The map, from 3TIER, shows departures from long-term mean wind speeds that range from -20% to +20% and provides an indication of how wind projects should have performed relative to their long-term production average based on their location. This type of analysis enables financiers and owners to perform portfolio analysis across regions and quickly view the effects of weather anomalies on both existing and proposed investments.

For the year as a whole, the US experienced above average wind speeds, though month-to-month and regional variability were not uniformly above average across the country. The Pacific Northwest and New England saw wind speeds roughly 5% below average for the year, while a broad section of the US from northern Montana to Texas and the mid-Atlantic states enjoyed a strong wind year with wind speeds 5-15% above normal.

The year began with weak La Niña conditions and lackluster wind speeds. However, a stronger La Niña, which occurred later than initially forecasted, combined with a negative Pacific/North America (PNA) pattern led to increased wind speeds throughout the spring. Summer winds were particularly strong in the southern states, where warm and dry conditions continued under a large upper-level ridge that suppressed winds further north. The ridge persisted into September, when the central and eastern US moved back into a more vigorous weather pattern, with frequent frontal passages contributing to higher than normal wind speeds.

By the end of 2011 the North Atlantic Oscillation (NAO) was in a positive phase, associated with lower than normal winds in the western US. The effects of this trend dominated those of the persistent La Niña state in the equatorial Pacific. However, winds were above normal for most of the country east of the Rocky Mountains, except in the northeastern US, where upper-level ridging kept temperatures warm and wind speeds low.

The wind performance map was created by comparing output from 3TIER’s continually updated meteorological dataset with wind conditions averaged over the period 1969-2008 from the same dataset. Wind speed values were computed using a numerical weather prediction (NWP) model run at a 15 km resolution and adjusted using available observations. The underlying datasets for 3TIER’s wind performance maps provide clients with operational intelligence for every location within a region and are available in nearly all regions worldwide.

Windpower Engineering & Development

Transformers serve as a hub for collection and distribution of energy changing the voltage level at different locations of the grid. They are a key component of the Smart Grid, loosely defined as an automated, widely distributed energy delivery network, characterized by a two-way flow of electricity and information, and capable of monitoring everything from power plants to customer preferences to individual appliances.

There is some ways to go before the vision of the Smart Grid is realized, but recently monitoring transformers has taken a leap forward, as energy production sites seek solutions to lower maintenance costs. Remote monitoring is seeing increasingly wider use especially remote wind farms and solar-powered production sites where having someone present to monitor transformers at fixed intervals is a costly proposition. As more renewable energy production sites come online, utilities have been investing in monitoring technology that keeps labor costs to a minimum.

Predictive maintenance gains favor over preventive

Remote monitoring and communication capabilities let utilities conduct “predictive” maintenance of transformers, which means conducting maintenance only when a parameter starts deviating from a pre-set standard. This typically does not occur at a pre-determined interval. Remote management let operators“see” how a transformer is operating and send someone to fix it only when it’s necessary.

The schematic show how Visizmax would network a wind farm for predictive maintenance.

Daniel Lambert, of Vizimax, a Montreal, Canada-based company that offers remote monitoring and control systems for public utilities and the industrial and private sectors, notes that remote management is being widely embraced, especially as operators need more profitable maintenance functions. “It is similar to today’s cars that tell you when to change your oil or conduct other maintenance based on specific driving habits,” said Lambert. “Rather than changing your oil at 6,000 mile intervals, electronic sensors can determine if you do mainly city or highway driving and signal the need for an oil change accordingly. That basic principle has been adapted for use in monitoring transformers.”

When maintenance is done purely in preventive mode, operators have no idea what is really happening inside and next to the transformer, and may tend to unnecessarily shorten intervals between each maintenance activity. With predictive monitoring, utilities can save money by having a real understanding of the key transformer parameters, which include temperature, liquid level, pressure vacuum, outgoing voltage, and ingoing voltage.

Utilities that conduct preventive maintenance programs usually use either time or quantity of energy consumed as the determining factor. Once they install remote monitoring capabilities, most switch to predictive-maintenance modes. Payback on the investment in remote monitoring equipment is estimated between 9 and 15 months.

Among those moving in the direction of this type of remote monitoring are GE, Siemens, Alstom Grid (Areva T&D), Schneider Electric, BHEL, Crompton Greaves, New York Power Authority, National Grid, Power Grid of India, and Hydro-Quebec, among others.

