Reliawind Project

ReliaWind project: “Reliability focused research on optimizing Wind Energy systems design, operation and maintenance: Tools, proof of concepts, guidelines & methodologies for a new generation.”

Project reference: 212966
Funded under: FP7-ENERGY

http://cordis.europa.eu/project/rcn/88411_en.html

http://cordis.europa.eu/result/rcn/55560_it.html

Reliawind Project PARTNERS

ReliaWind Consortium

The Case

Advantages of offshore wind farms

Reliability challenges for offshore wind farms

ReliaWind Project Consortium

ReliaWind project is the first European-wide program that brings together major stakeholders of the wind energy value chain to develop tools, models, and design guidelines for the next generation of wind turbines. First convening in March 2007 to examine renewable energy, the European Union (EU) Council of Ministers agreed that “renewable energy will cover at least 20 percent of the EU’s energy demand by 2020.”

Believing that wind power can make the most important contribution to meeting this particular target–as well as helping to meet 2020 targets for both improving energy efficiency and cutting carbon dioxide emissions by 20 percent–the EU Council of Ministers tasked a consortium of 10 industry and academic leaders with conducting reliability-focused research to optimize wind turbine design, operation, and maintenance. In their efforts to develop superior, next-generation solutions, consortium members used PTC Windchill Quality Solutions to analyze reliability and maintainability data from current wind turbines to determine how to improve future system designs.

The Case

For renewable energy to cover at least 20 percent of the EU’s energy demand by 2020, developing offshore wind farms is imperative. However, variable weather, extreme load cases, marine air, salt water, and difficult access all greatly increase the risks associated with installing, operating, and maintaining wind turbines offshore. To make investing in offshore wind farms more attractive, the EU Council of Ministers recognized that optimizing the overall reliability and maintainability of wind turbines was essential.

This optimization of reliability and maintainability is no simple task. Deploying wind turbines offshore requires the implementation of advanced corrosion protection technologies and the placement of electrical units in environmentally controlled sections of the turbine. Additionally, maintenance strategies for offshore service and repair actions not only need to reduce repair times but also improve access methods, making them less sensitive to wind and wave conditions.

In a drive to “Design for Reliability,” the EU Council of Ministers formed the ReliaWind project consortium, giving the 10 participating organizations a contribution of 5.5 million Euros and three years to improve the way in which wind turbines are designed, built, and maintained. With a total budget of 7.7 million Euros, ReliaWind was charged with developing and delivering reliability models specific to wind turbines to all stakeholders in the wind energy sector.

In addition to training stakeholders on how to use these models to apply a reliability-minded approach to development activities, ReliaWind project was charged with educating other organizations about their findings through conferences, workshops, websites, and media initiatives in the hope that this research will impact new turbine construction from 2015 onward.

Advantages of offshore wind farms

Offshore wind farms offer many advantages over land-based wind farms, including:

A greater variety of offshore wind patterns, creating more consistent wind turbulence for improved turbine efficiency
A tendency of increased offshore winds during the day, yielding more power during peak demand
An ability to develop offshore farms near large population grids, requiring shorter lines to transmit power to urban areas
A capacity to provide opportunities for wind power generation to EU countries unable to support land-based wind farms
A reduction of the environmental impact on bird species

Reliability challenges for offshore wind farms

While offshore farms offer several essential advantages, they present numerous reliability challenges, including the need for the following:

More durable blades, masts, and other components due to stronger wind and weather conditions
Reducing the number of overall components to simplify the design to a minimal of highly reliable components
A modular design to facilitate interchange of faulty components
Rigorous anti-corrosion and water sealing technologies to protect both surface and interior parts
Automation of as much preventive maintenance as possible to increase service intervals due to difficulty and cost of access

The Goals

ReliaWind’s chief goal was to evolve the wind power sector by making deployments of offshore wind farms comparable in costs to deployments of onshore farms. While one or more failures annually is common for an onshore turbine, this level of unreliability is unacceptable offshore, where costs associated with downtime and repairs are significantly higher. For offshore wind farm development to attract investors, the operational availability must be higher than 97 percent.

To achieve this goal, ReliaWind set several ambitious quantitative reliability objectives for onshore and offshore turbines:

Improve MTBF (mean time between failures) 10 percent for onshore turbines and 20 percent for offshore turbines
Reduce MTTR (mean time to repair) 20 percent for onshore turbines and 50 percent for offshore turbines
Boost operational availability from 97-98 percent to 98-99 percent for onshore turbines and from 85-90 percent to 97-98 percent for offshore turbines
Reduce CoE (cost of energy) to less than 0.04 Euros per kilowatt-hour

The Reliawind Approach

To better understand wind turbine reliability and to positively impact future designs, ReliaWind project established the following integrated approach for analyzing current systems.

Gather failure and maintenance data for existing turbines from manufacturers and suppliers and then standardize this data. Using PTC Windchill Prediction, define the system hierarchy and perform reliability predictions at the system, subsystem, and component levels to determine which items have the highest failure rates.
Using PTC Windchill OpSim, construct an RBD (reliability block diagram) and integrate collected data to calculate metrics such as availability, unavailability, MTBF, failure rate, expected number of failures, mean unavailability, total downtime, failure frequency, and hazard rate.
Using PTC Windchill FMEA, identify failure modes, causes, and effects and evaluate their potential impact on the system, determining how to eliminate or mitigate unacceptable effects based on criticality rankings.

