Enhanced Fault Detection in Solar Photovoltaic Modules Using VMD-LSTM Model

Sikandar Shah Syed; Bin  Li

doi:10.31258/ijeepse.7.3.130-142

Sikandar Shah Syed College of Electrical, Energy and Power Engineering, Yangzhou University, China
Bin Li College of Electrical, Energy and Power Engineering, Yangzhou University, China

DOI: https://doi.org/10.31258/ijeepse.7.3.130-142

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Keywords: Current signal, EMD, IMF, LSTM, PSCAD simulation, Solar Photovoltaic.

Abstract

Detection of solar PV faults in an Accurate and versatile technique are essential because the safety and efficiency of solar photovoltaic (PV) modules greatly depend on efficient fault identification. However, because it can be challenging to identify complex operating patterns and detect tiny faults, currently methods frequently have low accuracy. This may make it more difficult to validate the models, which would limit their usefulness in the actual world. This paper presents a new fault detection method that combines Empirical Mode Decomposition (EMD) with the power of Long Short-Term Memory (LSTM) networks. Critical features are efficiently extracted from the data by use of an adaptive decomposition of voltage and current signals into Intrinsic Mode Functions k(IMFs) through the use of EMD. An LSTM network that has been trained to recognize complex patterns and periodic connections then processes this information. Our model which has been validated using a PSCAD simulation model, shows notable improvements in accuracy and durability of more than 92% after undergoing thorough testing on a simulated PV system that allows for several fault types and their severities when compared to existing methods.

References

H. Haes Alhelou, M. E. Hamedani-Golshan, T. C. Njenda, and P. Siano, "A survey on power system blackout and cascading events: Research motivations and challenges," Energies, vol. 12, 2019.

Y. Zhang, Y. Xu, and Z. Y. Dong, "Robust ensemble data analytics for incomplete PMU measurements-based power system stability assessment," IEEE Trans. Power Syst., vol. 33, pp. 1124–1126, 2018.

L. Lawton, M. Sullivan, K. Van Liere, A. Katz, and J. Eto, "A framework and review of customer outage costs: Integration and analysis of electric utility outage cost surveys," 2003.

A. Jaech, B. Zhang, M. Ostendorf, and D. S. Kirschen, "Real-time prediction of the duration of distribution system outages," IEEE Trans. Power Syst., vol. 34, pp. 773–781, 2019.

N. Eyring and N. Kittner, "High-resolution electricity generation model demonstrates suitability of high-altitude floating solar power," iScience, vol. 25, p. 104394, 2022.

E. García, N. Ponluisa, E. Quiles, R. Zotovic-Stanisic, and S. C. Gutiérrez, "Solar panels string predictive and parametric fault diagnosis using low-cost sensors," Sensors, vol. 22, 2022.

V. Narayanaswamy, R. Ayyanar, C. Tepedelenlioglu, D. Srinivasan, and A. Spanias, "Optimizing solar power using array topology reconfiguration with regularized deep neural networks," IEEE Access, vol. 11, pp. 7461–7470, 2023.

M. M. Rahman, I. Khan, and K. Alameh, "Potential measurement techniques for photovoltaic module failure diagnosis: A review," Renewable Sustainable Energy Rev., vol. 151, p. 111532, 2021.

A. Mehmood, H. A. Sher, A. F. Murtaza, and K. Al-Haddad, "Fault detection, classification and localization algorithm for photovoltaic array," IEEE Trans. Energy Convers., vol. 36, pp. 2945–2955, 2021.

Y. He, C. Zhou, and Y. Hu, "Application of LSTM method combined with feature optimization in chiller failure detection," J. Phys.: Conf. Ser., vol. 2442, p. 012026, Feb. 2023.

F. Deng, Z. Liang, N. Ding, X. Fan, X. Gao, C. A. I. Yuyun, and J. Chen, Photovoltaic array fault diagnosis method based on composite information, Google Patents, 2021.