Building transformers that incorporate digital monitoring

Many transformer manufacturers are recognizing this growing demand for online transformer monitoring products and diagnostic services, and investing in building them, especially for step-up transmission, high-voltage transformers.

These technologies will be critical for improving grid reliability and helping utilities avoid transformer failures and resultant blackouts. They will also reduce maintenance costs and defer capital expenditures by extending a transformer’s useful life.

In addition to monitoring vital statistics such as temperature, pressure, and vacuum levels, there has also been a burgeoning interest in conducting dissolved gas analysis (DGA) of the oil in transformers. A DGA takes samples of an oil’s exhaust gases to determine if events have occurred that might be detrimental to the transformer and reduce its life. Industrial transformer maintenance people and utilities are setting up these planned sampling programs, using online devices that can monitor oil quality.

This can greatly improve reliability, because users will know in advance when something has to be replaced, rather than risk unscheduled outages. For food-processing plants and mills, which can lose millions of dollars when power is interrupted, this type of sampling program is being undertaken to ensure reliable power.

Transformers in place now are already using various smart devices for load switching. In the 21st century, the move will be towards monitoring systems that promote transformer reliability. Ensuring reliability on the grid by replacing equipment before it fails and anticipating upcoming problems is what transformer manufacturers will be focusing on.

Remote monitoring equipment

The latest technology used for remote transformer monitoring includes a combination of a remote terminal unit, a programmable language controller, a gateway (a network node equipped for interfacing with another network that uses different protocols), and a protocol converter. The complete monitoring system is usually placed in service by a system integrator or a power manufacturer.

One type of control panel can show the essentials for each turbine. Why is #2 not producing?

An example of one such system is Vizimax’s RightWON, a secure, modular, rugged automation remote terminal unit (RTU), programmable logic controller (PLC), gateway, communication conversion protocol platform that connects circuit breakers, transformers, IEDs, meters, sensors, control and monitoring devices at the substation level or anywhere on the distribution network. In addition to energy applications, the monitoring devices are used in water and telecommunications monitoring.

The unit is for remote management applications, providing support for equipment through its data acquisition, monitoring, control and remote maintenance functions. At renewable energy sites, the system shuts down and locks circuit breakers (CBs) and inverters remotely, which avoids traveling to an energy-production site for distribution-line maintenance. The production site is still operational while maintenance is occurring on the network.

The unit is often used in substations, where it usually connects to transformers, circuit breakers, intelligent electronic devices (IEDs), protective relays, and meters through the electric utility’s private network. In some instances, in can be used strictly for monitoring transformers in substations, using wireless connectivity (through GSM, GPRS, 3G or CDMA networks). Parameters such as temperature, liquid level, pressure vacuum, outgoing voltage, and ingoing voltage will be measured and transferred to a utility’s supervisory control and data acquisition (SCADA) system. Wireless monitoring calls for specialized Web service interfaces.

The system makes it economical to conduct monitoring, because it can convert older controls and sensors already implemented to newer communication protocols without having to replice or modify them. The monitoring system connects to the SCADA using fiber optic cables or web interface through a wireless device. It offers a remote and local view on event logs charting key assets status changes and alarm signals about exceeding thresholds.

Remote access is made possible through networking, security, and telecommunication functions, and is supported by a broad range of integrated interfaces. These interfaces support a wide variety of industrial protocols (IEC 61850, DNP3, ModBus, and IEC 60870) as well as Web access and remote maintenance functions. The information collected is easily viewed on smart phones from anywhere, any time. The system supports sending notifications by email, text messages, or pager. Users who receive a message can access the system using a Web browser to view the data and operate the site remotely.

The system is IEC 61850 KEMA certified and can also be used for a variety of other Smart Grid applications, including remote default detection and automated reclose/disconnect operations within distribution networks, and alarms and operational data broadcast and commands, usually to or from the SCADA of power utilities, IPPs, integrators and equipment manufacturers’ team management applications.

Monitoring equipment also used in re-energizing transformers

In addition, new flux management monitoring technology is being deployed more at the end of transmission lines during re-energizing a transformer, a regular process that takes place every time a production site connects to the grid. The need to re-energize varies considerably, but there are always times when required maintenance requires stopping the connection between the production site and transmission lines. When maintenance is completed, the transformer must be re-energized.

