JACIII Vol.28 No.1 pp. 41-48
doi: 10.20965/jaciii.2024.p0041

Research Paper:

Enhancing Fault Detection and Classification in Grid-Tied Solar Energy Systems Using Radial Basis Function and Fuzzy Logic-Controlled Data Switch

Hilario A. Calinao Jr.*,**,†, Reggie C. Gustilo*, Elmer P. Dadios*** ORCID Icon, and Ronnie S. Concepcion II*** ORCID Icon

*Department of Electronics and Computer Engineering, De La Salle University
2401 Taft Avenue, Manila 1004, Philippines

**Department of Electronics Engineering, Bulacan State University
Guinhawa, City of Malolos, Bulacan 3000, Philippines

***Department of Manufacturing Engineering and Management, De La Salle University
2401 Taft Avenue, Malate, Manila 1004, Philippines

Corresponding author

April 5, 2023
July 20, 2023
January 20, 2024
computational intelligence, fuzzy-logic controller, sensors, radial basis function neural network, grid-tied solar energy

This study integrates fuzzy logic-controlled data switching and the radial basis function neural network (RBFNN) for fault detection and classification in grid-tied solar energy systems. The fuzzy logic controller filters out invalid sensor data through a data switch, ensuring that the fault detection and classification system receives reliable input. Training data were prepared through data normalization using the z-score function and principal component analysis, thereby reducing data complexity and standardizing the inputs. The resulting RBFNN model exhibited a low mean squared error with a value of 7.67×10-4, indicating its ability to classify faults based on the actual system scenarios. Performance evaluation metrics, including accuracy, precision, recall, and F1-score, were used to assess the effectiveness of the RBFNN model. The model demonstrated high accuracy (96.4%), precision (98.281%), recall (98.013%), and F1-score (98.147%), indicating the suitability and effectiveness of the RBFNN model to identify and classify faults in grid-tied solar energy systems.

