Using Machine Learning for Analysis a Database Outdoor Monitoring of Photovoltaic System

: In this paper we propose a new method for analyzing the performance of photovoltaic system using classification, the monitoring of photovoltaic module (150 W) was controlled and analyzed, the system was deployed in Algiers over a long period (80 days), one of the most important difficulties faced by researchers is collecting and analyzing the results of monitoring for a long period, so in this paper we proposed a method for analyzing results by classification using SVM Classifier. More specifically, we regrouping a data variable to multiclass for according and analyzing using SVM. We have presented thoroughly all the calculation steps. Based on the application of artificial intelligence (classification), recorded data, the power output for a given solar panels technology, types and small or large stations under any seasons can be analyzed and treated easily. The several measurements in our laboratory was investigated based on data acquisition (Keysight 34972A).The system collects the measurements from the various sensors. The measurement system was taken the data between 05h00 to 21h00 with irradiation of 50 W/m2 which is starting point, however in 0 to 50 W/m2 the system cannot detect any photovoltaic effect. Results predict that the performance ratio (PR) from a Poly-crystalline panel was around 85.28 % for a different season’s exposure and 727 point analyzes at irradiation of 850-950 W/m2 in same time 14h00-15h00 . The temperature of solar panel are also calculated and compared in different irradiation and time

Using Machine Learning for Analysis a Database Outdoor Monitoring of Photovoltaic System

: In this paper we propose a new method for analyzing the performance of photovoltaic system using classification, the monitoring of photovoltaic module (150 W) was controlled and analyzed, the system was deployed in Algiers over a long period (80 days), one of the most important difficulties faced by researchers is collecting and analyzing the results of monitoring for a long period, so in this paper we proposed a method for analyzing results by classification using SVM Classifier. More specifically, we regrouping a data variable to multiclass for according and analyzing using SVM. We have presented thoroughly all the calculation steps. Based on the application of artificial intelligence (classification), recorded data, the power output for a given solar panels technology, types and small or large stations under any seasons can be analyzed and treated easily. The several measurements in our laboratory was investigated based on data acquisition (Keysight 34972A).The system collects the measurements from the various sensors. The measurement system was taken the data between 05h00 to 21h00 with irradiation of 50 W/m2 which is starting point, however in 0 to 50 W/m2 the system cannot detect any photovoltaic effect. Results predict that the performance ratio (PR) from a Poly-crystalline panel was around 85.28 % for a different season’s exposure and 727 point analyzes at irradiation of 850-950 W/m2 in same time 14h00-15h00 . The temperature of solar panel are also calculated and compared in different irradiation and time

Use an artificial intelligence method (Machine Learning) for analysis of the performance of photovoltaic systems

The performance of a photovoltaic system depends on several parameters such as temperature, clouds, and the season, which makes the study of PV performance from monitoring databases very complex given the size of the information and the complexity of the phenomena involved. This article applies an artificial intelligence (AI) method based on machine learning (ML). For more efficient analysis, the Support Vector Machine (SVM) is used to simplify and optimize the processing of these data for the study of the performance of PV systems. More precisely, we group a multi-class data variable according to the needs of the analysis using SVMs. In this article, we present all the stages of data processing based on the application of artificial intelligence (AI). We present as an example the results obtained in the study of the performance of a 150W monocrystalline photovoltaic (PV) module after one year of monitoring

Modelling and simulation of bifacial pv production using monofacial electrical models

In this paper, we investigate the use of monofacial PV models to simulate the production of bifacial PV systems over different albedos. Analytical and empirical models were evaluated using measured data obtained from three identical bifacial PV arrays: (1) with the backside covered by white plastic, (2) with normal albedo, and (3) with high albedo. The front-and rear-side irradiances were measured in order to integrate bifaciality of the modules into the models. The models showed good performance for non-real-time monitoring, especially under clear skies, and the analytical model was more accurate than the empirical model. The heatmap visualization technique was applied to six months of data in order to investigate the site conditions on the rear side of the modules as well as the accuracy of the models. The heatmap results of the rear- and front-sides irradiances showed that the installation conditions, such as the azimuth angles of the sun and the surrounding obstacles, had a strong impact on the energy received from the back of the modules. The heatmap results of the models validated the performance of the analytical model. The average daily errors for the analytical model were less than 1% and 3% for normal and high albedos, respectively.This work was supported by the SUDOKET SOE2/P1/E0677 project funded by FEDER of the EU under the Interreg-Sudoe program. The research leading to these results received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 712949 (TECNIOspring PLUS) and from the Agency for Business Competitiveness of the Government of Catalonia.Peer ReviewedPostprint (published version

Extraction of silicon metal impurities to be used for photovoltaic by plasma immersion ion implantation (PII)