The flux management units avoid network inrush that may create network outages while transformers are re-energized, something that in the past had been a frequent occurrence. This is extremely important, because several hours of lost revenue in a month can mean the difference between a profitable energy production site and an unprofitable one.

The system calculates the residual flux while a transformer is reenergized at the last operation, and ensures that the next transformer operation is performed at the exact millisecond such that residual flux is identical to the previous operation, which minimizes current inrush and stress on both the high voltage transformer and circuit breaker.

The unit measures relevant parameters from the transformer and sends a command to the circuit breaker of the transformer to achieve this operation. Using this equipment increases the quality of the network, decreases network outages, and decreases the frequency and the cost of maintenance operations.

Last thoughts

Remote monitoring makes it possible to manage maintenance of remote power equipment in the field in a predictive mode instead of the more traditional and more expensive preemptive or preventive mode. By remotely monitoring power equipment in remote locations, electric utilities now only must execute maintenance operations on their equipment when it is required. This is going a long way to cutting costs for the many renewable energy production sites that are coming online.

Pacific Crest Transformers
http://www.pacificcoasttransformers.com

Windpower Engineering & Development

The GLD360 is now able to locate and characterize lightning in areas of the world where meteorological observations may be partially lacking or absent.

The developer of a global lightning detection network will provide access to its Global Lightning Dataset GLD360, which holds lightning data with peak current estimates, greater location accuracy, and improved polarity classification than previously available. The enhancements in data quality will help users make more informed decisions, increase operational safety and efficiency, and deliver better services.

Vaisala has implemented a new processing algorithm that lets the global network identify the location of a cloud-to-ground lightning stroke within a range of 2 to 5 km. Polarity classification accuracy now stands at better than 90%, while peak current estimates have been improved to be accurate within 25% of the peak current value. The GLD360 is the only global lightning dataset, say developers,  that provides polarity and peak current estimates for lightning events. “For the first time, quality lightning warnings are now possible on a global-scale,” says Nick Demetriades, Offering Portfolio Manager for Vaisala’s Airports business.

“The significant improvement in the GLD360 data quality is due to a vastly improved location accuracy of the global network, combined with its ability to detect about 70% of all cloud-to-ground lightning flashes. This will improve the safety and efficiency of airport operations across the world.”

According to the company, the global lightning-detection network provides uniform coverage with high detection efficiency over the entire world because it has more lightning information than any other comparable dataset in the world. Daily counts routinely exceeding 1.5 million events.

A density study from Vaisala tells where and frequency of lightning strikes.

The GLD360 is now able to locate and characterize lightning in areas of the world where meteorological observations may be partially lacking or absent. Data from the network is easily assimilated into weather models to improve short-to-medium term forecasts. In addition, it can be used as a proxy for weather-radar information in areas with limited or non-existent radar coverage. Because the company owns and operates the global network and delivers lightning data as a service, users can access the information in the GLD360 without having to make substantial hardware investments.

Vaisala Inc.
www.vaisala.com

Windpower Engineering & Development

Generation technologies such as wind and solar are gaining market share, while at the same time they are introducing an uncertain wrinkle into the old reliable power grid — variability.

This article comes from NREL
With the simple flick of a light switch, you connect to “the machine,” the North American electric grid. It’s the world’s most complex transmission and distribution system — also is referred to as the world’s largest machine. That same machine has run reliably on coal, natural gas, and nuclear energy for decades.

Now, it’s time for a tune up. Newer power-generation technologies, such as wind and solar are gaining market share, while at the same time, they introduce an uncertainty into the power grid in the form of variability.

The new Energy Systems Integration Facility (ESIF) at U.S. Department of Energy’s (DOE) National Renewable Energy Laboratory (NREL) is tackling the challenge of keeping the power grid running reliability while at the same time introducing a host of new equipment into an already complex system.

According to NREL’s director for Electricity Resources and Buildings Integration, David Mooney, the grid has operated essentially the same for more than 50 years. Utilities know how to predict energy demand by looking at the day of the week and the weather forecast. Then, utilities dispatch generating sources (usually from coal or natural gas) to meet expected demand.

An artist with the Smith Group, rendered the Energy Systems Integration Facility. The view is from west to east.

“It was a pretty orderly way to operate a system,” says Mooney. “Today, there are a lot of technologies coming online — including wind and solar — that are going to require a transformation in the way this orderly system operates. Now instead of only having variability in the electric demand, we are also introducing technologies that make the generating supply variable as well.”

