The functionality of the proposed prediction models is validated over the historical data and the optimal hyperparameters are selected for each model to achieve the best results. Among all the models, gradient boosted trees obtained the minimum prediction error and thus the best performance. The results achieved prove the effectiveness of the proposed models for the prediction of daily snow loss of PV farms.
关键词： snow loss,Intelligent prediction,snowfall,photovoltaic (PV) farm,machine learning
更新于2025-09-23 15:21:01
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Adaptive Voltage Regulation for Solar Power Inverters on Distribution Systems
摘要： By means of time-series power flow, this paper compares the performance of several reactive power control modes allowed in the new IEEE 1547. It is shown that voltage-reactive power mode (sometimes called volt-var) should be considered for a universal default setting, with no deadband, the highest allowed gain of 22 for Category B inverters, and a gain of 12.5 for Category A inverters. The reference voltage should adapt to the system voltage, with a 300s time constant.
关键词： Photovoltaic systems,smart grids,inverters,automatic voltage control,distributed power generation
更新于2025-09-23 15:21:01
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Design Optimization of Photovoltaic Cell Stacking in a Triple-Well CMOS Process
摘要： Various self-powered devices employ energy-harvesting technology to capture and store an ambient energy. The photovoltaic (PV) cell is one of the most preferred approaches due to its potential for on-chip integration. Although serial connection of multiple PV cells is commonly required to obtain a sufficiently high voltage for circuit operation, a voltage boosting with serially stacked PV cells is limited in a standard bulk CMOS process because all the PV cells are intrinsically connected to the common substrate. It is possible to increase the output voltage by stacking multiple PV cells with a large area ratio between stages. However, nonoptimal design results in a poor conversion efficiency or a limited open-circuit voltage, making it unsuitable for practical applications. This article proposes a stacking structure and its optimal design method for PV cell stacking in a triple-well CMOS process. The proposed approach utilizes an additional current-sourcing photodiode and an optical filter, which allow high voltage generation without a significant efficiency degradation. The test chip with four-stage stacked PV cells was fabricated using a 0.25-μm standard triple-well CMOS process. The experimental results demonstrate an output voltage of 1.6 V and an electrical power of 263 nW/mm2 under an incident illumination with an intensity of 96 μW/mm2, achieving a responsivity of 1.91 mA/W and a conversion efficiency of 0.27%.
关键词： on-chip solar cell,photovoltaic (PV) cell stacking,Energy harvesting,voltage boosting
更新于2025-09-23 15:21:01
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Analysis of the Power Quality of a Grid-Connected Photovoltaic System
摘要： The use of Grid-Connected Photovoltaic Systems is increasingly on the rise in Brazil. Faced with this growth, it is necessary to evaluate the impacts of this source in the electric energy systems. Based on this scenario, the present work aims to analyze and quantify the impact of a grid-connected photovoltaic system connection, evaluating the Electric Power Quality indicators based on the levels specified in Module 8 of the Electric Energy Distribution Procedures in the National Electric System. To perform the data collection, an energy analyzer device was installed at the output of the grid-connected system inverter located in the Energy Laboratory of the Federal Rural Semi-Arid University, located in the city of Mossoró-RN. Using the collected data, it was possible to analyze parameters of voltage and current distortion, power factor, active, reactive and apparent power, voltage, frequency variations, and voltage unbalance. It was observed that the criteria analyzed were within the appropriate standards although there were also verified cases that there was an elevation in tension levels. Finally, it is concluded that the impacts caused are relevant within the electric system, and grid connected system performance was satisfactory, although there are still possibilities for improvements.
关键词： Power Quality,Photovoltaic System,Grid-Connected,Renewable Energy,Distributed Generation
更新于2025-09-23 15:21:01
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An Ensemble Learner-Based Bagging Model Using Past Output Data for Photovoltaic Forecasting
摘要： As the world is aware, the trend of generating energy sources has been changing from conventional fossil fuels to sustainable energy. In order to reduce greenhouse gas emissions, the ratio of renewable energy sources should be increased, and solar and wind power, typically, are driving this energy change. However, renewable energy sources highly depend on weather conditions and have intermittent generation characteristics, thus embedding uncertainty and variability. As a result, it can cause variability and uncertainty in the power system, and accurate prediction of renewable energy output is essential to address this. To solve this issue, much research has studied prediction models, and machine learning is one of the typical methods. In this paper, we used a bagging model to predict solar energy output. Bagging generally uses a decision tree as a base learner. However, to improve forecasting accuracy, we proposed a bagging model using an ensemble model as a base learner and adding past output data as new features. We set base learners as ensemble models, such as random forest, XGBoost, and LightGBMs. Also, we used past output data as new features. Results showed that the ensemble learner-based bagging model using past data features performed more accurately than the bagging model using a single model learner with default features.