Cite this article as:
H. Calinao Jr., R. Gustilo, E. Dadios, and R. Concepcion II, “Enhancing Fault Detection and Classification in Grid-Tied Solar Energy Systems Using Radial Basis Function and Fuzzy Logic-Controlled Data Switch,” J. Adv. Comput. Intell. Intell. Inform., Vol.28 No.1, pp. 41-48, 2024.
Data files:
  1. [1] A. Dhoke, R. Sharma, and T. K. Saha, “An approach for fault detection and location in solar PV systems,” Sol. Energy, Vol.194, pp. 197-208, 2019.
  2. [2] J. D. de Guia, R. S. Concepcion, H. A. Calinao, S. C. Lauguico, E. P. Dadios, and R. R. P. Vicerra, “Application of Ensemble Learning with Mean Shift Clustering for Output Profile Classification and Anomaly Detection in Energy Production of Grid-Tied Photovoltaic System,” 2020 12th Int. Conf. on Information Technology and Electrical Engineering (ICITEE), pp. 286-291, 2020.
  3. [3] A. Dhoke, R. Sharma, and T. K. Saha, “A technique for fault detection, identification and location in solar photovoltaic systems,” Sol. Energy, Vol.206, pp. 864-874, 2020.
  4. [4] H. A. Calinao, R. C. Gustilo, E. Dadios, and R. Concepcion, “Adaptive Neuro-Fuzzy Inference System-Based DC Voltage Sensor for Solar Energy Measurement,” 2022 IEEE 14th Int. Conf. on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), 2022.
  5. [5] B. De Bruijn, T. A. Nguyen, D. Bucur, and K. Tei, “Benchmark datasets for fault detection and classification in sensor data,” Proc. of 5th Int. Conf. on Sensor Networks (SENSORNETS 2016), pp. 185-195, 2016.
  6. [6] A. B. Sharma, L. Golubchik, and R. Govindan, “Sensor faults: Detection methods and prevalence in real-world datasets,” ACM Trans. Sens. Netw., Vol.6, No.3, Article No.23, 2010.
  7. [7] Y. Wu, H. Wang, B. Zhang, and K.-L. Du, “Using Radial Basis Function Networks for Function Approximation and Classification,” Int. Sch. Res. Not., Vol.2012, Article No.e324194, 2012.
  8. [8] M. Hussain, M. Dhimish, S. Titarenko, and P. Mather, “Artificial neural network based photovoltaic fault detection algorithm integrating two bi-directional input parameters,” Renew. Energy, Vol.155, pp. 1272-1292, 2020.
  9. [9] “ACS758: Current Sensor IC with 100 µΩ Current Conductor.” [Accessed July 29, 2022]
  10. [10] “DHT11 Sensor Development Tools—Mouser Philippines.” [Accessed June 10, 2023].
  11. [11] “POWER: AC Power Analyzer 20Amps.” [Accessed June 10, 2023]
  12. [12] C. P. Ugale and V. V. Dixit, “Buck-boost converter using fuzzy logic for low voltage solar energy harvesting application,” 2017 11th Int. Conf. on Intelligent Systems and Control (ISCO), pp. 413-417, 2017.
  13. [13] H. A. Calinao, A. Bandala, R. Gustilo, E. Dadios, and M. Rosales, “Battery Management System with Temperature Monitoring Through Fuzzy Logic Control,” 2020 IEEE Region 10 Conf. (TENCON), pp. 852-857, 2020.
  14. [14] K. M. Kotb, M. F. Elmorshedy, H. S. Salama, and A. Dán, “Enriching the stability of solar/wind DC microgrids using battery and superconducting magnetic energy storage based fuzzy logic control,” J. Energy Storage, Vol.45, Article No.103751, 2022.
  15. [15] H. W. Herwanto, A. N. Handayani, A. P. Wibawa, K. L. Chandrika, and K. Arai, “Comparison of Min-Max, Z-Score and Decimal Scaling Normalization for Zoning Feature Extraction on Javanese Character Recognition,” 2021 7th Int. Conf. on Electrical, Electronics and Information Engineering (ICEEIE), 2021.
  16. [16] I. S. L. Tebexreni and C. L. T. Borges, “Efficient Methods to Calculate the Reliability of Energy Systems with Correlated Renewable Sources,” 2022 IEEE Int. Smart Cities Conf. (ISC2), 2022.
  17. [17] S. Diop, P. S. Traore, and M. L. Ndiaye, “Power and Solar Energy Predictions Based on Neural Networks and Principal Component Analysis with Meteorological Parameters of Two Different Cities: Case of Diass and Taïba Ndiaye,” 2022 IEEE Int. Conf. on Electrical Sciences and Technologies in Maghreb (CISTEM), 2022.
  18. [18] T. Wang, F. Zhang, H. Gu, H. Hu, and M. Kaur, “A research study on new energy brand users based on principal component analysis (PCA) and fusion target planning model for sustainable environment of smart cities,” Sustain. Energy Technol. Assess., Vol.57, Article No.103262, 2023.
  19. [19] X. Wang, H. Lin, H. Zhang, D. Miao, Q. Miao, and W. Liu, “Intelligent Drone-Assisted Fault Diagnosis for B5G-Enabled Space-Air-Ground-Space Networks,” IEEE Trans. Netw. Sci. Eng., Vol.8, No.4, pp. 2849-2860, 2021.
  20. [20] P. P. K. Chan, J. Zhu, Z.-W. Qiu, W. W. Y. Ng, and D. S. Yeung, “Comparison of different classifiers in fault detection in microgrid,” 2011 Int. Conf. on Machine Learning and Cybernetics, pp. 1210-1213, 2011.
  21. [21] K. R. Sathish and T. Ananthapadmanabha, “Power Quality Enhancement in Distribution System Integrated with Renewable Energy Sources Using Hybrid RBFNN-TSA Technique,” 2021 7th Int. Conf. on Electrical Energy Systems (ICEES), pp. 189-194, 2021.
  22. [22] R. Rajasekaran and P. U. Rani, “Combined HCS–RBFNN for energy management of multiple interconnected microgrids via bidirectional DC–DC converters,” Appl. Soft Comput., Vol.99, Article No.106901, 2021.
  23. [23] R. Concepcion and E. Dadios, “Bioinspired optimization of germination nutrients based on Lactuca sativa seedling root traits as influenced by seed stratification, fortification and light spectrums,” AGRIVITA, J. of Agricultural Science, Vol.43, No.1, pp. 174-189, 2021.
  24. [24] R. Concepcion II, J. Alejandrino, C. H. Mendigoria, E. Dadios, A. Bandala, E. Sybingco, and R. R. Vicerra, “Lactuca sativa leaf extract concentration optimization using evolutionary strategy as photosensitizer for TiO2-filmed Grätzel cell,” Optik, Vol.242, Article No.166931, 2021.

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Last updated on Feb. 19, 2024