Renewable Energy Already is Connected — Or Is It?

For homeowners with photovoltaics (PV) on their roofs, or the technician working a wind farm in Texas, this may seem moot. Their technologies already are connected to the grid and the electrons seem to be flowing seamlessly.

However, this type of renewable energy still make up a small fraction of the America’s overall energy generation. According to the latest data from the U.S. Energy Information Administration, power generation from renewables such as biomass, geothermal, solar, and wind have so far accounted for less than 5% of the total U.S. power generation in 2011.

Connecting that small amount of renewable energy into the power grid is not a big deal. “If you have 1,000 homes and five to 10% of those add solar, the variation and the output of the PV is in the noise of the grid,” says Mooney. “Right now, utilities view PV systems in small numbers as a demand reduction technology — no different than people switching to compact florescent bulbs. But, if 50% of those 1,000 homes have a PV system, then the output could start to look more like a generating technology to the utility. Once it reaches those kinds of numbers, the utility has to start worrying about things like variation in cloud cover that cause PV output to vary.”

“As all of these new technologies become cost effective, they are starting to get used a lot more and utilities ask, what’s the best way to integrate these variable technologies while maintaining a safe and reliable electric power system?” says NREL’s Director of Energy Systems Integration Ben Kroposki.

ESIF is the first significant DOE laboratory designed specifically to deal with the integration issue. NREL’s researchers will be able to configure electric systems the way they would appear in the field and operate them at the same level of power as the utility uses.

“We all know how we react when the power goes out. The local utility usually bears the brunt of the PR problems associated with an outage,” says Mooney. “So it is understandable that they are conservative in how they go about adopting new technology. That is why the ESIF is so important for reducing that risk. The utilities will be able to operate new technologies in an environment that mimics the real system so they can work out the bugs of introducing new technologies beforehand and maintain the high reliability standards that we have all come to expect.”

ESIF also will be a plug-and-play environment for industry. “ESIF is set up so that partners can bring in technologies — such as a PV inverter or battery system — and we will have on hand the other equipment that the technology being tested would connect to. They don’t have to go and try to find these complete systems,” Kroposki said.

ESIF also will loop the utilities’ hardware into a simulation environment so they can look at new technologies operating in a combination of real world, real power and simulated or virtual environments to see the impact that these new systems are going to have on the reliability and quality of the power. “Once they try it all out in the ESIF, we believe the utilities will be much more inclined to adopt the technology,” says Mooney.

Fun facts to know and tell about ESIF:

• Cost: $135 million
• Area: 182,500 ft²
• Office space: about  200 ft²
• State-of-the-art electric systems simulation and visualization
• Component and systems testing and at MW-scale power
• Combine functioning systems with utility simulations for real-time, real-power evaluation of high penetrations of renewable energy
• 15 laboratories
• Four outdoor test areas
• Construction complete: fall 2012

Other key service and support features:

• Research Electrical Distribution Bus (REDB)
• High Performance Computing Data Center (HPCDC)
• Hardware-in-the-Loop Prototyping at Megawatt-scale Power
• Collaboration and Visualization Rooms
• High bay control room

Another challenge for NREL researchers will include looking for ways to improve the grid. “For mostly economic reasons, we can’t build up a new grid and then switch over to it,” Mooney said. “ESIF will let us look at how we can make the grid ‘smarter’ and more flexible to receive technologies in a way that can maintain or even enhance the existing grid.”

Many technologies on the grid are antiquated, and can be up to 50 years old. A “smart grid” has three components that the current grid doesn’t have:

• Sensors as part of the grid so that power quality is measured in real time
• Communications to relay data coming from sensors to utility operators to assist with decision making
• Controls that allow system operation changes from a central location.

“People are surprised to learn that most utilities still don’t know about a power outage until they get a call from a customer,” Mooney said. “If we had a smarter grid, we would have sensors on transformers and power lines that would let utilities act preemptively if problems in power quality were arising so they could keep the power on. However, if the power did go out, utilities would be able to see it immediately and dispatch crews to minimize down time.”

As utilities put smarter technologies onto the grid, another avenue for energy savings is to have the grid communicate with a home energy-management devices so the home can work with a utility to manage power needs.

