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

Bilal Taghezouit, Fouzi Harrou, Cherif Larbes, Ying Sun, Smail Semaoui, Amar Hadj Arab, Salim Bouchakour

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


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 (Formula presented.) of 0.99. Moreover, the obtained results show that the detection and recognition of faults were successfully achieved.
Original languageEnglish (US)
Pages (from-to)7955
Issue number21
StatePublished - Oct 26 2022

Bibliographical note

KAUST Repository Item: Exported on 2022-11-28
Acknowledged KAUST grant number(s): OSR-2019-CRG7-3800
Acknowledgements: This work was supported by funding from the Centre de Développement des Energies Renouvelables (CDER), Direction Générale de la Recherche Scientifique et du Développement Technologique (DGRSDT) under Socioeconomic impact project No: DGRSDT/ CDER/174/2017, and from the King Abdullah University of Science and Technology (KAUST), Office of Sponsored Research (OSR), under Award No: OSR-2019-CRG7-3800.

ASJC Scopus subject areas

  • General Computer Science


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