关键词： ensemble,decision tree,bagging,Light GBM,lagged data,machine learning,random forest,XGBoost,photovoltaic power forecasting
更新于2025-09-23 15:21:01
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Single-Junction Organic Photovoltaic Cells with Approaching 18% Efficiency
摘要： Optimizing the molecular structures of organic photovoltaic (OPV) materials is one of the most effective methods to boost power conversion efficiencies (PCEs). For an excellent molecular system with a certain conjugated skeleton, fine tuning the alky chains is of considerable significance to fully explore its photovoltaic potential. In this work, the optimization of alkyl chains is performed on a chlorinated nonfullerene acceptor (NFA) named BTP-4Cl-BO (a Y6 derivative) and very impressive photovoltaic parameters in OPV cells are obtained. To get more ordered intermolecular packing, the n-undecyl is shortened at the edge of BTP-eC11 to n-nonyl and n-heptyl. As a result, the NFAs of BTP-eC9 and BTP-eC7 are synthesized. The BTP-eC7 shows relatively poor solubility and thus limits its application in device fabrication. Fortunately, the BTP-eC9 possesses good solubility and, at the same time, enhanced electron transport property than BTP-eC11. Significantly, due to the simultaneously enhanced short-circuit current density and fill factor, the BTP-eC9-based single-junction OPV cells record a maximum PCE of 17.8% and get a certified value of 17.3%. These results demonstrate that minimizing the alkyl chains to get suitable solubility and enhanced intermolecular packing has a great potential in further improving its photovoltaic performance.
关键词： nonfullerene acceptors,organic photovoltaic cells,molecular modification,power conversion efficiency
更新于2025-09-23 15:21:01
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Estimating Solar Insolation and Power Generation of Photovoltaic Systems Using Previous Day Weather Data
摘要： Day-ahead predictions of solar insolation are useful for forecasting the energy production of photovoltaic (PV) systems attached to buildings, and accurate forecasts are essential for operational efficiency and trading markets. In this study, a multilayer feed-forward neural network-based model that predicts the next day’s solar insolation by taking into consideration the weather conditions of the present day was proposed. The proposed insolation model was employed to estimate the energy production of a real PV system located in South Korea. Validation research was performed by comparing the model’s estimated energy production with the measured energy production data collected during the PV system operation. The accuracy indices for the optimal model, which included the root mean squared error, mean bias error, and mean absolute error, were 1.43 kWh/m2/day, ? 0.09 kWh/m2/day, and 1.15 kWh/m2/day, respectively. These values indicate that the proposed model is capable of producing reasonable insolation predictions; however, additional work is needed to achieve accurate estimates for energy trading.
关键词： neural network,energy production,photovoltaic systems,solar insolation,forecasting
更新于2025-09-23 15:21:01
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Comparative Analysis of Intelligent Controller Based MPPT for Photovoltaic System with Super Lift Boost Converter
摘要： In recent years, the electrical energy demand increases gradually and the power generation does not meet the demand due to lack of fossil fuel and environmental issues. The only solution is to use renewable energy sources for generating electricity and meet the consumers demand. In this paper, photovoltaic power system analyses their performance under various weather conditions. The objective of this paper is comparing the different intelligent controllers such as Fuzzy, ANFIS and Hybrid Fuzzy & Firefly Algorithm (HFFA) for Maximum Power Point Tracking (MPPT) of 100 Watts PV system using a Super Lift Boost Converter (SLBC). The proposed intelligent controller is designed and simulated in MATLAB environment under various weather conditions. The simulation results have been analyzed and the performance of the proposed model evaluated with changing irradiation conditions. Finally, the performance of Hybrid Fuzzy and firefly based MPPT has been suggested as the optimum controller for the photovoltaic system.
关键词： photovoltaic,ANFIS,Fuzzy Logic,MPPT,MATLAB,HFFA,super-lift boost converter
更新于2025-09-23 15:21:01