Case Study on Photovoltaic System: Impact of Solarimetric Stations on Simulations and Anomaly Detection

João Lucas de S. Silva; João Antonio F. G. da Silva; Eslam Mahmoudi; João Frederico S. de Paula; Tárcio André dos S. Barros

doi:10.18618/REP.e202506

Authors

João Lucas de S. Silva Universidade Estadual de Campinas (UNICAMP) https://orcid.org/0000-0003-3206-2241
João Antonio F. G. da Silva Universidade Estadual de Campinas (UNICAMP) https://orcid.org/0000-0002-7488-3614
Eslam Mahmoudi Universidade Estadual de Campinas (UNICAMP) https://orcid.org/0000-0002-7531-371X
João Frederico S. de Paula Universidade Estadual de Campinas (UNICAMP) https://orcid.org/0009-0004-0150-5366
Tárcio André dos S. Barros Universidade Estadual de Campinas (UNICAMP)

DOI:

https://doi.org/10.18618/REP.e202506

Keywords:

Photovoltaic Plant, Solarimetric Stations, POA irradiance, Anomaly Detection

Abstract

Local solarimetric stations (LSS) are essential for collecting data to evaluate photovoltaic (PV) plant performance and improve simulation accuracy. When unavailable, commercial solar databases (CSD), typically derived from satellite-based typical years, are used. This study compared the impact of LSS and CSD data on PV simulations and explored the use of LSS data for anomaly detection. Two LSS were analyzed: one at a small-scale PV system (minigeneration in Brazil) and another at a large-scale PV plant. The minigeneration station measured irradiance components to calculate Plane of Array (POA) irradiance, while the large-scale station directly measured POA. For the minigeneration system, simulations using LSS data showed a lower discrepancy (0.21%) compared to CSD (3.13%). For the large-scale plant, a -5.99% discrepancy using LSS revealed anomalies in energy generation. MAE and RMSE improved significantly with LSS for the large-scale system, with MAE decreasing from 660.25 MWh (CSD) to 348.08 MWh. Additionally, an unsupervised anomaly detection flagged 2.88% and 4.47% of data for two inverters, showcasing LSS potential for predictive models. These findings suggest that while LSS data are valuable for PV plant performance analysis, their effectiveness may depend on spectral range, averaging intervals, and irradiance transposition in simulations.

Downloads

Download data is not yet available.

Author Biographies

João Lucas de S. Silva, Universidade Estadual de Campinas (UNICAMP)

Doctor of Electrical Engineering from Unicamp (2024), Master of Electrical Engineering from Unicamp, and Bachelor of Electrical Engineering from IFBA (Paulo Afonso). Founder of ProfJL, an Instagram profile dedicated to disseminating knowledge about PV Systems. His research interests include evaluating the performance of PV plants, developing PV converters, and studying ML techniques for PV applications.

João Antonio F. G. da Silva, Universidade Estadual de Campinas (UNICAMP)

Electrical Engineering student at Unicamp. Maintenance electrician trained at SENAI, focusing on building installations and industrial methods. His research interests include PV systems, with special attention to inverters and safety methods for PV plants.

Eslam Mahmoudi, Universidade Estadual de Campinas (UNICAMP)

Doctor from Unicamp (2022), Master's in Electrical Engineering from the State University/free educa - Shahid Bahonar University of Kerman. Has experience in Electrical Engineering, with an emphasis on Measurement, Control, and Protection of Electrical Power Systems.

João Frederico S. de Paula, Universidade Estadual de Campinas (UNICAMP)

Holds a Bachelor of Science and Technology and a degree in Electrical Engineering from UFERSA, and Master of Electrical Engineering from Unicamp. His research interests include modeling mono/bifacial PV systems, analyzing PV system performance metrics, and assessing performance loss rates and degradation in PV systems.

