Development of a Novel IoT-Enabled Power- Monitoring Architecture with Real-Time Data Visualization for Use in Domestic and Industrial Scenarios

Jadhav, A.R. et al (2021) Development of a Novel IoT-Enabled Power- Monitoring Architecture with Real-Time Data Visualization for Use in Domestic and Industrial Scenarios. IEEE Transactions on Instrumentation and Measurement, 70. ISSN 00189456

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Abstract

With the increased interest in smart cities and smart infrastructures, the need for energy conservation is increasing. Especially with the current electrical energy production mainly relying on nonrenewable resources, conservation of electrical energy is one of the challenging aspects across the globe. However, one can only perform energy conservation optimally by identifying consumption patterns at a granular level, which requires accurate and ubiquitous monitoring infrastructure. Because the electrical energy wastage can occur at any granularity (from a small house-hold appliance to grid-level wastage), the development of a low-cost, easy-to-install, and accurate power-monitoring infrastructure is need of the hour. Hence, in this article, we propose the developed designs for IoT-enabled power monitoring. First is the noninvasive power monitor with voltage connection. The second design introduces a novel split architecture with centralized voltage measurement, which removes the need for local voltage measurements. We have proposed the third and final version of the IoT-enabled power monitor to fulfill the need for three-phase power monitoring. Unlike first and second designs, this design can be used with noninvasive and invasive current sensors. The proposed architecture also supports essential features, such as secure data transfer. Developed devices transmit real-time data to the cloud server, which makes the data ubiquitously available anywhere and anytime. For analyzing the performance of the proposed architecture, the developed devices are deployed in real industrial scenarios. As an example use case, the electrical anomaly detection framework using the data collected is also explained, and the corresponding results are discussed.

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