Photovoltaic system faults diagnosis using discrete wavelet transform based artificial neural networks




artificial neural network, discrete wavelet transform, fault diagnosis, photovoltaic system


Introduction. This research work focuses on the design and experimental validation of fault detection techniques in grid-connected solar photovoltaic system operating under Maximum Power Point Tracking mode and subjected to various operating conditions. Purpose. Six fault scenarios are considered in this study including partial shading, open circuit in the photovoltaic array, complete failure of one of the six IGBTs of the inverter and some parametric faults that may appear in controller of the boost converter. Methods. The fault detection technique developed in this work is based on artificial neural networks and uses discrete wavelet transform to extract the features for the identification of the underlying faults. By applying discrete wavelet transform, the time domain inverter output current is decomposed into different frequency bands, and then the root mean square values at each frequency band are used to train the neural network. Results. The proposed fault diagnosis method has been extensively tested on the above faults scenarios and proved to be very effective and extremely accurate under large variations in the irradiance and temperature. Practical significance. The results obtained in the binary numerical system allow it to be used as a machine code and the simulation results has been validated by MATLAB / Simulink software.

Author Biographies

A. A. Bengharbi, University of Tiaret

PhD Student, Energy Engineering and Computer Engineering (L2GEGI) Laboratory

S. Laribi, University of Tiaret

Doctor of Electrical Engineering, Energy Engineering and Computer Engineering (L2GEGI) Laboratory

T. Allaoui, University of Tiaret

Professor of Electrical Engineering, Energy Engineering and Computer Engineering (L2GEGI) Laboratory

A. Mimouni, University of Tiaret

PhD Student, Energy Engineering and Computer Engineering (L2GEGI) Laboratory


Eltawil M.A., Zhao Z. MPPT techniques for photovoltaic applications. Renewable and Sustainable Energy Reviews, 2013, vol. 25, pp. 793-813. doi:

Albers M.J., Ball G. Comparative Evaluation of DC Fault-Mitigation Techniques in Large PV Systems. IEEE Journal of Photovoltaics, 2015, vol. 5, no. 4, pp. 1169-1174. doi:

Liu S., Dong L., Liao X., Cao X., Wang X., Wang B. Application of the Variational Mode Decomposition-Based Time and Time–Frequency Domain Analysis on Series DC Arc Fault Detection of Photovoltaic Arrays. IEEE Access, 2019, vol. 7, pp. 126177-126190. doi:

Akbar F., Mehmood T., Sadiq K., Ullah M.F. Optimization of accurate estimation of single diode solar photovoltaic parameters and extraction of maximum power point under different conditions. Electrical Engineering & Electromechanics, 2021, no. 6, pp. 46-53. doi:

Kim G.G., Lee W., Bhang B.G., Choi J.H., Ahn H.-K. Fault Detection for Photovoltaic Systems Using Multivariate Analysis With Electrical and Environmental Variables. IEEE Journal of Photovoltaics, 2021, vol. 11, no. 1, pp. 202-212. doi:

Appiah A.Y., Zhang X., Ayawli B.B.K., Kyeremeh F. Long Short-Term Memory Networks Based Automatic Feature Extraction for Photovoltaic Array Fault Diagnosis. IEEE Access, 2019, vol. 7, pp. 30089-30101. doi:

Jayamaha D.K.J.S., Lidula N.W.A., Rajapakse A.D. Wavelet-Multi Resolution Analysis Based ANN Architecture for Fault Detection and Localization in DC Microgrids. IEEE Access, 2019, vol. 7, pp. 145371-145384. doi:

Aziz F., Ul Haq A., Ahmad S., Mahmoud Y., Jalal M., Ali U. A Novel Convolutional Neural Network-Based Approach for Fault Classification in Photovoltaic Arrays. IEEE Access, 2020, vol. 8, pp. 41889-41904. doi:

