Europe, Vattenfall

From power plant maintenance to asset efficiency optimization

Issue 10 and Volume 18.

By holistically applying new condition monitoring techniques in asset efficiency optimization programmes, operators can build comprehensive risk models and achieve dramatic improvements in both the performance and the cost-effectiveness of their plant maintenance strategies.

Phil Burge, SKF, UK 

Most utilities spend too much on maintaining their assets, although maintenance investment plays a critical role in performance, availability and cost-effectiveness over the lifetime of a plant.

The sheer scale and complexity of plants, however, creates challenges for those charged with determining the right maintenance strategy. When does the risk of equipment failure make a preventative intervention desirable? When should the next large-scale maintenance turnaround be scheduled? Exactly what should be its scope?

As maintenance techniques and technologies have evolved, operators have become much more sophisticated in their planning and decision-making processes. Simply responding to equipment failures has given way to preventative maintenance strategies.

Condition monitoring techniques have become increasingly sophisticated and cost-effective, while the knowledge required to interpret and make decisions based on plant condition data has grown too, allowing condition-based strategies to be implemented for much critical equipment.

TOWARDS THE NEXT STEPS IN MAINTENANCE PERFORMANCE

These technological advances have transformed operators’ ability to plan and manage maintenance interventions for individual pieces of equipment, but by themselves they do little to assist in the challenge of allocating finite maintenance resources to thousands of items across a plant or network.

Today, a few companies are taking the next step in maintenance performance. They are building increasingly comprehensive models of asset risks and the costs associated with different maintenance options and integrating these models with their enterprise asset management systems. They are feeding these models with increasingly accurate data on plant condition and performance.

Based on this data, firms are making holistic maintenance choices, assisted by sophisticated decision support tools. Companies are also developing their organizations and capabilities in order to continuously improve the reliability of their assets, their understanding of plant condition and the quality of their maintenance interventions. In total, this approach is known as asset efficiency optimization (AEO).

Cost and risk

Plants using the AEO approach begin by building a comprehensive picture of the risks associated with equipment failure or underperformance. These risks can include operator safety hazards, damage to equipment or facilities, violation of environmental limits, plant stoppage or start-up delay or significant reduction in thermal or electrical performance.

Correctly allocating maintenance resources according to risk is the foundation of any effective maintenance strategy, but it can be a complex and lengthy task. Operators can build their own risk models using their engineering experience and historical data on plant performance, or they may choose to take advantage of external resources to audit their equipment and identify critical risks based on deep knowledge of equipment performance and likely failure modes.

An operator taking measurements with the SKFS109 machine condition advisor Source: SKF

Once equipment risks have been described, appropriate mitigation strategies can be determined. Here, engineers choose from the modern armoury of on- and off-line condition monitoring solutions, including vibration measurement, oil analysis and thermal imaging techniques. They will, of course, supplement these approaches with more traditional techniques including scheduled visual inspections and periodic preventive interventions.

Once again, companies can draw upon comprehensive industry knowledge to greatly simplify this process. For example, SKF reliability consultants frequently provide pre-populated templates that allow companies to import tasks, frequencies, job plans and even bills of materials directly into their own computerized maintenance management systems (CMMS).

Information on the run

Maintenance requirements are, by their nature, highly dynamic. Even the most sophisticated monitoring systems cannot eliminate the unpredictable from equipment operation. But they can ensure operators have an early, clear indication when intervention may be required and the best possible guidance on what steps to take.

Key recent advances in this area include decision-support tools that codify and standardize analysis and response to maintenance information. Systems like the SKF @ptitude Decision Support System provide staff with a quick visual overview of overall plant condition, together with the information they require to act in a consistent and proactive way in the event of a fault.

Modern decision support systems can monitor thousands of channels of on-line condition information, supplemented by off-line data from portable monitoring tools or scheduled inspections. When key variables exceed pre-set limits, these tools can alert operators, specifying immediate intervention if required, or automatically modifying inspection and monitoring schedules when the risk of failure is increased.

Where data indicates a problem, but no action is immediately required, the decision support system can simply flag the issue, allowing appropriate action to be incorporated into the next scheduled maintenance event.

Evolving reliability

As power generation assets age and equipment changes, so too do equipment risks and maintenance requirements. Central to the AEO approach is recognition that maintenance tools and strategies must evolve to meet changing needs.

Companies facilitate this evolution by training their staff – both dedicated maintenance personnel and operators – in the skills they need to constantly review and improve maintenance processes. For maintenance personnel, for example, this will include root-cause problem solving techniques.

When a piece of equipment fails, or requires early intervention to prevent failure, maintenance staff will as a matter of course study the item to find the root cause of the problem and see if changes to equipment or operating procedures could be introduced to prevent recurrence of the same issue. For plant operators, AEO requires active involvement in maintenance activities that may go well beyond their traditional experience. For example, operators may receive training in recording equipment condition and in regular preventative activities. Incentive processes are typically adjusted as well, so that operators are encouraged to maximize equipment reliability as well as overall productivity.