“The smart grid will offer the possibility of frequent, likely automated communication between a utility and a customer,” Mooney said. “As a consumer, you will likely be able to know when the power is cheapest, for example, and have a plan with the utility that tailors your power consumption accordingly.”

The foundations for a smarter grid are being laid today. In June 2011, Secretary of Energy Steven Chu announced that more than 5 million smart meters had been deployed thanks to Recovery Act-funded efforts to accelerate modernization of the nation’s electric grid.

“To compete in the global economy, we need a modern electricity grid,” he said. “An upgraded electricity grid will give consumers choices and promote energy savings, increase energy efficiency, and foster the growth of renewable-energy resources.”

NREL
www.NREL.gov

Windpower Engineering & Development

Isight 5.6 delivers a number of postprocessing enhancements, including a new option in the approximation viewer to examine constraint violations using a floor projection graph.

The latest release of Isight provides designers, engineers, and researchers with an open system for integrating design and simulation models—created with various CAD, CAE, and other software applications—to automate the execution of hundreds or thousands of simulations.  The software lets users trim time and improve their products by optimizing them against performance or cost metrics through statistical methods such as Design of Experiments (DOE) or Design for Six Sigma.

“Isight has features and add-ons that let us integrate all of the different codes and interfaces used for our physics subroutines,” said Cosimo Chiarelli, head of the Aeromechanics and Propulsion unit in Business Segment Space Infrastructure and Transportation at Thales Alenia Space Italia in Turin.  “It played a key role in helping us unify our processes and saved a considerable amount of time in our design optimization process.”  Key features of Isight 5.6 include:

• Reliability analysis techniques for importance sampling lets users compute and sample around the most probable point of failure in a design. When compared to sampling around the mean value point, importance sampling requires orders of magnitude fewer evaluations for the same accuracy in predicting the probability of failure or success. This is especially important in the verification of high-reliability systems, such as jet turbines or automotive brakes.

• Updates to Abaqus support multiple Abaqus/CAE cases by providing users with the option to parse all detected input files and create output parameters for multiple analyses
• Updates to the Data Matching component allow defining and matching multiple data sets within multiple ranges
• New options in an approximation viewer for overlay-constraint graphs perform quick trade-off studies by relaxing constraints, show or hide constraint boundaries, and view constraint violations using a floor projection graph

Dassault Systems
http://simulia.com/products/isight.html

Windpower Engineering & Development

Brett A. Marmo

Barry J. Carruthers

A tower-wall vibration following the yaw test indicates a suite of resonances between 735 and 877 Hz.

Wind turbines occasionally get a bad rap for noise. At times, it’s not the turbine’s fault but rather an internal problem that amplifies the noise. For instance, a recent set of megawatt-scale wind turbines were emitting noise in the 800-830-Hz band. The manufacturer identified 820 Hz as the frequency at which the gear teeth mesh in the gearbox’s last stage. All conventional wind turbine gearboxes have gear-meshing frequencies but do not cause problematic noise, indicating the vibration was being amplified structurally. A vibration survey using a set of accelerometers identified the vibration pathway between the gearbox, nacelle, and tower walls.

A structural resonance involving the gearbox and its mounts was found in the 776 to 878-Hz band, as were other resonances in a tower wall. A complex dynamic relationship between meshing gear teeth, the gearbox mount, and the tower wall led to the amplification of the gear mesh frequency and the resulting tonal noise. Software such as COMSOL Multiphysics, can assist in identifying the modal shapes of each vibrating element using eigenfrequency and frequency response models. It may be useful to other teams working on turbine noise problems to see how we developed a structural-acoustic interaction model and found solutions to the tonal-noise problem.

Sound pressure levels were measured 20 m from the tower base between 1 and 3,000 Hz. Peaks appear between 700 and 900 Hz, and show good agreement with the vibration survey at site and the eigenfrequency (FEA) analysis of the tower skin.

A vibration survey

First, we initiated a vibration survey used tri-axial accelerometers on the gearbox, generator, yaw bearing, and tower wall. Data was logged at 3,000 Hz and all accelerometers were aligned with the z-axis (up) and x-axis (parallel to the main shaft).

