An Internet of Things (IoT) based Monitoring System for Oil-immersed Transformers

Abstract

While the electricity power industry in the world continues to grow, it also becomes more traceable and smart with the developing technology. Naturally, the integration of these technologies into the electrical power systems brings with an additional cost. Most of the time producers and consumers struggle under the pressure of these additional costs and try new products that will reduce the cost. In this sense, competitive products in the market must be advantageous in terms of cost. In this study, an original IoT based transformer monitoring system has been developed by using open source software, modular and low cost components as an alternative to studies in literature. Arduino Mega 2560 microcontroller has been used as the main component in the system. The system can acquire data from a wide variety of sensors using internal and external transducers. And this data can be saved to the cloud system synchronously with the memory card on the monitoring system. The developed monitoring system has been utilized free and reliable platforms such as the ThingSpeak web interface and PushingBox Notification application. The all test process of the developed monitoring system has been carried out in the laboratory environment on a porotype transformer.

Exploring Digital Twin-Based Fault Monitoring: Challenges and Opportunities

“…For instance, they may need help capturing the subtle and complex interactions of the components in the system; they may not provide real-time monitoring and analysis; and they may be resource-intensive and costly to implement and maintain. To overcome these challenges, advanced fault monitoring systems, including but not limited to condition-based monitoring [ 6 ], predictive maintenance [ 7 ], prognostics and health management [ 8 ], and Internet-of-Things-based monitoring [ 9 ], have been developed that can automatically detect and diagnose faults in complex systems. These monitoring systems utilize combinations of various technologies such as data analytics, artificial intelligence, computer vision, control systems, etc, for the pattern identification that indicates the presence of a fault in real-time and massive data set analysis, which ensures a high level of precision and accuracy in capturing the interactions between components of the system and thus has an edge over traditional FM approaches.…”

Transformer vibration and noise monitoring system using internet of things

“…However, this system is not suitable for all applications due to wired connection requirements. In another approach [20], the authors built a monitoring system to monitor the aging level of transformer insulation instantly and anticipate maintenance needs. Here, the authors measured the current value and the winding, oil, and ambient temperature.…”