The Results

Data collection and standardization

What is it? Analysis results are only as good as the data on which they are based. Because previous availability work for wind turbines was limited to high-level data of questionable quality, gathering and preparing good failure and maintenance data from turbines in the field was the first priority.
How was it used? Databases, fault logs, manual records, work orders, and monthly operation reports obtained from wind farms and turbine component manufacturers were reviewed by ReliaWind, who then developed a standard turbine taxonomy and a common data structure format.
What did it reveal? After creating and populating the database, ReliaWind had valid and usable field data from manufacturers for over 250 wind farms worldwide, with each wind farm operating from at least one year to as many as 15 years. Based on failure data for more than 290 turbines, ReliaWind defined a failure as the stoppage of the turbine for one or more hours, requiring at least a manual restart to return it to operation. With high-quality data in hand, ReliaWind could begin using PTC Windchill Quality Solutions to analyze and better understand turbine reliability.

PTC Windchill Prediction

What is it? A reliability prediction, one of the most common forms of reliability analysis, estimates the rates at which parts or components fail. These failure rates are generally based on calculated results from globally accepted standards, such as MIL-HDBK-217, Telcordia (previously Bellcore), and IEC TR 62380. Each of these standards supplies equations, or failure rate models, to compute component failure rates based on values supplied for stresses, part quality, temperature, and other environmental factors. Overall system failure rate is the sum of all component failure rates.
How was it used? For two generic wind turbine configurations, ReliaWind integrated 12 separate subsystems into system definitions and then took advantage of PTC Windchill Prediction’s unique ability to mix calculation models from various standards–along with field failure data for identical and similar components, supplier data, and various failure data handbooks for non-electronic and mechanical parts–to calculate estimated failure rates.
What did it reveal? For these configurations, the subsystems with the highest failure rates were the rotor module, pitch system, power module, and nacelle module. With this information, ReliaWind began investigating how to change these subsystems to increase their reliability. Possibilities included using more reliable components, applying new technologies or processes, and eliminating single failure points, which result in total system failure.

PTC Windchill OpSim (Optimization and Simulation)

What is it? A reliability block diagram (RBD) is a visual representation of a complex system that is analyzed using sophisticated mathematical algorithms to reveal comprehensive reliability and maintainability metrics. While reliability predictions assume that all components are configured in series, RBDs can consider fault-tolerance mechanisms, such as redundancies and backup systems.
How was it used? The ease of adding parallel and series redundancies and backup systems in PTC Windchill OpSim allowed ReliaWind to perform trade-off studies, assessing whether such system design changes would impact operational availability enough to warrant additional component and maintenance costs and complexities.
What did it reveal? Deriving results for each month in one year (8760 operating hours), ReliaWind constructed an RBD with blocks in series to verify that the system failure rate for the turbine is constant with time. Availability reached a steady-state after a period that was four times the MTTR (mean time to repair), which is in accordance with the analytical definition of the availability function. Using PTC Windchill OpSim’s advanced simulation and optimization techniques, ReliaWind analyzed complex system scenarios, such as the use of redundant systems and spare components. The ability to predict future turbine conditions allowed them to evaluate how more efficient and proactive maintenance planning and resource scheduling would reduce operation and maintenance costs and increase turbine availability.

PTC Windchill FMEA

What is it? A FMEA (failure mode and effects analysis) is a bottom-up approach to analyzing system design and performance at a particular system level. It involves identifying all potential failure modes, determining the end effect of each mode, and assessing the risk of each effect to eliminate or mitigate those that are unacceptable.
How was it used? Constructing a piece-part FMEA, which starts from the component level and considers mode criticality, ReliaWind used PTC Windchill FMEA along with MIL-STD-1629–a long-recognized standard employed worldwide by government, military, and commercial organizations to calculate mode criticality. Ranking modes allowed ReliaWind to concentrate on eliminating or mitigating the unacceptable effects for those modes that most impacted the operational availability of the turbine.
What did it reveal? After distributing the 81 modes that were identified into a criticality matrix based on each mode’ probability of occurrence and severity classification, ReliaWind could easily determine which posed the highest risks, allowing them to focus on establishing the corrective actions needed to eliminate or reduce their occurrence. Actions included finding failure detection methods and possible compensating provisions for catastrophic and critical modes to maximize reliability, component life, and turbine availability. To optimize both power production and the loads imposed on critical components, implementing the best sensing technology for supervisory controls, diagnostics, and prognostics was required.

The Reliawind project Deliverables

Prior to concluding the three-year project, ReliaWind met deliverable objectives by:

Providing a common set of protocols and standards to guarantee interoperability among different turbine manufacturers and customers
Integrating technologies, methods, and applications in a consistent set of remote control and monitoring tools
Developing a consistent set of applications that support operation and maintenance optimization to maximize turbine availability and minimize the cost of wind energy
Providing training to partners and other stakeholders about the tools needed to apply a reliability-minded approach to future design activities
Disseminating project findings to the EU wind energy sector through conferences, workshops, websites, and media initiatives

The Conclusion

The benefits of a fully integrated reliability analysis toolset like PTC Windchill Quality Solutions stem from the ability to use a single source of data across multiple analysis modules. In addition to eliminating the error-prone, time-intensive process of redundant data entry, PTC Windchill Quality Solutions effectively use legacy information, providing real-world results in its reliability prediction calculations to support the development of new system designs.

By being able to use the system metrics calculated as inputs for risk analysis, the PTC Windchill FMEA and Fault Tree modules can quantify the probability and severity of system risks in which part failure is a contributing factor. A fully integrated analysis using multiple PTC Windchill Quality Solutions modules considers various dimensions of system reliability simultaneously, saving time and streamlining analysis activities.