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. 2021 Dec 3;21(23):8093.
doi: 10.3390/s21238093.

Large-Scale Internet of Things Multi-Sensor Measurement Node for Smart Grid Enhancement

Adrian I Petrariu  1   2 , Eugen Coca  1 , Alexandru Lavric  1
Affiliations

Large-Scale Internet of Things Multi-Sensor Measurement Node for Smart Grid Enhancement

Adrian I Petrariu et al. Sensors (Basel). .

Abstract

Electric power infrastructure has revolutionized our world and our way of living has completely changed. The necessary amount of energy is increasing faster than we realize. In these conditions, the grid is forced to run against its limitations, resulting in more frequent blackouts. Thus, urgent solutions need to be found to meet this greater and greater energy demand. By using the internet of things infrastructure, we can remotely manage distribution points, receiving data that can predict any future failure points on the grid. In this work, we present the design of a fully reconfigurable wireless sensor node that can sense the smart grid environment. The proposed prototype uses a modular developed hardware platform that can be easily integrated into the smart grid concept in a scalable manner and collects data using the LoRaWAN communication protocol. The designed architecture was tested for a period of 6 months, revealing the feasibility and scalability of the system, and opening new directions in the remote failure prediction of low voltage/medium voltage switchgears on the electric grid.

Keywords: LoRaWAN; partial discharge; predictive maintenance; scalability; smart grid.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Technologies identified in the smart grid concept.
Figure 2
Figure 2
The LoRaWAN communication stack.
Figure 3
Figure 3
The proposed smart grid architecture based on the LoRaWAN communication.
Figure 4
Figure 4
Proposed MSMN architecture.
Figure 5
Figure 5
Hardware implementation of the MSMN node.
Figure 6
Figure 6
Communication architecture for the coverage estimation.
Figure 7
Figure 7
LoRaWAN coverage map measurement.
Figure 8
Figure 8
LoRaWAN coverage map simulation using RadioPlanner.
Figure 9
Figure 9
LoRaWAN Coverage Scenario 1.
Figure 10
Figure 10
Losses due to the geographical terrain variation for Scenario 1.
Figure 11
Figure 11
LoRaWAN Coverage Scenario 2.
Figure 12
Figure 12
Losses due to the geographical terrain variation for Scenario 2.
Figure 13
Figure 13
Environmental parameters from the MSMN (temperature, relative humidity, and dew point).
Figure 14
Figure 14
Ozone level received from the MSMN node.
See this image and copyright information in PMC

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