1 March 2007

True Power Generation

Advanced condition monitoring ensures hydroelectric plant runs efficiently

By Neil Gregory, Yvonne Power, Fouad Eddy Sarraf, and Sam Crisafulli

Deep in the land of the Kiwi, Meridian Energy is New Zealand's largest generator and produces approximately 30% of the country's total generation using hydro assets in Manapouri and the Waitaki Valley along the South Island.

Meridian's generation assets underpin New Zealand's electricity supply. Assets go across nine hydro (water) power stations located around the island. Meridian also owns and operates New Zealand's largest operational wind farm (Te Apiti), located on the North Island.

Because of the company's distributed nature, Meridian remotely manages its operation at a central control room located at Twizel, at the center of South Island. The control room focuses on real-time demands of New Zealand's electricity market. What it does not do, however, is indicate the deteriorating state or "health" of generating assets.

Some of the key assets at each station are approaching 30 to 40 years of operational life and are subject to changing operational and market demands. A condition-monitoring program has operated at Meridian for quite a while, but introducing a plant asset management system brings this program to a new level.

Safe and reliable power system operation is integral to maintaining a continuous supply of power. Reliability of the power supply directly links to the condition of the assets supplying the power, namely transformers, turbines, governors and generators.

When assets go off-line for maintenance or for poor operation, the systems may not meet the demand for power. Also, a poorly operating asset may have serious environmental consequences. Transformer failures can generate excessive amounts of heat, which could result in fire or explosion. When assets are new, relatively few things can go wrong. However, as they age, deterioration becomes more apparent. That is when a predictive asset maintenance program becomes key.

Predictive asset maintenance can predict when the asset is likely to fail. Because of the distributed nature of power generation, assets may be across vast distances with data coming in from a variety of sources. Measurements, tests, and on-line data are available, but these measurements have to undergo evaluation in an effective manner. The integration of data from a wide variety of sources is the basis of advanced condition monitoring.

Meridian proposition

The challenge of achieving asset management excellence at Meridian requires continuous improvement of plant processes and people. Challenges include:

• Availability and integrity of current and historical data
• Adoption of a consistent approach when assessing data
• Avoiding delays in data interpretation
• Using condition assessment information for proactive rather than for reactive planning
• Coping with demands on engineering expertise due to increasing number of major refurbishment projects and growth opportunities

The plan for Meridian addressed asset management and integration of existing systems so the energy provider could turn masses of data into actionable information.

Meridian's asset management system provides more than simple condition monitoring of core assets (transformers, turbines, governors, and generators). It provides a predictive monitoring capability for each of the assets. They could add additional assets to the system at any time.

Meridian's asset management  system should support analysis based on on-line and off-line data. An on-line analysis runs at a pre-scheduled interval configured within the control center, and an off-line analysis runs following user input into the maintenance management system. The data then routes to the asset management system where it performs complex calculations and/or classifies test results. Following the analysis of test results, an e-mail notification goes out alerting specific users of test result classification and provides a link to the corresponding page for visualization of analysis results.

Two key assets monitored within plant asset management  include transformers and hydroelectric turbines.

From a maintenance perspective, a transformer is an oil-filled tank containing an iron core (insulated with cellulose) that conducts a magnetic field separating two windings. Cellulose insulates the windings. A tap changing mechanism allows for voltage adjustment. Bushings are the means of connecting the windings internal to the transformer to the outside world and provide an insulation mechanism between the outside frame of the transformer and the live connections. Because the transformer contains oil (the oil removes heat and provides insulation), it attaches to an oil-filled tank. All of these components are subject to wear, aging, and deterioration.

When transformers are new, relatively few things go wrong. There are no moving parts that wear out. However, as transformers age, the insulation system starts to break down and can eventually fail.

Failure can be dangerous and can result in considerable heat generation within the transformer, which poses serious hazards to people and the environment.

Transformers and the associated equipment covered by the asset management system include load tap changers, bushings, and the transformer cooling system. By conducting a series of oil and electrical tests and by monitoring the thermal characteristics of the transformer, you can monitor the rate of deterioration and generate warning of abnormal operating conditions.

In contrast, hydroelectric turbines consist of stationary and rotary components, where hydraulic oil lubricates some of the parts. These components are subject to wear, excessive vibrations, thermal stresses, and water leakages. The monitoring of hydraulic turbines ranges from display of visual inspection results, to clearances, vibrations, temperatures, and turbine operation.

System functionality

The following describes the plant asset management system key functionality:

Data integration: The asset management system has integrated data from a variety of sources. Tests can also run using on-line data. Often factory test data and IEEE standards and limits are part of data analysis, calculations, and classifications. This stored data is available for use within asset management calculations.

The user can retrieve historical data for calculations and comparison between current and historical operation.

Diagnostics: Advanced condition monitoring encompasses numerous techniques depending on the information available. The asset management system integrates a variety of diagnostic techniques including engineering knowledge, rule-based methods, first principle model-based, and history-based methods.