Tárcio André dos S. Barros, Universidade Estadual de Campinas (UNICAMP)

Obtained the titles of Master and Doctor and Livre Docência in Electrical Engineering from the Unicamp. Holds a degree in Electrical Engineering from the Univasf, where he was an awarded student. Currently an professor at the FEEC-Unicamp. Conducts research in renewable energy generation, wind and solar energy, industrial electronics, electronic control systems, modeling of electromechanical devices, and electronic instrumentation and industrial

References

P. A. Baste, S. R. Jadkar, A. M. Pathak, “Weather Station for Solar PV Power Plant Using Arduino Mega”, in 2021 International Conference on Computer Communication and Informatics (ICCCI), pp. 1–6, 2021.

B. K. Fontes Rodrigues, M. Gomes, A. M. Oliveira Santanna, D. Barbosa, L. Martinez, “Modelling and forecasting for solar irradiance from solarimetric station”, IEEE Latin America Transactions, vol. 20, no. 2, pp. 250–258, 2022.

N. Riedel-Lyngskær, M. Ribaconka, M. Po, S. Thorsteinsson, A. Thorseth, C. Dam-Hansen, M. L. Jakobsen, “Spectral Albedo in Bifacial Photovoltaic Modeling: What can be learned from Onsite Measurements?”, in 2021 IEEE 48th Photovoltaic Specialists Conference (PVSC), pp. 0942–0949, 2021.

M.-H. Pham, V. M. Phap, N. N. Trung, T. T. Son, D. T. Kien, V. T. Anh Tho, “A Study on the Impact of Various Meteorological Data on the Design Performance of Rooftop Solar Power Projects in Vietnam: A Case Study of Electric Power University”, Energies, vol. 15, no. 19, 2022.

M. Ejgar, B. Momin, “Solar plant monitoring system: A review”, in Proceedings of the International Conference on Computing Methodologies and Communication, ICCMC 2017, vol. 2018-January, pp. 1142–1144, 2018.

M. Aghaei, N. M. Kumar, A. Eskandari, H. Ahmed, A. K. V. de Oliveira, S. S. Chopra, “Chapter 5 - Solar PV systems design and monitoring”, in S. Gorjian, A. Shukla, eds., Photovoltaic Solar Energy Conversion, pp. 117–145, Academic Press, 2020.

M. Ibrahim, A. Alsheikh, F. M. Awaysheh, M. D. Alshehri, “Machine Learning Schemes for Anomaly Detection in Solar Power Plants”, Energies, vol. 15, no. 3, pp. 1–17, 2022.

I. A. Zulfauzi, N. Y. Dahlan, H. Sintuya, W. Setthapun, “Anomaly detection using K-Means and long-short term memory for predictive maintenance of large-scale solar (LSS) photovoltaic plant”, Energy Reports, vol. 9, pp. 154–158, 2023.

S. Voutsinas, D. Karolidis, I. Voyiatzis, M. Samarakou, “Development of a machine-learning-based method for early fault detection in photovoltaic systems”, Journal of Engineering and Applied Science, vol. 70, no. 1, pp. 0–17, 2023.

J. L. de Souza Silva, E. Mahmoudi, R. R. M. Carvalho, T. A. dos Santos Barros, “Classification of anomalies in photovoltaic systems using supervised machine learning techniques and real data”, Energy Reports, vol. 11, pp. 4642–4656, 2024.

J. L. De Souza Silva, J. A. F. G. Da Silva, E. Mahmoudi, J. F. S. De Paula, T. A. Dos Santos Barros, M. G. Villalva, “Evaluating the Significance of Solarimetric Data for Photovoltaic System Simulation in a Real-World Case”, in 2023 IEEE 8th Southern Power Electronics Conference and 17th Brazilian Power Electronics Conference (SPEC/COBEP), pp. 1–6, 2023.

Z. S¸en, Solar Energy Fundamentals and Modeling Techniques: Atmosphere, Environment, Climate Change and Renewable Energy, Springer, London, UK, 2008.

M. K. da Silva, Estudo de modelos matemáticos para análise da ´ radiação solar e desenvolvimento de ferramenta para modelagem e ˜ simulação de sistemas fotovoltaicos ˜ , Master’s thesis, Fac. de Eng. Elétrica e de Computação, Universidade Estadual de Campinas, Campinas, SP, Brazil, 2019, (in Portuguese).

