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

Simplified modelling of nonlinear electromethanogenesis stack for power-to-gas applications

Bioelectrochemical systems performing electromethanogenesis (EMG-BES) represent an emerging technology for Power-to-gas as well as wastewater treatment. Moreover, EMG-BES can be used as a high-capacity energy storage system to absorb surplus energy in the electrical grid. This paper presents a modelling approach, which is based on building an equivalent electric circuit of the EMG-BES, which can be used to emulate static and dynamic non-linear behaviour of EMG-BES for different input voltages, which is advantageous if compared to other existing models. This model is a suitable choice for future studies in the development of the electric converters for EMG-BES plants connected to the electrical grid. The proposed model consists of practical and commercial elements, including capacitors, resistors, voltage sources, and a diode. The modelling of non-linear behaviour is achieved by adding a diode to the model. Four simple tests were performed to determine the equivalent circuit parameters in a medium-scale EMG-BES prototype. This prototype was built by stacking 45 cells together and connecting them in parallel, and it was long-term operated and tested under different electric inputs to determine the model parameters. A comparative study was finally conducted as reported in this paper in order to validate the proposed model against experimental results and values collected with other models.The research leading to these results has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 712949 (TECNIOspring PLUS) and from the Agency for Business Competitiveness of the Government of Catalonia. Also, this work has been supported by the Spanish Ministry of Economy and Competitiveness under the projects RTI2018-100921-BC21. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the host institutions or funders. The authors wish to acknowledge the Leitat collaborators who took part in Power2Biomethane project, which was financially supported by the Spanish Ministry of Economy and Competitiveness (RTC-2016- 5024-3, 2016).Peer ReviewedPostprint (author's final draft

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

Experimental energy performance assessment of a bifacial photovoltaic system and effect of cool roof coating

In the quest for high albedo materials that boost the energy production of bifacial photovoltaic systems, a range of material already exists for reducing building roof surface temperatures, called cool roof materials. However, there is a noticeable absence of scientific literature addressing the combination of cool roofs and bifacial photovoltaic systems. This study investigates the photovoltaic performance of a bifacial photovoltaic system with cool roof coating on the underside and its impact on floor temperature. For this purpose, four ∼1kWp prototypes were installed on the terrace of the GAIA building of the UPC near Barcelona, Spain: (1) bifacial panels above a cool roof, (2) bifacial panels above normal floor, (3) bifacial panels above a normal floor with n-type solar cells encapsulated in TPO, and (4) monofacial panels. The results reveal 8.6 % higher PV yield for bifacial with cool roof compared to monofacial, and 4–4.5 % higher for bifacial (normal floor) compared to monofacial. Additionally, the cool roof coating contributes to reducing the floor temperatures, particularly in the unshaded (exposed) areas during summer (−3.8 °C). The presence of photovoltaic panels has also demonstrated a positive impact on floor temperatures during both winter and summer. Thus, the cool roof coating offers two benefits: increased photovoltaic yield and reduced building cooling requirements, both of which are associated with economic advantages. The cool roof coating can be integrated into existing or new bifacial roof systems.This work was supported by the SUDOKET SOE2/P1/E0677 project funded by FEDER of the EU under the Interreg-Sudoe program

Intelligent Monitoring of Photovoltaic Systems via Simplicial Empirical Models and Performance Loss Rate Evaluation under LabVIEW: A Case Study

