Most of the offerings that are on the market with regards to measuring conduction in the network stations, collect and transmit data without doing any kind of processing on the edge. This entails a transaction cost (LTE / 5G subscription) in cases where one does not have their own fiber connected network that can be used as a data carrier. As stated, before this kind of cost, could mean that one would not collect a large enough and accurate dataset that can be used for AI / ML analytics. In order to be able to start with analytic, predictions and machine learning, companies must have access to both real-time data that has a high sampling rate and historical data.
Today’s solutions, where you simply pass on data without doing any form of edge analytics, also entail a certain risk that the data may be lost if the data carrier used goes down. With edge computing nodes, one is able to store all the acquired data and, in the event of a data carrier outage, no data is lost. The edge nodes will simply resend the collected data when the data link is up and running again or over an alternative data carrier if available.
If one chooses to install edge computing nodes out in the network, one will introduce a hardware that is capable of capturing sensor and measurement data with as high sampling rate the sensors can offer. The nodes can be used to store and process the acquired data, so that results and/or alarms can be sent to a central solution. Metadata can be shared with third parties while maintaining a secure infrastructure.
Another benefit of processing the data where it resides is that it will also result in a reduction of data carrier cost, due to the fact that only the metadata is forwarded. The high sampling rate that was made possible by the edge computing nodes will have a follow-on effect of high quality and accurate data.
Having access to a large, accurate and high-quality data set, will be crucial when providing this data to customers or third-party vendors for further analytics.
With the introduction of edge computing nodes in substations, one will achieve access to high sampling rates from sensors, which can include: voltage, current, machine sound, other sensor data such as moisture and humidity, ambient temperature, transformer temperature, etc. Edge computing nodes will offer the ability to move AI and ML models from the cloud to the edge for analysis and “automatic “decision making. Rules and alarms can be controlled from the edge nodes directly.
On the grounds of the collected sensory data, one can start the process of moving from inefficient, periodic maintenance over to real-time, condition-based maintenance for the next generation of smart utility grids.