AEO in practice

Taking an integrated approach to plant maintenance is already paying dividends for pioneering utilities. One utility introduced the approach after it began to experience reliability and performance problems following several years of cost-cutting efforts. Working with SKF, the company’s engineers conducted a comprehensive analysis of its systems across 27 coal and gas fired operating units at 15 power plants.

Following the analysis, the company developed a totally new maintenance strategy that focused planned maintenance on critical equipment and dominant failure mechanisms. It increased the emphasis on condition-based tasks and was able to eliminate many unnecessary routine tasks and reduce the scope of scheduled shutdowns.

The introduction of the programme helped the company to reduce its equivalent forced outage rate (EFOR) by 30 per cent and increase peak period reliability by 7 per cent. The new approach helped to smooth maintenance activity too. High priority corrective work dropped by 30 to 40 per cent in the months following the programme’s introduction and overall maintenance costs went down.

Asset Efficiency Optimization is an SKF work management process with four key facets Source: SKF

At a North American utility, the AEO approach was applied to standardize and consolidate maintenance activities over nine coal fired units at four plants. The company introduced a central predictive maintenance centre, which received condition data from all nine units. Analysts at the central unit used the @ptitude decision support tool to manage the data, using a common set of maintenance polices.

The system has not only enabled the identification of previously undetected faults. It has also allowed knowledge and best practices to be shared between units, helping all to improve their maintenance performance.

A pearl of power

Hydroelectric power plants supply almost 50 per cent of Sweden’s energy needs. The country’s largest hydro plant is Vattenfall’s Harsprånget plant situated on the Lule river about 30 km north of Jokkmokk in northern Sweden.

Vattenfall is Sweden’s state-owned power company, with sales in 2007 of €15.2 billion ($22.2 billion). Including its operations in Finland, Poland, Denmark and Germany, Vattenfall produced a total of 167.6 TWh in 2007, which includes all forms of power generation.

In Sweden, 30.8 TWh are produced by hydroelectric power plants, of which 16 per cent is produced by the 15 Vattenfall hydroelectric power plants that dot the 460 km-long Lule river.

The Harsprånget plant has a long history. Development of Harsprånget, which today generates 2 TWh per year and has an output of 970 MW, was initiated at the beginning of the 1920s but was discontinued, as were several other power plant projects, during the recession that followed World War I. Construction resumed in 1945, and the first generator was operational in 1951.

The biggest turbine/generator aggregate at Harsprånget, also known as ‘Gerhard’, can handle almost 500 m3 of water per second, making it the biggest and most powerful turbine/generator in Sweden. It was commissioned in 1980.

The turbine has a diameter of almost 17 metres, and the rotating weight of the axle and rotors combined is more than 1000 tonnes. The generator’s power output of 475 MW is equal to that of the Ringhals nuclear power station, which is also owned by Vattenfall.

While a few maintenance workers visit the plant every day, Harsprånget is essentially run unmanned and is controlled from Vattenfall’s central control centre in Vuollerim, 100 km from the site.

During a planned maintenance stoppage in late 2007, Vattenfall decided to install SKF on-line conditioning monitoring equipment on two of its turbine/generator aggregate systems at the Harsprånget plant.

One of them was the big ‘Gerhard’ turbine, on which two SKF on-line condition monitoring units – a MasCon 48 and a MasCon 16 – were installed. There was an identical installation on the plant’s smaller 190 MW turbine.

Vattenfall had previously installed the SKF system at four nearby sites in the Lule river basin, so the choice was obvious. The installation has been designed to link directly to Vattenfall’s Lule River central control facility in Vuollerim.

“The SKF project team worked in close co-operation with representatives from Vattenfall to develop a suitable sensor configuration for the different turbines,” says Hans Steding, business manager for SKF Nordic Region’s Service Division. “Low-frequency accelerometers together with displacement sensors were installed in order to monitor turbine imbalances and wear in the plain bearings.”

All data is collected and stored in an SKF @ptitude Observer system that in turn is linked via an OPC server to Vattenfall’s own control system. The benefit for Vattenfall’s Harsprånget power plant is the continuous surveillance of vibration levels as well as the accompanying analysis software.

“SKF has strong expertise in this area,” says Göran Öhlund, project manager at Vattenfall Power Consultant AB. “This is a very valuable tool. If there is a problem, this system will tell us in advance.”

As the technologies needed to measure equipment performance and predict likely failures become ever more sophisticated, the next challenge for utilities and plant operators will be in ensuring that they aim their preventive firepower accurately, targeting the most significant risks to their business performance, while keeping maintenance costs firmly under control. The asset efficiency optimization approach gives them a structured comprehensive way to achieve that goal.

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