The capacity of photovoltaic solar power installations has been boosted last years by reaching a new record with 175 GWdc of newly installed solar power in 2021. To guarantee reliable performances of photovoltaic (PV) plants and maintain target requirements, faults have to be reliably detected and diagnosed. A method for an effective and reliable fault diagnosis of PV plants based on the behavioral model and performance analysis under the LabVIEW environment is presented in this paper. Specifically, the first phase of this study consists of the behavioral modeling of the PV array and the inverter in order to estimate the electricity production and analyze the performance of the 9.54 kWp Grid Connected PV System (GCPVS). Here, the results obtained from the empirical models were validated and calibrated by experimental data. Furthermore, a user interface for modeling and analyzing the performance of a PV system under LabVIEW has been designed. The second phase of this work is dedicated to the design of a simple and efficient diagnostic tool in order to detect and recognize faults occurring in the PV systems. Essentially, the residuals obtained using the parametric models are analyzed via the performance loss rates (PLR) of four electrical indicators (i.e., DC voltage, DC current, DC power, and AC power). To evaluate the proposed method, numerous environmental anomalies and electrical faults affecting the GCPVS were taken into account. Results demonstrated the satisfactory prediction performance of the considered empirical models to predict the considered variables, including DC current, DC power, and AC power with an R2 of 0.99. Moreover, the obtained results show that the detection and recognition of faults were successfully achieved

Intelligent Monitoring of Photovoltaic Systems via Simplicial Empirical Models and Performance Loss Rate Evaluation under LabVIEW: A Case Study

The capacity of photovoltaic solar power installations has been boosted last years by reaching a new record with 175 GWdc of newly installed solar power in 2021. To guarantee reliable performances of photovoltaic (PV) plants and maintain target requirements, faults have to be reliably detected and diagnosed. A method for an effective and reliable fault diagnosis of PV plants based on the behavioral model and performance analysis under the LabVIEW environment is presented in this paper. Specifically, the first phase of this study consists of the behavioral modeling of the PV array and the inverter in order to estimate the electricity production and analyze the performance of the 9.54 kWp Grid Connected PV System (GCPVS). Here, the results obtained from the empirical models were validated and calibrated by experimental data. Furthermore, a user interface for modeling and analyzing the performance of a PV system under LabVIEW has been designed. The second phase of this work is dedicated to the design of a simple and efficient diagnostic tool in order to detect and recognize faults occurring in the PV systems. Essentially, the residuals obtained using the parametric models are analyzed via the performance loss rates (PLR) of four electrical indicators (i.e., DC voltage, DC current, DC power, and AC power). To evaluate the proposed method, numerous environmental anomalies and electrical faults affecting the GCPVS were taken into account. Results demonstrated the satisfactory prediction performance of the considered empirical models to predict the considered variables, including DC current, DC power, and AC power with an R2 of 0.99. Moreover, the obtained results show that the detection and recognition of faults were successfully achieved

Bioelectrochemical systems for energy storage: a scaled-up power-to-gas approach

The development and implementation of energy storage solutions is essential for the sustainability of renewable energy penetration in the electrical system. In this regard, power-to-gas technologies are useful for seasonal, high-capacity energy storage. Bioelectrochemical systems for electromethanogenesis (EMG-BES) represent an additional power-to-gas technology to the existing chemical and biological methanation. EMG-BES process can be retrofitted in traditional anaerobic digesters, with advantages in terms of biologic process stability and high-quality biogas production. Nowadays, there are no reported studies of scaled-up EMG-BES plants for energy storage. The present work describes the setup and operation of a medium-scale EMG-BES prototype for power-to-gas, storing energy in the form of biomethane. The prototype was built by stacking 45 EMG-BES cells, accounting for a total volume of 32¿L. It was continuously fed with 10¿L¿day-1 municipal wastewater, and it was long-term operated at different voltage and temperature ranges. A steady-state current density demand of 0.5¿A¿m-2 was achieved at 32¿°C while producing 4.4¿L¿CH4¿m-2¿d-1 and removing 70% of the initial organic matter present in wastewater. Microbial competition between electro-active bacteria and acetoclastic methanogens was observed. Energy storage efficiency was estimated around 42–47%, analyzing surplus CH4 production obtained when applying voltage to the stack. A first order electric model was calculated, based on the results of a series of electrical characterization tests. The model may be used in the future to design the converter for EMG-BES plant connection to the electrical grid. The obtained results show that energy storage based on EMG-BES technology is possible, as well as its future potential, mixing renewable power overproduction, biomethane generation and wastewater treatment under the circular economy umbrella.Peer ReviewedPostprint (author's final draft