Analysis of Intra-String Line-Line Fault in Photovoltaic System
Document Type : Original Article
Authors
1 Department of Electrical Engineering, Imam Khomeini International University, Qazvin, Iran
2 Faculty of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran.
10.22059/jser.2024.367117.1352
Abstract
Line-to-line fault (LLF) is one of the most important fault occurring in photovoltaic (PV) systems. necessitating comprehensive investigation and analysis to develop optimal fault detection methodologies. This study focuses on analyzing a specific LLF variant known as intrastring line-to-line fault (ISLLF), wherein one or more modules within individual strings are short-circuited. The power-voltage (P-V) and current-voltage (I-V) curves of PV systems contain extensive data valuable for fault detection. Thus, exact analysis of these curves to extract various features is essential. The extremum points of P-V curve indicate the severity of occurred faults in PV system. In this paper, different states of triple and quadruple ISLLF are simulated and according to the obtained result, mathematical equations are presented for extremum values. Additionally, the performance of the maximum power point tracking (MPPT) controller is evaluated, and the requisite constraints for optimal power selection using MPPT across different states of the P-V curves are presented. The derived equations suggest insights into accurately determining the severity and location of LLF occurrences.
Keywords
References
[1] Yang, H., Ding, K., Chen, X., Jiang, M., Yang, Z., Zhang, J., & Gao, R. (2024). Fast simulation modeling and multiple-PS fault diagnosis of the PV array based on I–V curve conversion. Energy Conversion and Management, 300, 117965. DOI: 10.1016/j.enconman.2023.117965.
[2] Ma, X., Huang, W. H., Schnabel, E., Köhl, M., Brynjarsdóttir, J., Braid, J. L., & French, R. H. (2019). Data-driven I-V feature extraction for photovoltaic modules. IEEE Journal of Photovoltaics, 9(5), 1405-1412. DOI: 10.1109/JPHOTOV.2019.2928477.
[3] Ma, M., Zhang, Z., Yun, P., Xie, Z., Wang, H., & Ma, W. (2021). Photovoltaic module current mismatch fault diagnosis based on I-V data. IEEE Journal of Photovoltaics, 11(3), 779-788. DOI: 10.1109/JPHOTOV.2021.3059425.
[4] Pandey, A. K., Singh, V., & Jain, S. (2023). Maximum power point tracking algorithm based on fuzzy logic control using PV and IV characteristics for PV array. IEEE Transactions on Industry Applications. DOI: 10.1109/TIA.2023.3272536.
[5] Mehta, H. K., & Panchal, A. K. (2021). PV panel performance evaluation via accurate V–I polynomial with efficient computation. IEEE Journal of Photovoltaics, 11(6), 1519-1527. DOI: 10.1109/JPHOTOV.2021.3115250.
[6] Aquib, M., Jain, S., & Gosh, S. (2022). A Technique for Tracking the Global Peak of PV Arrays During Partially Shaded Conditions Using the Detection of Current Source and Voltage Source Regions of I–V Curves. IEEE Journal of Emerging and Selected Topics in Industrial Electronics, 3(4), 1096-1105. DOI: 10.1109/JESTIE.2022.3150257.
[7] Sarvi, M., & Azadian, A. (2022). A comprehensive review and classified comparison of MPPT algorithms in PV systems. Energy Systems, 13(2), 281-320. DOI: 10.1007/s12667-021-00427-x.
[8] Haj Seyed Aboutorabi, S. M., & Sarvi, M. (2020). A new method for solar array maximum power point determining and tracking. Tabriz Journal of Electrical Engineering, 49(4), 1559-1567. https://sid.ir/paper/401772/en.
[9] Huang, J. M., Wai, R. J., & Gao, W. (2019). Newly-designed fault diagnostic method for solar photovoltaic generation system based on IV-curve measurement. IEEE Access, 7, 70919-70932. DOI: 10.1109/ACCESS.2019.2919337.
[10] Chen, Z., Chen, Y., Wu, L., Cheng, S., & Lin, P. (2019). Deep residual network based fault detection and diagnosis of photovoltaic arrays using current-voltage curves and ambient conditions. Energy Conversion and Management, 198, 111793. DOI: 10.1016/j.enconman.2019.111793.
[11] Abdulrazzaq, A. K., Bognár, G., & Plesz, B. (2022). Accurate method for PV solar cells and modules parameters extraction using I–V curves. Journal of King Saud University-Engineering Sciences, 34(1), 46-56. DOI: org/10.1016/j.jksues.2020.07.008.
[12] Wei, D., Wei, M., Cai, H., Zhang, X., & Chen, L. (2020). Parameters extraction method of PV model based on key points of IV curve. Energy conversion and management, 209, 112656. DOI: 10.1016/j.enconman.2020.112656.
[13] Gude, S., & Jana, K. C. (2020). Parameter extraction of photovoltaic cell using an improved cuckoo search optimization. Solar Energy, 204, 280-293. DOI: 10.1016/j.solener.2020.04.036.
[14] Qais, M. H., Hasanien, H. M., Alghuwainem, S., & Nouh, A. S. (2019). Coyote optimization algorithm for parameters extraction of three-diode photovoltaic models of photovoltaic modules. Energy, 187, 116001. DOI: 10.1016/j.energy.2019.116001.