A. F. Amiri, S. Kichou, H. Oudira, A. Chouder, and S. Silvestre, "Fault detection and diagnosis of a photovoltaic system based on deep learning using the combination of a convolutional neural network (CNN) and bidirectional gated recurrent unit (Bi-GRU)," Sustainability, vol. 16, 2024.

C. M. Furse, M. Kafal, R. Razzaghi, and Y.-J. Shin, "Fault diagnosis for electrical systems and power networks: A review," IEEE Sens. J., vol. 21, pp. 888–906, 2021.

J. Van Gompel, D. Spina, and C. Develder, "Temporal convolutional networks for fault diagnosis of photovoltaic systems using satellite and inverter measurements," in Proc. 8th ACM Int. Conf. Syst. Energy-Efficient Buildings, Cities, Transportation, New York, NY, USA, 2021.

Z. Chen, L. Wu, S. Cheng, P. Lin, Y. Wu, and W. Lin, "Intelligent fault diagnosis of photovoltaic arrays based on optimized kernel extreme learning machine and I-V characteristics," Appl. Energy, vol. 204, pp. 912–931, 2017.

Z. Chen, F. Han, L. Wu, J. Yu, S. Cheng, P. Lin, and H. Chen, "Random forest based intelligent fault diagnosis for PV arrays using array voltage and string currents," Energy Convers. Manage., vol. 178, pp. 250–264, 2018.

D. J. Sailor, J. Anand, and R. R. King, "Photovoltaics in the built environment: A critical review," Energy Buildings, vol. 253, p. 111479, 2021.

N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N.-C. Yen, C. C. Tung, and H. H. Liu, "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis," Proc. R. Soc. Lond. A Math. Phys. Sci., vol. 454, pp. 903–995, 1998.

A. Y. Appiah, X. Zhang, B. B. K. Ayawli, and F. Kyeremeh, "Long short-term memory networks based automatic feature extraction for photovoltaic array fault diagnosis," IEEE Access, vol. 7, pp. 30089–30101, 2019.

Z. Chang, Y. Zhang, and W. Chen, "Electricity price prediction based on hybrid model of Adam optimized LSTM neural network and wavelet transform," Energy, vol. 187, p. 115804, 2019.

Y. Yin, Z. Yu, X. Gou, and J. Wang, "Photovoltaic power prediction model based on empirical mode decomposition-long-short memory neural network," in 2021 Int. Conf. Intell. Comput., Autom. Syst. (ICICAS), 2021.

R. Jia, Y. A. N. G. GH, H. F. Zheng, H. Zhang, X. Liu, and H. Yu, "Combined wind power prediction method based on CNN-LSTM & GRU with adaptive weights," Electric Power, vol. 55, pp. 47–56, 2022.

A. Y. Appiah, X. Zhang, B. B. K. Ayawli, and F. Kyeremeh, "Long short-term memory networks based automatic feature extraction for photovoltaic array fault diagnosis," IEEE Access, vol. 7, pp. 30089–30101, 2019.

Z. Chen, L. Wu, S. Cheng, and P. Lin, "Intelligent fault diagnosis of photovoltaic arrays based on optimized kernel extreme learning machine and I-V characteristics," Applied Energy, vol. 204, pp. 912-931, 2017.

A. Mehmood, H. A. Sher, A. F. Murtaza, and K. Al-Haddad, "Fault detection, classification, and localization algorithm for photovoltaic array," IEEE Trans. Energy Convers., vol. 36, no. 3, pp. 2945–2955, 2021.

M. Ahmad, A. Iqbal, and S. Ali, "Efficient solar photovoltaic fault detection and classification method based on convolutional neural networks (CNN)," Int. J. Electr. Energy Power Syst. Eng., vol. 6, no. 2, pp. 89-96, 2023.

M. M. Rahman and I. Khan, "A hybrid model for solar PV fault diagnosis based on signal processing and machine learning techniques," Int. J. Electr. Energy Power Syst. Eng., vol. 5, no. 3, pp. 120-129, 2021.