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Deployment of AI-based RBF network for photovoltaics fault detection procedure

Department of Engineering and Technology, Laboratory of Photovoltaics, University of Huddersfield, Huddersfield, HD1 3DH, United Kingdom

Special Issues: Health Monitoring of Electrical Actuators and their supplies

In this paper, a fault detection algorithm for photovoltaic systems based on artificial neural networks (ANN) is proposed. Although, a rich amount of research is available in the field of PV fault detection using ANN, this paper presents a novel methodology based on only two inputs for the training, validating and testing of the Radial Basis Function (RBF) network achieving unprecedented detection accuracy of 98.1%. The proposed methodology goes beyond data normalisation and implements a ‘mapping of inputs’ approach to the data set before exposing it to the network for training. The accuracy of the proposed network is further endorsed through testing of the network in partial shading and overcast conditions.
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References

1. Di Piazza C, Viola F, Vitale G (2018) Evaluation of ground currents in a PV system with high frequency modeling. International Journal of Renewable Energy Research 8: 1770-1778.

2. Dhimish M, Holmes V, Mehrdadi B, et al. (2017) Seven indicators variations for multiple PV array configurations under partial shading and faulty PV conditions. Renew Energ 113: 438-460.    

3. Dhimish M, Mather P, Holmes V (2018) Evaluating power loss and performance ratio of hot-spotted photovoltaic modules. IEEE T Electron Dev 56: 5419-5427.

4. Schirone L, Califano FP, Pastena M (1994) Fault detection in a photovoltaic plant by time domain Reflectometry. Prog Photovoltaics 2: 35-44.    

5. Silvestre S, da Silva MA, Chouder A, et al. (2014) New procedure for fault detection in grid connected PV systems based on the evaluation of current and voltage indicators. Energ Convers Manage 86: 241-249.    

6. Li X, Wen H, Hu Y, et al. (2019) Drift-free current sensorless MPPT algorithm in photovoltaic systems. Sol Energy 177: 118-126.    

7. Wu Y, Lan Q, Sun Y (2009) Application of BP neural network fault diagnosis in solar photovoltaic system. 2009 International conference on Mechatronics and Automation 2581-2585.

8. Chen Z, Chen Y, Wu L, et al. (2019) Deep residual network based fault detection and diagnosis of photovoltaic arrays using current-voltage curves and ambient conditions. Energ Convers Manage 198: 111793.    

9. Dhimish M, Mather P, Holmes V, et al. (2018) CDF modelling for the optimum tilt and azimuth angle for PV installations: case study based on 26 different locations in region of the Yorkshire UK. IET Renewable Power Generation 13: 399-408.

10. Pan T, Chen J, Zhou Z, et al. (2019) A Novel Deep Learning Network via Multi-Scale Inner Product with Locally Connected Feature Extraction for Intelligent Fault Detection. IEEE T Ind Inform 9: 5119-5128.

11. Belaout A, Krim F, Mellit A, et al. (2018) Multiclass adaptive neuro-fuzzy classifier and feature selection techniques for photovoltaic array fault detection and classification. Renew Energ 127: 548-558.    

12. Lapedes A (1987) Nonlinear signal processing using neural networks. Technical Report No. LA-UR-87-2662.

13. Polo O, Bermejo F, Fernández G, et al. (2015) Failure mode prediction and energy forecasting of PV plants to assist dynamic maintenance tasks by ANN based models. Renew Energ 81: 227-238.    

14. Sun Y, Xu, J, Qiang H, et al. (2019) Adaptive sliding mode control of maglev system based on RBF neural network minimum parameter learning method. Measurement 141: 217-226.    

15. Yagi Y, Kishi H, Hagihara R, et al. (2003) Diagnostic technology and an expert system for photovoltaic systems using the learning method. Sol Energ Mat Sol C 75: 655-663.    

16. Chine W, Mellit A, Lughi V, et al. (2016) A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks. Renew Energ 90: 501-512.    

17. Tadj M, Benmouiza K, Cheknane A, et al. (2014) Improving the performance of PV systems by faults detection using GISTEL approach. Energ Convers Manage 80: 298-304.    

18. Mellit A, Sağlam S, Kalogirou A (2013) Artificial neural network-based model for estimating the produced power of a photovoltaic module. Renew Energ 60: 71-78.    

19. Chine W, Mellit A, Lughi V, et al. (2016) A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks. Renew Energ 90: 501-512.    

20. Chen Y, Yu G, Long Y, et al. (2019) Application of radial basis function artificial neural network to quantify interfacial energies related to membrane fouling in a membrane bioreactor. Bioresource Technol 293: 122103.    

21. Zhao Z, Lou Y, Chen Y, et al. (2019) Prediction of interfacial interactions related with membrane fouling in a membrane bioreactor based on radial basis function artificial neural network (ANN). Bioresource Technol 282: 262-268.    

22. Karmacharya M, Gokaraju R (2017) Fault location in ungrounded photovoltaic system using wavelets and ANN. IEEE T Power Deliver 33: 549-559.

23. Dhimish M, Holmes V, Mehrdadi B, et al. (2018) Comparing Mamdani Sugeno fuzzy logic and RBF ANN network for PV fault detection. Renew Energ 117: 257-274.    

24. Fadhel S, Delpha C, Diallo D, et al. (2019) PV shading fault detection and classification based on IV curve using principal component analysis: Application to isolated PV system. Sol Energy 179: 1-10.    

25. Dhimish M, Chen Z (2019) Novel Open-Circuit Photovoltaic Bypass Diode Fault Detection Algorithm. IEEE J Photovolt 9: 1819-1827.    

© 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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