Unlocking asset infrastructure

 

As networks work harder, asset management planning needs to be smarter and data driven. For some network operators, there is a significant shift from traditional time-based maintenance towards condition-based regimes to cope with additional demand on heavily utilised assets.

 “With any condition-based system the challenge is acquiring the condition data in a timely way and establishing the trigger points for maintenance activities,” says Carl Ketley-Lowe, engineering policy manager at Western Power Distribution (WPD).

Part of this shift towards a condition-based system has seen WPD explore ways to reduce invasive maintenance tasks, thus reducing the working risk to on-site teams and the cost to customers.

“To reduce invasive tasks, we need to develop better inspection regimes and, where possible, collect good proxy data to inform maintenance. In many areas this has developed greatly over recent years with increased use of infra-red imagery and partial discharge sensors,” explains Ketley-Lowe. “In our larger transformers the insulating oil is their life blood and oil sample analysis has also improved, giving us a better picture of asset condition.”

For WPD, low voltage networks in the streets of towns and villages will also be required to work harder to support the additional demands of electric vehicles and heat pumps. As customers become increasingly dependent on electricity for day-to-day tasks, the challenge will be increasing network resilience to mitigate failure through overload or damage.

“We can meet this challenge with new monitoring solutions for these local networks which have been historically designed from template and modelling. Our monitoring solutions will be able to inform us when networks are working near capacity and also waveform analysis of the network can give early indication of some fault conditions, which allows us to rectify them before they develop into a loss of supply,” says Ketley-Lowe.

 

Low voltage issues

Historically, UK energy networks required asset managers to focus on higher voltages. But this is changing with the introduction of distributed generation and the electrification of heat and transport, says Andrew McHarrie, a member of the Institute of Asset Management (IAM) Panel of Experts.

“Asset managers now need to have greater visibility across the length of their networks to ensure the voltage is maintained within regulated limits, and the assets are not stressed beyond their thermal capacity,” adds McHarrie, who is also head of asset management and power system studies at EA Technology.

As energy networks become more active, there are new generators and new loads appearing at all voltages. While the size of each connection at lower voltages is smaller, there are far more of them. According to Paul Barnfather, head of electric vehicle infrastructure at EA Technology and also a member of the IAM Panel of Experts, these smaller connections are inherently less controllable and predictable – and the sheer number of them can become overwhelming.

“This means that asset management needs to become much, much smarter: using big data to identify trends and patterns from millions of assets and using machine learning and artificial intelligence (AI) to respond quickly to changes,” says Barnfather. “We need to ensure that our networks are fit for the future and improving our asset management will be a key part of that.”

 

Embracing innovation

New technologies offer a raft of opportunities for networks to improve the collection of asset condition data and trigger maintenance tasks when they are required. AI and analysis of condition points is increasingly used to give asset managers early indication of potential faults on the network.

An example of this is the work WPD is doing with Dynamic Ratings to fit condition monitoring equipment to some of its 132kV tapchangers. The equipment measures the operation of the tapchangers in real time and also collects more long-term data to help assess the duty that has been placed on the asset. The condition monitoring equipment records operational data on the unit but also checks for signals which could help identify a failure developing, such as an unexpected change to the temperature of the unit or a change to the operation of ancillary items.

“Some changes are within tolerance but others may fall outside and the system will use AI to work out the difference and prompt us so that we can take action and avoid the units deteriorating,” explains Ketley-Lowe.

As energy networks transition to a smarter, more flexible system, the availability of data that can confirm the condition and performance of assets is crucial. But the use of big data and innovation to improve asset management plans and pinpoint vital areas for investment is not the preserve of electricity networks.

Northern Gas Networks (NGN) is utilising new technologies and new ways of working to capture asset information that informs key decision making and allows the business to accurately plan asset management activities.

NGN owns and maintains around 1,700km of large diameter gas mains, stretching across Cumbria, Yorkshire and the North East – all of which require regular maintenance and assessment.

Traditionally, asset assessment and repairs typically require an excavation at each point of enquiry which can be expensive, disruptive and presents safety and environmental risks. The development of robotic platforms, camera systems and pipe access technologies are creating opportunities for networks to undertake complex activities with minimal disruption.

“While CCTV camera systems and robotic platforms have been increasingly available over recent years, these are typically expensive to deploy, and cost benefit assessments do not always make this a viable option,” admits Richard Hynes-Cooper, head of innovation at NGN.

Mitigating cost concerns, NGN is involved with an NIA-funded innovation project called System Two Access and Seal (Stass) that has allowed the company to embrace robotics to deliver speedier repairs and maintenance across its underground network.

 

New technologies

The company’s “robo-engineer” can travel up to 250m along the length of an underground gas pipe to carry out a repair. It is equipped with a camera that transmits live footage of a pipe’s condition and it can treat imperfections in a pipe by applying a “flexspray”.

Nicknamed Stan, the robot is being used on large diameter pipe projects, which can be particularly disruptive to motorists, and expensive to carry out. The company says it is currently using robotics on an average of two jobs every week, saving time and money, and reducing disruption for customers, by limiting the number and size of holes that need to be excavated.

Hynes-Cooper added: “By using the robot, we can reduce the number of holes we need to dig to carry out routine repairs and maintenance on our larger pipes. This is good news for motorists, as it means fewer roadworks, and good news for the environment, as we don’t need to dig as many holes.

“Stan will also help us keep customers’ bills affordable. Early indications are that we can save £2,000 per job, by getting the work done more quickly and efficiently.”

The robotic platform is delivered as an in-house service by NGN operators and allows the capture of asset data at a more economical rate – an increasingly useful tool as the network seeks to become more resilient to future demand.

“As we move to a smarter, more flexible energy system, the availability of data that can confirm the condition and performance of assets is crucial,” says Hynes-Cooper. Indeed, any change on network demand, transportation requirements and whole-system solutions must be underpinned by accurate data and a comprehensive understanding of asset condition.

As the industry looks to the smarter, more flexible energy system of the future, there remains the consistent objective to improve network resilience while simultaneously driving down costs.

“Both of these are possible,” insists Barnfather. “But it will require a shift in mindset away from centralised planning around assumed scenarios and a move towards more dynamic, data-driven decision making.”

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