An Intelligent Power Transformers Diagnostic System Based on Hierarchical Radial Basis Functions Improved by Linde Buzo Gray and Single-Layer Perceptron Algorithms

Mounia Hendel, Imen Souhila Bousmaha, Fethi Meghnefi, Issouf Fofana () and Mostefa Brahami
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Mounia Hendel: Electrical Engineering and Materials Laboratory, Higher School of Electrical and Energy Engineering, Oran 31000, Algeria
Imen Souhila Bousmaha: Intelligent Control and Electrical Power System, Djilali Liabes University of Sidi Bel Abbes, Sidi Bel Abbes 22000, Algeria
Fethi Meghnefi: Canada Research Chair, Tier 1, ViAHT, Department of Applied Sciences, University Québec, Chicoutimi, QC G7H 2B1, Canada
Issouf Fofana: Canada Research Chair, Tier 1, ViAHT, Department of Applied Sciences, University Québec, Chicoutimi, QC G7H 2B1, Canada
Mostefa Brahami: Intelligent Control and Electrical Power System, Djilali Liabes University of Sidi Bel Abbes, Sidi Bel Abbes 22000, Algeria

Energies, 2024, vol. 17, issue 13, 1-22

Abstract: Transformers are fundamental and among the most expensive electrical devices in any power transmission and distribution system. Therefore, it is essential to implement powerful maintenance methods to monitor and predict their condition. Due to its many advantages—such as early detection, accurate diagnosis, cost reduction, and rapid response time—dissolved gas analysis (DGA) is regarded as one of the most effective ways to assess a transformer’s condition. In this contribution, we propose a new probabilistic hierarchical intelligent system consisting of five subnetworks of the radial basis functions (RBF) type. Indeed, hierarchical classification minimizes the complexity of the discrimination task by employing a divide-and-conquer strategy, effectively addressing the issue of unbalanced data (a significant disparity between the categories to be predicted). This approach contributes to a more precise and sophisticated diagnosis of transformers. The first subnetwork detects the presence or absence of defects, separating defective samples from healthy ones. The second subnetwork further classifies the defective samples into three categories: electrical, thermal, and cellulosic decomposition. The samples in these categories are then precisely assigned to their respective subcategories by the third, fourth, and fifth subnetworks. To optimize the hyperparameters of the five models, the Linde–Buzo–Gray algorithm is implemented to reduce the number of centers (radial functions) in each subnetwork. Subsequently, a single-layer perceptron is trained to determine the optimal synaptic weights, which connect the intermediate layer to the output layer. The results obtained with our proposed system surpass those achieved with another implemented alternative (a single RBF), with an average sensitivity percentage as high as 96.85%. This superiority is validated by a Student’s t -test, showing a significant difference greater than 5% ( p -value < 0.001). These findings demonstrate and highlight the relevance of the proposed hierarchical configuration.

Keywords: DGA; probabilistic RBF; Linde–Buzo–Gray algorithm; single-layer perceptron; “divide-and-conquer” strategy (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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