M. Lave, W. Hayes, A. Pohl, C. W. Hansen, “Evaluation of Global Horizontal Irradiance to Plane-of-Array Irradiance Models at Locations Across the United States”, IEEE Journal of Photovoltaics, vol. 5, no. 2, pp. 597–606, March 2015.

R. Perez, P. Ineichen, R. Seals, J. Michalsky, R. Stewart, “Modeling daylight availability and irradiance components from direct and global irradiance”, Solar Energy, vol. 44, no. 5, pp. 271–289, 1990.

I. E. Commission, IEC 61724-1: Photovoltaic system performance monitoring - Guidelines for measurement, data exchange and analysis - Part 1: Grid-connected systems, Geneva, Switzerland, 2017, URL: https://www.iec.ch/standards/62873.

I. O. for Standardization, ISO 9060:2018 - Solar energy – Specification and classification of instruments for measuring hemispherical solar and direct solar radiation, Geneva, Switzerland, 2018, URL: https: //www.iso.org/standard/75159.html.

P. Gilman, SAM Photovoltaic Model Technical Reference, National Renewable Energy Laboratory, May 2015, URL: https://www.nrel.gov/ docs/fy15osti/64102.pdf.

B. Liu, R. Jordan, “A Rational Procedure for Predicting The Long-term Average Performance of Flat-plate Solar-energy Collectors”, Solar Energy, vol. 7, no. 3, pp. 53–74, 1963.

J. L. de Souza Silva, K. B. de Melo, K. V. dos Santos, E. Y. Sako, M. K. da Silva, H. S. Moreira, G. B. Archilli, J. G. I. Cypriano, R. E. Campos, L. C. P. da Silva, M. G. Villalva, “Case study of photovoltaic power plants in a model of sustainable university in Brazil”, Renewable Energy, vol. 196, pp. 247–260, 2022.

Meteonorm, “Meteonorm”, Accessed: 2024-07-23, 2024, URL: https://meteonorm.com/en/.

European Commission, “Photovoltaic Geographical Information System (PVGIS)”, Accessed: 2024-07-23, 2024, URL:

https://joint-research-centre.ec.europa.eu/photovoltaic-geographicalinformation-system-pvgisen.

C.-J. Huang, P.-H. Kuo, “Multiple-Input Deep Convolutional Neural Network Model for Short-Term Photovoltaic Power Forecasting”, IEEE Access, vol. 7, pp. 74822–74834, 2019.

E. Lorenzo, Energy Collected and Delivered by PV Modules, Handbook of Photovoltaic Science and Engineering, John Wiley & Sons, 2003.

N. Lindsay, Q. Libois, J. Badosa, A. Migan-Dubois, V. Bourdin, “Errors in PV power modelling due to the lack of spectral and angular details of solar irradiance inputs”, Solar Energy, vol. 197, pp. 266–278, 2020.

W. Hardle, L. Simar, Applied Multivariate Statistical Analysis, 2nd ed., Springer, 2007.

W. J. Wu, Y. Xu, “Correlation analysis of visual verbs’ subcategorization based on Pearson’s correlation coefficient”, 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010, vol. 4, no. July, pp. 2042–2046, 2010.

S. Voutsinas, D. Karolidis, I. Voyiatzis, M. Samarakou, “Development of a multi-output feed-forward neural network for fault detection in Photovoltaic Systems”, Energy Reports, vol. 8, pp. 33– 42, 2022.

M. M. Cavalcante, J. L. De Souza Silva, S. B. Martins, I. F. Silva Nunes, A. C. Ribeiro, T. A. Dos Santos Barros, “Comparison and Application of Data Science Techniques for Anomaly Detection in Photovoltaic Systems”, in 2023 IEEE 8th Southern Power Electronics Conference and 17th Brazilian Power Electronics Conference (SPEC/COBEP), pp. 1–5, 2023.

S. R. Madeti, S. Singh, “Modeling of PV system based on experimental data for fault detection using kNN method”, Solar Energy, vol. 173, pp. 139–151, 2018.

PVSyst, “Unavailability Loss”, Accessed: 2024-10-28, 2024, URL: https://www.pvsyst.com/help/unavailabilityloss.htm.