Teknologi Deteksi dan Diagnosis Kerusakan pada PLTS: Review

Taufal Hidayat

Sari


Perkembangan yang signifikan dari pemanfaatan Pembangkit Listrik Tenaga Surya (PLTS) sebagai sumber energi alternatif pengganti sumber energi fosil telah membuat pentingnya ada model rancangan sistem deteksi dan monitoring yang baik guna menghindari terjadinya kerusakan pada PLTS tersebut. Beragam teknik deteksi dan monitoring telah dikembangkan dalam berbagai literatur. Artikel ini mereview dan menganalisis berbagai kerusakaan yang mungkin terjadi pada PLTS dan berbagai teknologi deteksi yang dapat diterapkan untuk menghindari kemungkinan terjadi kerusakan serius pada PLTS.

Teks Lengkap:

PDF

Referensi


S. Time et al., “An Irradiance Independent , Robust Ground Fault Detection Scheme for PV Arrays Based on Spread,” vol. 8993, no. c, 2017.

D. S. Pillai and N. Rajasekar, “An MPPT Based Sensorless Line - Line and Line - Ground Fault Detection Technique for PV Systems,” IEEE Trans. Power Electron., vol. PP, no. c, p. 1, 2018.

B. P. Kumar et al., “Online Fault Detection and Diagnosis in Photovoltaic Systems Using Wavelet Packets,” vol. 8, no. 1, pp. 257–265, 2018.

M. Dhimish, P. Mather, and V. H. Member, “Novel Photovoltaic Hot - spotting Fault Detection Algorithm,” IEEE Trans. Device Mater. Reliab., vol. PP, no. c, p. 1, 2019.

P. Jain et al., “Transactions on Power Electronics A Digital Twin Approach for Fault Diagnosis in Distributed Photovoltaic Systems,” IEEE Trans. Power Electron., vol. PP, no. c, p. 1, 2019.

H. Momeni, N. Sadoogi, M. Farrokhifar, and H. F. Gharibeh, “Fault Diagnosis in Photovoltaic Arrays Using GBSSL Method and Proposing a Fault Correction,” IEEE Trans. Ind. Informatics, vol. PP, no. X, p. 1, 2019.

A. Haque and M. A. Khan, “Fault diagnosis of Photovoltaic Modules,” no. September, pp. 1–23, 2018.

Y. Li, K. Ding, J. Zhang, F. Chen, X. Chen, and J. Wu, “A fault diagnosis method for photovoltaic arrays based on fault parameters identification,” Renew. Energy, 2019.

N. Katayama, S. Osawa, S. Matsumoto, T. Nakano, and M. Sugiyama, “Solar Energy Materials and Solar Cells Degradation and fault diagnosis of photovoltaic cells using impedance spectroscopy,” Sol. Energy Mater. Sol. Cells, vol. 194, no. September 2018, pp. 130–136, 2019.

F. Harrou, B. Taghezouit, and Y. Sun, “Robust and fl exible strategy for fault detection in grid-connected photovoltaic systems,” Energy Convers. Manag., vol. 180, no. June 2018, pp. 1153–1166, 2019.

G. K. F. C-means and C. Algorithm, “Photovoltaic Array Fault Diagnosis Based on,” pp. 1–15, 2019.

F. Pedro, G. Márquez, and I. Segovia, “Condition Monitoring System for Solar Power Plants with Radiometric and Thermographic Sensors Embedded in Unmanned Aerial Vehicles,” Measurement, 2019.

F. Harrou, A. Dairi, B. Taghezouit, and Y. Sun, “An unsupervised monitoring procedure for detecting anomalies in photovoltaic systems using a one-class Support Vector Machine,” Sol. Energy, vol. 179, no. December 2018, pp. 48–58, 2019.

Y. Chaibi, M. Malvoni, A. Chouder, M. Boussetta, and M. Salhi, “Simple and efficient approach to detect and diagnose electrical faults and partial shading in photovoltaic systems,” Energy Convers. Manag., vol. 196, no. February, pp. 330–343, 2019.

J. Huang, R. Wai, and W. Gao, “Newly-Designed Fault Diagnostic Method for Solar Photovoltaic Generation System Based on IV-Curve Measurement,” no. 1, p. 1.

M. Ahmadi, H. Samet, and T. Ghanbari, “A new method for detecting series arc fault in photovoltaic systems based on the blind source separation,” IEEE Trans. Ind. Electron., vol. PP, no. c, p. 1, 2019.

R. Fezai, M. Mansouri, M. Trabelsi, M. Hajji, H. Nounou, and M. Nounou, “Online reduced kernel GLRT technique for improved fault detection in photovoltaic systems,” Energy, vol. 179, pp. 1133–1154, 2019.

Z. Chen, Y. Chen, L. Wu, S. Cheng, and P. Lin, “Deep residual network based fault detection and diagnosis of photovoltaic arrays using current-voltage curves and ambient conditions,” Energy Convers. Manag., vol. 198, no. May, p. 111793, 2019.

J. Wang, D. Gao, S. Zhu, S. Wang, and H. Liu, “Environmental Effects Fault diagnosis method of photovoltaic array based on support vector machine,” Energy Sources, Part A Recover. Util. Environ. Eff., vol. 00, no. 00, pp. 1–16, 2019.

R. Fazai et al., “Machine learning-based statistical testing hypothesis for fault detection in photovoltaic systems,” Sol. Energy, vol. 190, no. August, pp. 405–413, 2019.

S. Fadhel et al., “PV shading fault detection and classi fi cation based on I-V curve using principal component analysis : Application to isolated PV system,” Sol. Energy, vol. 179, no. December 2018, pp. 1–10, 2019.

X. Lu, P. Lin, S. Cheng, Y. Lin, Z. Chen, and L. Wu, “Fault diagnosis for photovoltaic array based on convolutional neural network and electrical time series graph,” Energy Convers. Manag., vol. 196, no. February, pp. 950–965, 2019.




DOI: http://dx.doi.org/10.21063%2FJTE.2020.3133903

Refbacks

  • Saat ini tidak ada refbacks.


DOAJ logo

Ciptaan disebarluaskan di bawah  Creative Commons Attribution-ShareAlike 4.0 International License.