Compare between the performance of different technologies of PV Modules using Artificial intelligence techniques

Main Article Content

Hichem Hafdaoui
Nasreddine Belhaouas
Houria Assem
Farid Hadjrioua
Nadira Madjoudj

Abstract

In this paper, we applied the artificial intelligence technique (SVM Classifier) to compare the performance of two different technologies of PV modules (class to class and backsheet to glass) after five (05) months of operation in Algeria under the same weather conditions (moderate and humid climate) . We have a database for the outdoor monitoring of these two PV modules, consisting of data (Isc, Voc, Pmax, Imp, Vmp, Tm, Tamb, G, WD, WS, Date, Time) which are variables data, where the SVM creates the groups or class according to the conditions that we entered, after which it produces heatmaps that help us in reading the results and making the decision easily, unlike the classic methods which are very difficult. This method is applicable for comparison between several solar panels or several photovoltaic PV plants. It is enough just to give the database.

Article Details

How to Cite
[1]
H. Hafdaoui, N. . Belhaouas, H. . Assem, F. . Hadjrioua, and N. . Madjoudj, “Compare between the performance of different technologies of PV Modules using Artificial intelligence techniques”, J. Ren. Energies, vol. 1, no. 1, pp. 99 -, May 2024.
Section
special

References

AMEL, Mechnane, HICHEM, Hafdaoui, et DJAMEL, Benatia. Study of Leaky Acoustic Micro-Waves in Piezoelectric Material (Lithium Niobate Cut YX) Using Probabilistic Neural Network (PNN) Classification. International Journal of Microwave & Optical Technology, 2022, vol. 17, no 2.

BALACHANDRAN, Gurukarthik Babu, DEVISRIDHIVYADHARSHINI, M., RAMACHANDRAN, Muthu Eshwaran, et al. Comparative investigation of imaging techniques, pre-processing and visual fault diagnosis using artificial intelligence models for solar photovoltaic system–A comprehensive review. Measurement, 2024, vol. 232, p. 114683.

CHOI, Jongwoo, LEE, Il-Woo, et CHA, Suk-Won. Analysis of data errors in the solar photovoltaic monitoring system database: An overview of nationwide power plants in Korea. Renewable and Sustainable Energy Reviews, 2022, vol. 156, p. 112007.

DEMIR, Vahdettin et CITAKOGLU, Hatice. Forecasting of solar radiation using different machine learning approaches. Neural Computing and Applications, 2023, vol. 35, no 1, p. 887-906.

HAFDAOUI, Hichem, , KOUADRI BOUDJELTHIA, El Amin, BOUCHAKOUR, Salim, BELHAOUAS, Nasreddine, et al. Using Machine Learning for Analysis a Database Outdoor Monitoring of Photovoltaic System. International Journal of Integrated Engineering, 2022, vol. 14, no 6, p. 275-280.

HAFDAOUI, Hichem, BOUCHAKOUR, Salim, BELHAOUAS, Nasreddine, et al. Use an artificial intelligence method (Machine Learning) for analysis of the performance of photovoltaic systems. Journal of Renewable Energies, 2022, vol. 25, no 2, p. 199–210-199–210.

JAYAKUMAR, V., SIVASELVAN, S., NANTHAKUMAR, P., et al. Analysis of Multilevel Inverter with Different Control Techniques. In: 2024 International Conference on Integrated Circuits and Communication Systems (ICICACS). IEEE, 2024. p. 1-7.

LEBRUN, Gilles. Sélection de modèles pour la classification supervisée avec des SVM (Séparateurs à Vaste Marge). Application en traitement et analyse d'images. Thèse de doctorat. Université de Caen Basse-Normandie, 2006.

MADETI, Siva Ramakrishna et SINGH, S. N. Comparative analysis of solar photovoltaic monitoring systems. In: AIP Conference Proceedings. AIP Publishing, 2017.

MUNSHI, Anuradha et MOHARIL, R. M. Binning Based Data Driven Machine Learning Models for Solar Radiation Forecasting in India. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 2024, p. 1-12.

Nahla Farid, Bassant Elbagoury, Mohamed Roushdy, et al., A comparative analysis for support vector machines for stroke patients, Rec. Adv. Inf. Sci. (2013) 71–76.

PUTRA, Jimmy Trio, SETYONEGORO, M. Isnaeni Bambang, et al. Modeling of high uncertainty photovoltaic generation in quasi dynamic power flow on distribution systems: A case study in Java Island, Indonesia. Results in Engineering, 2024, vol. 21, p. 101747.

Ruben Ruiz-Gonzalez, Jaime Gomez-Gil, Francisco Javier Gomez-Gil, et al., An SVM-based classifier for estimating the state of various rotating components in agro-industrial machinery with a vibration signal acquired from a single point on the machine chassis, Sensors 14 (11) (2014) 20713–20735.

Tara Doyle, Ryan Desharnais, Tristan Erion-Lorico, 2019 PV Module Reliability Scorecard, PVEL, 2019.

TEHRANI, Alireza Attarhay, VEISI, Omid, FAKHR, Bahereh Vojdani, et al. Predicting solar radiation in the urban area: A data-driven analysis for sustainable city planning using artificial neural networking. Sustainable Cities and Society, 2024, vol. 100, p. 105042.

WANG, Jing, CHEN, Jiahong, ZHANG, Kuangen, et al. Training feedforward neural nets in Hopfield-energy-based configuration: A two-step approach. Pattern Recognition, 2024, vol. 145, p. 109954.