Transmission & Distribution Technology
As the velocity and variety of data becomes available through advancements in sensor technology to monitor just about anything, machine learning is being applied to efficiently manage increasingly large and fast-moving data sets. Previously, organisations with predictive analytics could use big data (current and historic) to try and predict future events with reasonable results. What it brings is a more accurate prediction using algorithmic models to deliver insight faster. Machine learning can handle large and complex information, from sensors, mobile devices and computer networks, to discover hidden patterns or trends in the data. It can then learn these patterns and apply it to new, real-time data to detect similar patterns in the future. An example would be to model the performance of a piece of equipment, such as an overhead line, in relation to the temperature of its surroundings. Machine learning can be taught to see what normal and abnormal behaviour looks like, and by applying the model to current data, it can identify events, such as how, for example, internal and outside environmental temperatures will affect sag on the line. Te system can then predict, from existing knowledge, that something isn’t right and send out notifications, and prescribe actions. Te more data that is analysed, the more accurate the predictive model. Part of the implementation process is understanding how it works and the number of techniques involved. Your software service provider or machine learning expert will recommend what techniques to use and when. Te most common techniques are:
T
he industrial world is awash with data and new information from sensors, applications, equipment and people. But the data
is worthless if it is left untouched or not used to its full potential to gain insights and make improved decisions. To make the most of big data, utility leaders should implement machine learning alongside the Industrial Internet of Tings (IIoT) and use 3D visualisation to take advantage of the increased insight they can bring to the operation regarding performance and reliability. Applied together and working in
tandem, they can reap the rewards of cost savings and improved uptime. Te smart grid is already transforming the industry, but with machine learning and reality models exploiting the IIoT, the smart grid has the potential to be even smarter.
Demystifying machine learning We have all experienced some form of machine learning, from streaming movie recommendations to banks that monitor spending patterns to detect fraudulent activity. Now, the industrial arena is moving quickly toward using a type of artificial intelligence to leverage the IIoT.
l Supervised machine learning. Te program is trained on a pre-defined set of ‘test’ data comprised of historical or similar data to the real thing, which then facilitates its ability to reach an accurate conclusion when given new data.
l Unsupervised machine learning. Te program is given a mix of data and must find patterns and relationships therein with no training whatsoever, without any specific target or outcome.
So, what it comes down to is knowing what it is that you want your data to tell
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