Malgré son grand potentiel, l’énergie photovoltaïque n’arrive pas encore à trouver une grande place dans le paysage énergétique mondial. Elle se heurte à deux problèmes de taille : le coût et le rendement. Les cellules solaires à base du silicium multicristallin (mc-Si) perdent beaucoup de leur rendement à cause de la présence des impuretés métalliques. Plusieurs recherches ont montré que les cavités induites par implantation ionique sont efficaces dans le piégeage des impuretés. Mais les techniques utilisées dans l’implantation n’ont pas permis à ce procédé de se développer dans l’industrie à cause de leur coût élevé. Le plasma immersion ion implantation (PIII) est une technique bas coût qui permet d’implanter de grandes surfaces. Elle est utilisée dans le traitement de surface à l’échelle industrielle, mais à ce jour aucune étude n’a montré son utilisation dans le piégeage des impuretés dans le silicium. Dans cette thèse nous avons créé des cavités dans le mc-Si par implantation d’hydrogène par PIII. Plusieurs techniques de caractérisation ont été utilisées afin d’étudier le mécanisme de formation de ces cavités. La MET, la photoluminescence et les positons ont été utilisées pour avoir un maximum d’informations sur la nature et l’évolution des défauts créés par implantation d’hydrogène. Nous avons également étudié la différence entre les cavités formées par PIII et celles formées par implantation classique. Les cavités formées ont été utilisées, par la suite, pour le piégeage des impuretés métalliques présentes dans le mc-Si (Cu, Fe, Cr et Ni). Les résultats obtenus par SIMS ont monté l’efficacité de notre procédé dans le piégeage des impuretés métalliques.Extraction of silicon metal impurities to be used for photovoltaic by plasma immersion ion implantation (PII

Dust effect on optical transmittance of photovoltaic module glazing in a desert region

International audienc

Considerable improvement of long-persistent luminescence in germanium and tin substituted ZnGa2O4

International audienc

Considerable Improvement of Long-Persistent Luminescence in Germanium and Tin Substituted ZnGa₂O₄

The red long-lasting luminescence properties of the ZnGa₂O₄:Cr³⁺ spinel material are shown to be much improved when germanium or tin is substituted to the nominal composition. The resulting Zn_1+<i>x</i>Ga_{2–2<i>x</i>}(Ge/Sn)_<i>x</i>O₄ (0 ≤ <i>x</i> ≤ 0.5) spinel solid solutions synthesized here by a classic solid state method have been structurally characterized by X-ray and neutron powder diffraction refinements coupled to ⁷¹Ga solid state NMR studies. In contrast to ZnGa₂O₄:Cr³⁺ for which long lasting luminescence properties have been reported to arise from tetrahedral positively charged defects resulting from the spinel inversion, our results show that a different mechanism occurs complementary for Zn_1+<i>x</i>Ga_{2–2<i>x</i>}(Ge/Sn)_<i>x</i>O₄. Here, the great enhancement of the brightness and decay time of the long lasting luminescence properties is directly driven by the substitution mechanism which creates distorted octahedral sites surrounded by octahedral Ge and Sn positive substitutional defects which likely act as new efficient traps

Considerable Improvement of Long-Persistent Luminescence in Germanium and Tin Substituted ZnGa₂O₄

The red long-lasting luminescence properties of the ZnGa₂O₄:Cr³⁺ spinel material are shown to be much improved when germanium or tin is substituted to the nominal composition. The resulting Zn_1+<i>x</i>Ga_{2–2<i>x</i>}(Ge/Sn)_<i>x</i>O₄ (0 ≤ <i>x</i> ≤ 0.5) spinel solid solutions synthesized here by a classic solid state method have been structurally characterized by X-ray and neutron powder diffraction refinements coupled to ⁷¹Ga solid state NMR studies. In contrast to ZnGa₂O₄:Cr³⁺ for which long lasting luminescence properties have been reported to arise from tetrahedral positively charged defects resulting from the spinel inversion, our results show that a different mechanism occurs complementary for Zn_1+<i>x</i>Ga_{2–2<i>x</i>}(Ge/Sn)_<i>x</i>O₄. Here, the great enhancement of the brightness and decay time of the long lasting luminescence properties is directly driven by the substitution mechanism which creates distorted octahedral sites surrounded by octahedral Ge and Sn positive substitutional defects which likely act as new efficient traps

Considerable Improvement of Long-Persistent Luminescence in Germanium and Tin Substituted ZnGa₂O₄

The red long-lasting luminescence properties of the ZnGa₂O₄:Cr³⁺ spinel material are shown to be much improved when germanium or tin is substituted to the nominal composition. The resulting Zn_1+<i>x</i>Ga_{2–2<i>x</i>}(Ge/Sn)_<i>x</i>O₄ (0 ≤ <i>x</i> ≤ 0.5) spinel solid solutions synthesized here by a classic solid state method have been structurally characterized by X-ray and neutron powder diffraction refinements coupled to ⁷¹Ga solid state NMR studies. In contrast to ZnGa₂O₄:Cr³⁺ for which long lasting luminescence properties have been reported to arise from tetrahedral positively charged defects resulting from the spinel inversion, our results show that a different mechanism occurs complementary for Zn_1+<i>x</i>Ga_{2–2<i>x</i>}(Ge/Sn)_<i>x</i>O₄. Here, the great enhancement of the brightness and decay time of the long lasting luminescence properties is directly driven by the substitution mechanism which creates distorted octahedral sites surrounded by octahedral Ge and Sn positive substitutional defects which likely act as new efficient traps