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172:(Supervisory Control and Data Acquisition) system, by interpreting turbine performance as its capability to generate power under dynamic environmental conditions. Wind speed, wind direction, pitch angle and othera parameters are first selected as input. Then two key parameters in characterizing wind power generation, wind speed and actual power output, collected while turbine is known to work under nominal healthy condition are used to establish a baseline model. When real-time data arrives, same parameters are used to model current performance. GHE is obtained by computing the distance between the new data and its baseline model.
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Micro-Electro-Mechanical
Systems (MEMS) sensors provide more detail, as they have the ability to measure frequencies all the way down to 0 Hz (also referred to as DC offset). This capability enables these advanced sensors to identify critical frequencies related to the input shaft ('1P') and blade passing ('3P'), both of which often fall below 1 Hz and remain undetected by traditional CMS technologies.
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Early small-scale wind turbines were relatively simple and typically fitted with minimal instrumentation required to control the turbine. There was little design focus on ensuring long-term operation for the relatively infantile technology. The main faults resulting in turbine downtime are typically
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technology. Wind
Turbines are typically designed to reach a 20-year life, however, due to the complex loading and environment in which they operate wind turbines rarely operate to that age without significant repairs and extensive maintenance during that period. In order to improve the management of
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Leveraging cutting-edge technology, this advanced monitoring solution excels in tracking vibration trends, including those caused by ice formation on turbine blades. When a rotor experiences imbalance, the system is designed to note any increase in the 1P rotor frequency. Mass imbalance can arise
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There has been rapid development of wind turbine technology. As turbines have grown in capacity, complexity and cost, there have been significant improvements in the sophistication of instrumentation installed on wind turbines which has enabled more effective prognostic systems on newer wind
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Traditional content management systems (CMS) typically rely on piezoelectric vibration sensors for gearbox monitoring tasks. While these sensors are capable of capturing adequate data, they fall short when it comes to detecting low-frequency phenomena, such as rotor imbalance. This is where
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and a variety of other electro-mechanical drives. Due to the scale of some mechanical systems and the remoteness of some sites, wind turbine repairs can be prohibitively expensive and difficult to co-ordinate resulting in long periods of downtime and lost production.
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By trending the GHE over time, performance prediction can be made when unit revenue will drop below a predetermined break-even threshold. Maintenance should be triggered and directed to components with low LDE values. LDE is computed based on measurements from
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and other industrial applications. As the cost of repairing wind turbines has increased as designs have grown more complex it is expected that the Wind
Turbine industry will adopt a number of prognostic methods and economic models from these industries such a
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from a variety of issues, such as the non-uniform build-up of dirt or ice, presence of moisture, or damage. Additionally, aerodynamic imbalance may occur due to inaccuracies within individual blade profiles, physical damage, or errors in pitch calibration.
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For more complex designs, with complex drivetrain and lubrication systems, a number of studies have demonstrated the value of
Vibration monitoring and Oil monitoring prognostic systems. These are now widely commercially available.
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As typical wind turbine capacity is expected to reach over 15MW is coming years combined with the inaccessibility of
Offshore wind farms, the use prognostic method is expected to become even more prevalent within the industry.
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is also used by collecting and analyzing massive amounts of data such as vibration, temperature, power and others from thousands of wind turbines several times per second to predict and prevent failures.
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Most wind turbines are fitted with a range of instrumentation by the manufacturer. However this is typically limited to parameters required for turbine operation, environmental conditions and
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GarcĂa Márquez, Fausto Pedro; Tobias, Andrew Mark; Pinar PĂ©rez, JesĂşs MarĂa; Papaelias, Mayorkinos (2012-10-01). "Condition monitoring of wind turbines: Techniques and methods".
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Raw parameters and derived health indicators are typically trended over time. Due to the nature of drivetrain faults, these are typically analysed in the
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temperatures. This SCADA based turbine prognostics approach is the most economical approach for more rudimentary wind turbine designs.
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Wind
Turbine prognostics is also referred to as Asset Health Management, Condition Monitoring or Condition Management.
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Prognostic methods that enable preventative maintenance have been common place in some industries for decades such as
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391:"The unique advantages of ecoCMS – cutting edge sensor technology enabling real advances in condition monitoring"
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and lost production. This is achieved through the use of prognostic monitoring/management systems.
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Typical Wind
Turbine architecture consists of a variety of complex systems such as multi stage
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The growing demand for renewable energy has resulted in global adoption and rapid expansion of
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similar systems on existing wind turbines in order to manage aging assets effectively.
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The methods for wind turbine prognostics can broadly be grouped into two categories:
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systems, this is typically processed and communicated to ground based or
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as opposed to scheduled and reactive maintenance to reduce
244:"Why wind-turbine gearboxes fail to hit the 20-year mark"
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turbines. In response, there has been a growing trend of
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29:wind farms there is an increasing move towards
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335:"Wind Turbine Condition Monitoring Methods"
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248:Windpower Engineering & Development
20:Early small scale onshore Wind Turbines
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292:"Wind Turbine Failures Encyclopedia"
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212:References
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