Veerasamy V., Wahab N.I.A., Othman M.L., Padmanaban S., Sekar K., Ramachandran R., Hizam H., Vinayagam A., Islam M.Z. LSTM Recurrent Neural Network Classifier for High Impedance Fault Detection in Solar PV Integrated Power System. IEEE Access, 2021, vol. 9, pp. 32672-32687. doi:

Wang M.-H., Lu S.-D., Liao R.-M. Fault Diagnosis for Power Cables Based on Convolutional Neural Network With Chaotic System and Discrete Wavelet Transform. IEEE Transactions on Power Delivery, 2022, vol. 37, no. 1, pp. 582-590. doi:

Ankar S., Sahu U., Yadav A. Wavelet-ANN Based Fault Location Scheme for Bipolar CSC-Based HVDC Transmission System. 2020 First International Conference on Power, Control and Computing Technologies (ICPC2T), 2020, pp. 85-90. doi:

Souad S.L., Azzedine B., Meradi S. Fault diagnosis of rolling element bearings using artificial neural network. International Journal of Electrical and Computer Engineering (IJECE), 2020, vol. 10, no. 5, pp. 5288-5295. doi:

Jayamaha D.K.J., Lidula N.W., Rajapakse A. Wavelet Based Artificial Neural Networks for Detection and Classification of DC Microgrid Faults. 2019 IEEE Power & Energy Society General Meeting (PESGM), 2019, pp. 1-5. doi:

Bakdi A., Bounoua W., Guichi A., Mekhilef S. Real-time fault detection in PV systems under MPPT using PMU and high-frequency multi-sensor data through online PCA-KDE-based multivariate KL divergence. International Journal of Electrical Power & Energy Systems, 2021, vol. 125, art. no. 106457. doi:

Guichi A., Talha A., Berkouk E.M., Mekhilef S., Gassab S. A new method for intermediate power point tracking for PV generator under partially shaded conditions in hybrid system. Solar Energy, 2018, vol. 170, pp. 974-987. doi:

Bakdi A., Bounoua W., Mekhilef S., Halabi L.M. Nonparametric Kullback-divergence-PCA for intelligent mismatch detection and power quality monitoring in grid-connected rooftop PV. Energy, 2019, vol. 189, art. no. 116366. doi:

Eddine C.B.D., Azzeddine B., Mokhtar B. Detection of a two-level inverter open-circuit fault using the discrete wavelet transforms technique. 2018 IEEE International Conference on Industrial Technology (ICIT), 2018, pp. 370-376. doi:

Souad L., Azzedine B., Eddine C.B.D., Boualem B., Samir M., Youcef M. Induction machine rotor and stator faults detection by applying the DTW and N-F network. 2018 IEEE International Conference on Industrial Technology (ICIT), 2018, pp. 431-436. doi:

Bouchaoui L., Hemsas K.E., Mellah H., Benlahneche S. Power transformer faults diagnosis using undestructive methods (Roger and IEC) and artificial neural network for dissolved gas analysis applied on the functional transformer in the Algerian north-eastern: a comparative study. Electrical Engineering & Electromechanics, 2021, no. 4, pp. 3-11. doi:

Talha M., Asghar F., Kim S.H. A Novel Three-Phase Inverter Fault Diagnosis System Using Three-dimensional Feature Extraction and Neural Network. Arabian Journal for Science and Engineering, 2019, vol. 44, no. 3, pp. 1809-1822. doi:

Bakdi A., Guichi A., Mekhilef S., Bounoua W. GPVS-Faults: Experimental Data for fault scenarios in grid-connected PV systems under MPPT and IPPT modes, Mendeley Data, 2020, V1. doi:




How to Cite

Bengharbi, A. A., Laribi, S., Allaoui, T., & Mimouni, A. (2022). Photovoltaic system faults diagnosis using discrete wavelet transform based artificial neural networks. Electrical Engineering & Electromechanics, (6), 42–47.



Power Stations, Grids and Systems