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

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

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

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:
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Last updated on Feb. 19, 2024