Resilience and reliability are more important than ever in the power generation and utility sector. As organizations look to digital transformation to reduce risk in today, they’re discovering the benefits predictive analytics can bring to their operations, says David Thomason, Industry Principal – Power Generation at AVEVA (LON: AVV ).
Power generation companies are facing a growing number of challenges, from increased market complexity and demand, through to regulatory compliance, sustainability objectives and a rise in uncertainty spurred by Covid-19.
The pandemic caused the industry to accelerate remote working, and to deal with maintenance gaps created by supply chain disruptions. This highlighted the need for operational resiliency and agility in order to ensure the delivery of power, and Gartner has reported that resilient delivery is one of 2021’s top utility trends due to the industry’s underlying belief that this volatility will continue.
Power plants are becoming ever more digital, and the combination of assets with connected devices – and most importantly the data captured from these assets – supports the sector’s growing focus on resilience, agility and reliability.
According to Gartner, 50% of utilities will have progressed their use of internet of things (IoT) technology to build dynamic capabilities that optimize processes and improve decision making by 2024. In addition, 40% of new monitoring and control systems in this sector will be using IoT to enable intelligent operations by 2025.
The benefits of digital transformation
The use of artificial intelligence (AI) and machine learning (ML) enables organizations to have full visibility of operations, and create insights that can help overcome some of the sector’s most disruptive challenges.
The amount of big data produced by power generation companies means that forward-thinking businesses are investing in monitoring and predictive analytics tools that help leverage this data to its full capacity. Navigant Research, for example, estimated that almost $50bn will have been spent on asset management and grid monitoring technologies by 2023.
By supporting agility, organizations can more quickly respond to change. Predictive maintenance allows the power industry to identify malfunctions before they happen, ensuring the reliability of their operations. This better positions them for growth in the uncertain times ahead.
What does predictive analytics offer?
Predictive analytics enables operations and maintenance personnel to be more proactive in their work. In addition, the reliability and performance of assets are improved through early warning notifications and diagnosis of equipment problems days, weeks or months before failure.
It can even forecast the remaining useful life of assets to help provide deep insights into operations and maintenance risk.
Using predictive analytics, companies are able to implement asset strategies designed to avoid unplanned downtime for their most critical assets, while also deciding which preventative or corrective asset strategy is the best option for less vital equipment.
But benefits go far beyond optimizing maintenance schedules to ensure reliability of operations. As risk assessment becomes more exact, prioritization of capital and operational expenditures can be optimized, and companies can also realize financial savings by avoiding costs related to loss of power and/or productivity, replacement equipment and additional man hours accrued when a fault occurs.
Tangible business benefits
A great example of the benefits of predictive analytics in the power sector comes from EDF (PA: EDF ). The French utility company uses predictive analytics for fleet-wide asset monitoring (coal, gas, renewable – wind and solar – and the world’s largest fleet of nuclear assets), and to check equipment health and performance and identify failures before they occur.
To date this has helped the company to not only optimize power production, which in turn improves energy security, but also ensure resiliency and continued sustainable delivery of critical services by avoiding downtime. For example, just one single early warning catch saved the company an estimated €1.5m.
Knowledge capture and transfer
Knowledge capture and transfer is another key benefit of predictive analytics, an area of huge importance to a sector which is seeing many of its experienced staff reaching retirement age.
Accumulated knowledge stays available to new staff as they join the business, ensuring best practices, operating procedures and maintenance processes are passed on to the next generation, again reducing risk and therefore improving reliability.
Remain competitive in today’s changeable times
The power generation and utility sector is grappling with a world that’s more volatile and complex, but demands greater speed, agility and resilience.
In response it’s undergoing a digital transformation that enhances the way power is produced and delivered.
Predictive analytics has a key role in this transformation, as it enables organizations to become more resilient, reliable and efficient by moving from a reactive to a proactive way of working.
As more power generation companies embrace this technology, we’ll see risk reduced, crisis response sped up and resiliency improved – in turn helping organizations remain competitive and profitable in our more uncertain ‘new normal’.
To understand how AVEVA Predictive Analytics benefits power generation companies, please click here.
For India can replace EDF with the following:
Tata Power turned to predictive analytics as a means of avoiding asset failures and reducing downtime by monitoring the health and performance of equipment – fleet wide – in real-time.
By moving from reactive to proactive maintenance, the company was able to receive early warnings of potential faults – on one occasion saving an estimated $270,00, improve equipment reliability and performance, and capture knowledge of equipment failure modes.
For China can replace EDF with the following:
Datang Power turned to predictive analytics as a way to lower costs of production while adjusting to fluctuating market conditions. By optimizing assets and performance it was able realize significant cost savings, improve reliability and maximize return on investment of assets by balancing the availability and use of equipment, energy, fuel optimization and improvement.
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