[15] Premkumar, M., Babu, T. S., Umashankar, S., & Sowmya, R. (2020). A new metaphor-less algorithms for the photovoltaic cell parameter estimation. Optik, 208, 164559. DOI: 10.1016/j.ijleo.2020.164559.
[16] Kler, D., Goswami, Y., Rana, K. P. S., &Kumar, V. (2019). A novel approach to parameter estimation of photovoltaic systems using hybridized optimizer. Energy Conversion and Management, 187, 486-511. DOI: 10.1016/j.enconman.2019.01.102.
[17] Chen, H., Jiao, S., Heidari, A. A., Wang, M., Chen, X., & Zhao, X. (2019). An opposition-based sine cosine approach with local search for parameter estimation of photovoltaic models. Energy Conversion and Management, 195, 927-942. DOI: 10.1016/j.enconman.2019.05.057.
[18] Qais, M. H., Hasanien, H. M., & Alghuwainem, S. (2019). Identification of electrical parameters for three-diode photovoltaic model using analytical and sunflower optimization algorithm. Applied Energy, 250, 109-117. DOI: 10.1016/j.apenergy.2019.05.013.
[19] Li, S., Gong, W., Yan, X., Hu, C., Bai, D., & Wang, L. (2019). Parameter estimation of photovoltaic models with memetic adaptive differential evolution. Solar Energy, 190, 465-474. DOI: 10.1016/j.solener.2019.08.022.
[20] Yang, X., Gong, W., & Wang, L. (2019). Comparative study on parameter extraction of photovoltaic models via differential evolution. Energy conversion and management, 201, 112113. DOI: 10.1016/j.enconman.2019.112113.
[21] Mostafa, M., Rezk, H., Aly, M., & Ahmed, E. M. (2020). A new strategy based on slime mould algorithm to extract the optimal model parameters of solar PV panel. Sustainable Energy Technologies and Assessments, 42, 100849. DOI: 10.1016/j.seta.2020.100849.
[22] Polo, J., Martín-Chivelet, N., Alonso-García, M. C., Zitouni, H., Alonso-Abella, M., Sanz-Saiz, C., & Vela-Barrionuevo, N. (2019). Modeling IV curves of photovoltaic modules at indoor and outdoor conditions by using the Lambert function. Energy conversion and management, 195, 1004-1011. DOI: 10.1016/j.enconman.2019.05.085.
[23] Chen, Z., Chen, Y., Wu, L., Cheng, S., Lin, P., & You, L. (2019). Accurate modeling of photovoltaic modules using a 1-D deep residual network based on IV characteristics. Energy conversion and management, 186, 168-187. DOI: 10.1016/j.enconman.2019.02.032.
[24] Pourmousa, N., Ebrahimi, S. M., Malekzadeh, M., & Alizadeh, M. (2019). Parameter estimation of photovoltaic cells using improved Lozi map based chaotic optimization Algorithm. Solar Energy, 180, 180-191. DOI: 10.1016/j.solener.2019.01.026.
[25] Liang, J., Qiao, K., Yu, K., Ge, S., Qu, B., Xu, R., & Li, K. (2020). Parameters estimation of solar photovoltaic models via a self-adaptive ensemble-based differential evolution. Solar Energy, 207, 336-346. DOI: 10.1016/j.solener.2020.06.100.
[26] Hao, P., Zhang, Y., Lu, H., & Lang, Z. (2021). A novel method for parameter identification and performance estimation of PV module under varying operating conditions. Energy Conversion and Management, 247, 114689. DOI: 10.1016/j.enconman.2021.114689.
[27] Amiri, A. F., Oudira, H., Chouder, A., & Kichou, S. (2024). Faults detection and diagnosis of PV systems based on machine learning approach using random forest classifier. Energy Conversion and Management, 301, 118076. DOI: 10.1016/j.enconman.2024.118076.
[28] Abd el-Ghany, H. A., ELGebaly, A. E., & Taha, I. B. (2021). A new monitoring technique for fault detection and classification in PV systems based on rate of change of voltage-current trajectory. International Journal of Electrical Power & Energy Systems, 133, 107248. DOI: 10.1016/j.ijepes.2021.107248.
[29] Aallouche, A., & Ouadi, H. (2022). Online fault detection and identification for an isolated PV system using ANN. IFAC-PapersOnLine, 55(12), 468-475. DOI: 10.1016/j.ifacol.2022.07.356.
[30] Bacha, M., & Terki, A. (2022). Diagnosis algorithm and detection faults based on fuzzy logic for PV panel. Materials Today: Proceedings, 51, 2131-2138. DOI:10.1016/j.matpr.2021.12.490.
[31] Li, C., Yang, Y., Zhang, K., Zhu, C., & Wei, H. (2021). A fast MPPT-based anomaly detection and accurate fault diagnosis technique for PV arrays. Energy Conversion and Management, 234, 113950. DOI: 10.1016/j.enconman.2021.113950.
[32] Liu, Y., Ding, K., Zhang, J., Lin, Y., Yang, Z., Chen, X., ... & Chen, X. (2022). Intelligent fault diagnosis of photovoltaic array based on variable predictive models and I–V curves. Solar Energy, 237, 340-351. DOI: 10.1016/j.solener.2022.03.062.
[33] Miao, W., Lam, K. H., & Pong, P. W. (2020). A string-current behavior and current sensing-based technique for line–line fault detection in photovoltaic systems. IEEE Transactions on Magnetics, 57(2), 1-6.
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