Transmission & Distribution Technology
3D model of an electrical power station
you and understanding what sort of data you have available. Unlike business intelligence and
predictive analytics methods that require a signifi cant amount of manual labour and time, machine learning automatically produces insights at a consistent and accurate rate. It can then apply the learning to new, real-time data for future predictions for easier and more reliable decision making. T e continuous delivery of reliable and stable electrical power is paramount to utility companies. While users who operate on a 24/7 basis rely on a constant and an uninterrupted supply, it is imperative utility companies take every precaution necessary to reduce outages and downtime. In the electric utility industry, the ability to recognise equipment failure and avoid unplanned downtime, repair costs and potential environmental damage is critical to success across all areas of the business, as it directly aff ects the customer. T is is even more relevant in today’s turbulent times aff ected by ageing assets, energy demand and higher costs. But, with machine learning, there are numerous opportunities to improve the situation. Some of the main forms of predictive analysis machine learning can deliver to the electric utility industry are detailed below.
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www.engineerlive.com One of the most applicable areas
where machine learning can be applied within the utility sector is predictive maintenance. Predictive maintenance is the failure inspection strategy that uses data and models to predict when an asset or piece of equipment will fail so that maintenance can be planned well ahead of time to minimise disruption. Predictive maintenance can cover a large area of topics, from failure prediction, failure diagnosis, to recommending mitigation or maintenance actions after failure. T e best maintenance is advanced forms of proactive condition-based maintenance. With the combination of machine learning and maintenance applications leveraging IIoT data to deliver more accurate estimates of equipment failure, the range of positive outcomes and reductions in costs, downtime, and risk are worth the investment. Extending the life of an asset can be a
low-cost alternative to capital replacement. With many utility assets nearing end of life, asset health indices can provide a safe and reliable solution to extend asset life as well as satisfy regulatory demands for proof of compliance and justify rate cases/ budgets. Machine learning can improve asset health indexing methodologies empowering utilities to make defensible
asset investment decisions. Even with a limited budget, asset health indexing software, such as that off ered by Bentley Systems, is being leveraged to automate the analysis and ensure sustainability of the process.
The issue of imaging Video and image interpretation is another issue. T ermal imaging has become a core predictive maintenance tool in any ongoing inspection programme. It is widely used for substation surveys and safety checks before planned maintenance work. T is helps avoid costly service interruptions and equipment losses. Machine learning can be applied here to spot the patterns of the images of what a healthy piece of equipment looks like by identifying hotspots across transformers and transmission lines, therefore speeding up the time process. Demand forecasting should also be
considered when discussing machine learning. Accurately forecasting high levels of demand, such as within a utility service, gives a company a competitive advantage. It provides them with the information they need to meet customer demand by anticipating future demand or consumption. In the energy sector, storing energy is not cost-eff ective,
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