Journal of Electronic & Information Systems

Transformer Fault Diagnosis: A Shallow Learning Approach for DGA-Based Incipient Fault Detection

2025-12-06T10:11:01+08:00

Power transformers are exposed to electrical, thermal, and mechanical stresses during operation, leading to the degradation of insulation and the generation of dissolved gases. Utilities use IEEE and IEC standards use dissolved gas analysis (DGA) to detect incipient faults in oil-filled in-service transformers. Traditional gas ratio-based DGA methods, at times inconclusive diagnoses, limiting their effectiveness in scheduling preventive maintenance. This study presents the application of a shallow learning Backpropagation Neural Network (BP-NN) for assessing the condition of normal ageing and classification of incipient faults in oil-immersed power transformers. The model is trained using the concentrations (ppm) of five key gases—H₂, CH₄, C₂H₂, C₂H₄, and C₂H₆—as input features. The classified condition of a transformer is normal ageing and five fault type, namely partial discharge, low-energy and high-energy discharges, and thermal faults across two varying temperature ranges. The data set used for the classification of incipient faults within transformers is that where the fault type is confirmed by physical inspection. The 256 samples used in this work are from published sources, including the IEC TC10 database. The results achieved by the BP-NN demonstrate its capability to accurately classify normal ageing and diagnose five types of faults. For evaluating the performance of the trained NN, the IEEE/IEC method of classification, the benchmark used is the actual fault type. The shallow network of pattern recognition successfully identified the presence of normal ageing and the five fault types. The performance of the test set is 94.73%. The results highlight the potential of BP-NNs for enhanced transformer condition monitoring and early fault detection. As more high-quality labelled data become available, the diagnostic accuracy and robustness of the model are expected to improve further.

Semantic Variational Bayes Based on Semantic Information G Theory for Solving Latent Variables

2026-01-29T11:49:00+08:00

The minimum variational free energy criterion comprises two criteria: the maximum semantic information criterion and the maximum information efficiency criterion, but it does not provide a method for balancing them. The Semantic Information G Theory, the author proposed in his early years, extends the rate-distortion function R(D) to the rate-fidelity function R(G), where R is the minimum mutual information for given semantic mutual information G. Semantic Variational Bayes (SVB) is based on the parameter solution of R(G), where the variational and iterative methods originated from Shannon et al.'s research on the rate-distortion function. SVB not only uses likelihood functions but also truth, membership, similarity, distortion, and copula density functions as constraint functions. It explicitly uses the maximum information efficiency (G/R) criterion and facilitates the trade-off between maximum semantic information and maximum information efficiency. The computational experiments include 1) using some mixture models as an examples to show that mixture models converges as G/R increases; 2) demonstrating the application of SVB in data compression with a group of error ranges as the constraint; 3) illustrating how the semantic information measure and SVB can be used for maximum entropy control and reinforcement learning in control tasks with given range constraints, providing numerical evidence for balancing control's purposiveness and efficiency. The limitation of SVB is that it does not account for parameter probability distributions. Further research is needed to apply SVB to deep learning.

Development of an IoT-Based Real-Time Monitoring System for Light Intensity, Temperature, and Humidity in Dragon Fruit Farms

2026-01-06T09:49:24+08:00

The rapid advancement of smart agriculture under the Industry 4.0 paradigm has accelerated the integration of digital and IoT technologies into modern farming systems, aiming to enhance productivity, optimize resource utilization, and promote environmental sustainability. Meanwhile, dragon fruit is a major export fruit of Vietnam, grown mostly in Binh Thuan, Long An, and Tien Giang provinces. Following the above trend, this study presents the design and implementation of an Internet of Things (IoT)-based climate monitoring system that allows real-time observation and recording of light intensity, temperature, and humidity parameters at dragon fruit farms. The system integrates an ESP32 microcontroller, a DFRobot SEN0390 light sensor, and a digital temperature and humidity sensor SHT30. Data is transmitted via Wi-Fi to a cloud platform for real-time display, IoT MQTT (Message Queuing Telemetry Transport) Panel application, web interface and automatically stored in Google Sheets for long-term analysis. A key improvement of this study lies in the integration of wide-range light sensors compared to previous greenhouse IoT system studies. Experimental validation demonstrates stable system performance, with average data latency under two seconds and high measurement accuracy, confirming the reliability and scalability. The system provides an agricultural environmental monitoring solution for farmers, setting a basis for big data analytics and future automation in Vietnam.