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.

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.

Journal of Electronic & Information Systems

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

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