Rule-based methods work within the asset management system when there are well-defined limits that indicate abnormal operation. For example, as part of transformer monitoring when oil analysis results are available, you can use the IEEE standards to indicate faults when reading acidity levels. Acidity values beyond the IEEE limit indicate abnormal transformer operation.

Forming a prediction based on the current acidity reading crossing the defined threshold, you can then predict days to oil refurbishment. This means transformer oil refurbishment can occur on the next scheduled maintenance rather than the transformer being taken off-line later to perform the refurbishment. Within the asset management system, it programs in the rules.

Model-based methods: These rely on a fundamental understanding of the process using first principle knowledge to develop mathematical relations in order to describe the modeled process. An example of this within the asset management system is the hot spot temperature calculation and calculation of relative thermal aging rate based on steady state transformer operation. This is a well-defined calculation within the IEEE standards (IEC354: 1991). Programs within the asset management system have these models.

History-based methods: These methods use large amounts of historical data in order to model the process. This includes techniques such as principal component analysis and partial least squares. Generally, these techniques apply when there is a need for variable reduction. An example of a history-based method under consideration for Phase 2 (turbines) of the asset management system is on-line monitoring of guide bearing vibrations.

The asset management system is for:

• Asset coordinators/plant managers in order to view any updates and alerts of what is changing as well as flag any possible work to be undertaken from an operational perspective
• Service providers from a maintenance and condition perspective
• Tactical engineering teams in order to investigate specific alerts as they arise and trending information in support of improved reliability
• Strategic engineering to assist with planning and updates to the asset management plan, i.e. maintenance may be deferred or brought forward as a result of improved knowledge of the asset condition
• Generation controllers in order to see alerts as they arise and know the general health of the sites
• Administrators in order to deal with model behavior and changes to functionality
• Management in order to view the overall site and asset health

Integral to the success of any condition monitoring system is clear results presentation and event notification. Visualization of analysis results occurred within asset management via the use of dashboard displays and the use of key performance indicators.

The Corporate Overview (Station) is the first screen personnel will see when they log onto the asset management system.

Its design focuses the user's attention on the worst performing station that reflects the worst performing asset at a particular station. By selecting a station, the user then can view a station overview screen that includes all assets at the selected station.

The transformer overview display summarizes the health of each transformer at a particular station.

Transformer crystal ball

On the screen, each transformer has a predicted "Days to Failure," which comes from certain key tests performed on the transformer. Each transformer also contains two horizontal bars that represent the "condition index" and "health %" for the transformer.

The condition index (CI) consists of three components:

1. Oil test results ("Oil")
2. Electrical test results ("Elect")
3. Outstanding tests count ("OutS")

The percentages for the oil and electrical test component of the CI come from using algorithms that examine each individual test result. Outstanding tests is a count of any enabled tests that have not occurred on a particular transformer or expired test results. The health % forms by averaging the oil and electrical tests components of the condition index to give an indication of total transformer health.

The custom dashboard provides powerful functionality that enables users to create their own specific view of assets in particular interest to them. For example, an engineer may want to observe/track the condition of a particular type of transformer at several locations or a single transformer at one station. Similarly, a plant manager may only want to see assets at the station they are responsible for.

Drilling down on individual test methods provides the user with a detailed test display. The detailed display provides a list of individual raw data values used within the analysis calculation and allows for trending of values. The display can show a customized output display that provides an indication of when the predicted value will cross the alarm threshold, which indicates days to failure.

Whenever an off-line analysis result becomes available or whenever an on-line analysis produces an exception or indication of an abnormal event, an alert goes out to the user via e-mail.

The  asset management system has the ability to retrieve maintenance and alert information for immediate access via the Web-based user interface. A user can access maintenance and alert information either at a station or unit level by selecting the alert or toolbox icon in these figures.

The worst performing transformer at Tekapo B is transformer 1097837 with a "Health %" of 43% and three "days to failure." A user can find these details on the corporate overview page.

Implementation evaluation

When implementing an asset management system, it is necessary to evaluate maintenance practices and instrumentation available at different stations.

Development issues include: Consistency in testing across all units/stations; testing procedure testing performed incorrectly with faulty testing instruments; instrumentation issues-instrumentation not commissioned, not displaying correct value (eg. oil temperature probe reading air temperature); accuracy and usefulness of current testing and measurements being recorded; integration of existing systems; and processes to achieve maximum benefits.

The asset management system is a decision support application that supports asset management by providing the visibility of plant conditions required to manage a range of critical assets.

Meridian successfully installed and commissioned the asset management system in January 2006 with the company already receiving benefits. Work is currently underway into turbine governor and generator monitoring. Future phases of the system include the addition of other critical plant assets.

About the authors

Neil Gregory is with Meridian Energy. Yvonne Power, Fouad Eddy Sarraf, and Sam Crisafulli are with system integrator Matrikon.