Photovoltaic fault diagnosis algorithm using fuzzy logic controller based on calculating distortion ratio of values
DOI:
https://doi.org/10.20998/2074-272X.2023.4.06Keywords:
photovoltaic system, fault diagnosis, distortion ratio of voltage and current, fuzzy logic controllerAbstract
Introduction. The efficiency of solar energy systems in producing electricity in a clean way. Reliance on it in industrial and domestic systems has led to the emergence of malfunctions in its facilities. During the operating period, these systems deteriorate, and this requires the development of a diagnostic system aimed at maintaining energy production at a maximum rate by detecting faults as soon as possible and addressing them. Goal. This work proposes the development of an algorithm to detect faults in the photovoltaic system, which based on fuzzy logic. Novelty. Calculate the distortion ratio of the voltage and current values resulting from each element in the photovoltaic system and processing it by the fuzzy logic controller, which leads to determining the nature of the fault. Results. As show in results using fuzzy logic control by calculating the distortion ratio of the voltage and current detect 12 faults in photovoltaic array, converter DC-DC and battery.
References
Khan S.A., Mahmood T., Awan K.S. A nature based novel maximum power point tracking algorithm for partial shading conditions. Electrical Engineering & Electromechanics, 2021, no. 6, pp. 54-63. doi: https://doi.org/10.20998/2074-272X.2021.6.08.
Latreche S., Badoud A.E., Khemliche M. Implementation of MPPT Algorithm and Supervision of Shading on Photovoltaic Module. Engineering, Technology & Applied Science Research, 2018, vol. 8, no. 6, pp. 3541-3544. doi: https://doi.org/10.48084/etasr.2354.
Basnet B., Chun H., Bang J. An Intelligent Fault Detection Model for Fault Detection in Photovoltaic Systems. Journal of Sensors, 2020, vol. 2020, art. no. 6960328. doi: https://doi.org/10.1155/2020/6960328.
Djalab A.A., Rezaoui M.M., Mazouz L., Teta A., Sabri N. Robust Method for Diagnosis and Detection of Faults in Photovoltaic Systems Using Artificial Neural Networks. Periodica Polytechnica Electrical Engineering and Computer Science, 2020, vol. 63, no. 3, pp. 291-302. doi: https://doi.org/10.3311/PPee.14828.
Davarifar M., Rabhi A., Hajjaji A.El. Comprehensive Modulation and Classification of Faults and Analysis Their Effect in DC Side of Photovoltaic System. Energy and Power Engineering, 2013, vol. 5, no. 4, pp. 230-236. doi: https://doi.org/10.4236/epe.2013.54B045.
Yi Z., Etemadi A.H. Fault Detection for Photovoltaic Systems Based on Multi-Resolution Signal Decomposition and Fuzzy Inference Systems. IEEE Transactions on Smart Grid, 2017, vol. 8, no. 3, pp. 1274-1283. doi: https://doi.org/10.1109/TSG.2016.2587244.
Livera A., Theristis M., Makrides G., Georghiou G.E. On-line failure diagnosis of grid-connected photovoltaic systems based on fuzzy logic. 2018 IEEE 12th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG 2018), 2018, pp. 1-6. doi: https://doi.org/10.1109/CPE.2018.8372537.
Perveen S., Ashfaq H., Asjad M. Fault Ranking in PV Module based on Artificial Intelligence Technique (AIT). 2019 International Conference on Power Electronics, Control and Automation (ICPECA), 2019, pp. 1-6. doi: https://doi.org/10.1109/ICPECA47973.2019.8975619.
Dhimish M., Badran G. Photovoltaic Hot-Spots Fault Detection Algorithm Using Fuzzy Systems. IEEE Transactions on Device and Materials Reliability, 2019, vol. 19, no. 4, pp. 671-679. doi: https://doi.org/10.1109/TDMR.2019.2944793.
Djalab A., Nekbil N., Laouid A.A., Kouzou A., Kadiri K. An Intelligent Technique to Diagnosis and Detection the Partial Shading Based on Fuzyy Logic for PV System. 2020 17th International Multi-Conference on Systems, Signals & Devices (SSD), 2020, pp. 235-238. doi: https://doi.org/10.1109/SSD49366.2020.9364109.
Boudaraia K., Mahmoudi H., Abbou A. MPPT Design Using Artificial Neural Network and Backstepping Sliding Mode Approach for Photovoltaic System under Various Weather Conditions. International Journal of Intelligent Engineering and Systems, 2019, vol. 12, no. 6, pp. 177-186. doi: https://doi.org/10.22266/ijies2019.1231.17.
Ali Moussa M., Derrouazin A., Latroch M., Aillerie M. A hybrid renewable energy production system using a smart controller based on fuzzy logic. Electrical Engineering & Electromechanics, 2022, no. 3, pp. 46-50. doi: https://doi.org/10.20998/2074-272X.2022.3.07.
Madeti S.R., Singh S.N. A comprehensive study on different types of faults and detection techniques for solar photovoltaic system. Solar Energy, 2017, vol. 158, pp. 161-185. doi: https://doi.org/10.1016/j.solener.2017.08.069.
Nebti K., Lebied R. Fuzzy maximum power point tracking compared to sliding mode technique for photovoltaic systems based on DC-DC boost converter. Electrical Engineering & Electromechanics, 2021, no. 1, pp. 67-73. doi: https://doi.org/10.20998/2074-272X.2021.1.10.
Mellit A., Tina G.M., Kalogirou S.A. Fault detection and diagnosis methods for photovoltaic systems: A review. Renewable and Sustainable Energy Reviews, 2018, vol. 91, pp. 1-17. doi: https://doi.org/10.1016/j.rser.2018.03.062.
Latreche S., Khenfer A., Khemliche M. Sensors placement for the faults detection and isolation based on bridge linked configuration of photovoltaic array. Electrical Engineering & Electromechanics, 2022, no. 5, pp. 41-46. doi: https://doi.org/10.20998/2074-272X.2022.5.07.
Saravanan S., Senthil Kumar R., Prakash A., Chinnadurai T., Tiwari R., Prabaharan N., Chitti Babu B. Photovoltaic array reconfiguration to extract maximum power under partially shaded conditions. Distributed Energy Resources in Microgrids: Integration, Challenges and Optimization, 2019, pp. 215-241. doi: https://doi.org/10.1016/B978-0-12-817774-7.00008-9.
Abbes H., Abid H., Loukil K., Toumi A., Abid M. Etude comparative de cinq algorithmes de commande MPPT pour un système photovoltaïque. Revue des Énergies Renouvelables, 2014, vol. 17, no. 3, pp. 435-445. (Fra).
Abdel-Maksoud H., Khater M., Shaaban S. Adaptive Fuzzy Logic PI Control for Switched Reluctance Motor Based on Inductance Model. International Journal of Intelligent Engineering and Systems, 2017, vol. 10, no. 4, pp. 41-49. doi: https://doi.org/10.22266/ijies2017.0831.05.
Marhraoui S., Abbou A., Cabrane Z., Rhaili S., Hichami N. Fuzzy Logic-Integral Backstepping Control for PV Grid-Connected System with Energy Storage Management. International Journal of Intelligent Engineering and Systems, 2020, vol. 13, no. 3, pp. 359-372. doi: https://doi.org/10.22266/ijies2020.0630.33.
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