Mapping and Testing Internet of Things Platforms for the Intelligent Maintenance of the Electrical Distribution Network
Katajamäki, Topias (2021-03-22)
Julkaisun pysyvä osoite on
http://urn.fi/URN:NBN:fi-fe202103228013
http://urn.fi/URN:NBN:fi-fe202103228013
Tiivistelmä
New technologies are crucial in the changing energy sector and the electricity network. The climate change and increasing dependence upon electricity are two main factors in this con-text. Consequently, there is a need to develop the reliability and quality of the electricity distribution system. The study was carried out in cooperation with Vaasan Sähköverkko. They wanted to explore and pilot possible alternatives to internet of things (IoT) technologies to be used in predictive maintenance of the electricity distribution network. The purpose of this study was to examine the features expected from good IoT platforms. Central to this study, was to demonstrate that IoT solutions could be built on these platforms in their operating environments connected to the distribution system. Internet of things platforms are a set of integrated software capabilities. The compared platforms in this study were M-Files, IoT-Ticket, Microsoft Azure, Amazon Web Services and Google Cloud Platform.
When comparing the selected IoT platforms, data related to different features was collected by implementing four practical cases. The first case was monitoring air conditions at Vaasa primary substation using a Ruuvitag sensor. The second case was use CoreTec and CoreSense to import condition monitoring data from the power transformer at Purola primary substation. The third example was import measurement and status data from the DC system at Alskat primary substation to IoT platforms. In the final case, data was retrieved from MicroSCADA Historian to a comma separated value file and exported to IoT platforms using either the representational state transfer application programmable Interface (REST API) or a Python software development kit. The results of this study demonstrate that it is possible to install of IoT technology on significantly different platforms. M-Files was the IoT platform with largest amount of open questions still remaining. IoT-Ticket appeared to be the easiest option for installation and end use. If an organization were to choose Microsoft Azure, Amazon Web Services or Google Cloud Platform, they would need to find reliable partners to develop the platforms with end users.
During this study, it became evident that IoT technology is relatively evolved and organizations should begin using to use it with a low threshold if suitable applications are found. For example, predictive maintenance can be considered as a particularly suitable option for the IoT platform further utilization by a distribution system operator.
When comparing the selected IoT platforms, data related to different features was collected by implementing four practical cases. The first case was monitoring air conditions at Vaasa primary substation using a Ruuvitag sensor. The second case was use CoreTec and CoreSense to import condition monitoring data from the power transformer at Purola primary substation. The third example was import measurement and status data from the DC system at Alskat primary substation to IoT platforms. In the final case, data was retrieved from MicroSCADA Historian to a comma separated value file and exported to IoT platforms using either the representational state transfer application programmable Interface (REST API) or a Python software development kit. The results of this study demonstrate that it is possible to install of IoT technology on significantly different platforms. M-Files was the IoT platform with largest amount of open questions still remaining. IoT-Ticket appeared to be the easiest option for installation and end use. If an organization were to choose Microsoft Azure, Amazon Web Services or Google Cloud Platform, they would need to find reliable partners to develop the platforms with end users.
During this study, it became evident that IoT technology is relatively evolved and organizations should begin using to use it with a low threshold if suitable applications are found. For example, predictive maintenance can be considered as a particularly suitable option for the IoT platform further utilization by a distribution system operator.