A yaw test excited all resonant frequencies in the structure. The test is akin to a bump test in which a structure is struck with a hammer and the response recorded. However, wind turbines and towers weigh many tons so an acoustic hammer would be too small. Instead, the nacelle is yawed 90° then stopped and the response recorded. The gearbox showed a strong vibration with a band between 720 and 900Hz with peaks at 776 and 878 Hz in the y-direction (transverse to the main drive shaft). This indicates a structural resonance involving the gearbox and its isolation mounts. A suite of resonances were also identified in the tower wall, with the tower skin vibrating at sensor frequencies of 735, 777and 877 Hz.

An active test with the turbine at normal operational speed determined how forced frequencies interact with the structural resonances. Forced frequencies come from some periodic motion, usually mechanical. In turbines, they come from rotor rotation, gear-meshing, and bearings. Waterfall diagrams separate forced frequencies from the structural harmonics of a system. The accompanying waterfall diagram shows a variation of vibration amplitude with frequency (x-axis) and time (y-axis).

High-order structural resonances tend to have complex modal shapes with wavelengths orders of magnitude less than the scale of the entire dynamic structure. While it is possible to interpret the first one or two bending modes and their modal shapes based on data from accelerometers, this becomes increasingly difficult with higher orders because more accelerometers are required.

Gearbox resonance

Geometry and boundary conditions (left) were used in the eigenfrequency study. The modal shape of the 792.3 Hz resonance where the colors indicate unitless displacement with red as max.

The gearbox and its bushing mounts were modeled in the FEA program using solid elements. The 11,000-kg gearbox has a moment of inertia, 8.1 kg m2, about an axis co-linear with the main-drive shaft. Rubber bushings isolate the gearbox with a spring constant of 160 kN/mm, which equates to a Young’s Modulus of 43 MPa. The outer boundaries of the bushings were fixed conditions. The main-drive shaft connects to the front of the gearbox prohibiting vertical and horizontal movement but allowing rotation, so roller-boundary conditions were applied to the front of the gearbox. An eigenfrequency analysis found a resonance at 792.3 Hz that involves rotation about an axis co-axial with the main drive shaft. Frequency response analysis later showed that resonance can be excited by frequencies from 720 to 840 Hz. The modal shape and frequency range that excites the resonance is consistent with the data recorded during yaw tests. This resonance involves displacement across the rubber bushings. Hence, their isolating properties are ineffectual in this frequency range letting the 820 Hz gear-meshing pass into the nacelle’s support frame and into the tower.

Gearbox vibration from the yaw test indicates a structural resonance at 792.2 Hz.

Sheet metal such as that used in the tower, has what are referred to as skin frequencies. These are high-order bending modes excited when a piece of metal is stuck with a hammer. Skin frequencies, which produce the greatest overall displacement, often cluster around one frequency and combine to produce a distinct note. The tower uses 21-mm thick sheet metal at the base and 10 mm near the top.

A 3D eigenfrequency analysis of the tower uses shell elements. There are two planes of symmetry about the tower’s vertical axis, so it was quartered with symmetric boundary conditions on appropriate edges. The lower (foundation) edge was assigned a fixed boundary condition. Four skin frequencies were found with high participation rates: 727, 763, 799, and 829 Hz. These skin frequencies excite large areas close to the top, thus providing a mechanism to amplify the gear-meshing vibration and transmit it to the surrounding air as noise.

We also developed an acoustic-structural interaction model of the tower walls and air inside and outside the tower. The model combined two acoustic time-harmonic analyses representing internal and external air, which are separated by an axial-symmetric, structural mechanic, harmonic-response analysis that represents the tower wall. A “sound-hard” wall served as the lower boundary condition for the acoustic model inside the tower (the concrete base) and a sound-soft condition worked for the acoustic model outside the tower. The acoustic model of air inside the tower was excited at the upper boundary (bounding the nacelle) with a constant non-zero pressure source. Energy was allowed to propagate through all three models. Tower walls show the resultants.

A remedy for the noise involved breaking the vibration pathway between the gearbox and tower walls. The simplest method was to modify the elastic properties of the rubber bushings used for gearbox isolation so they no longer participate in resonances close to 820 Hz and thereby cease allowing passage of gear-mesh vibration. However, due to constraints, such as a required stroke length, it was impractical to either sufficiently stiffen or soften the bushings. Instead, resonance in the tower skin was modified. Tower skin has a high Q-factor (about 200, equivalent to a loss factor of 0.0025) which is why it rings so effectively. To reduce the amplification effect of the tower skin in the 720 to 830 Hz range, we selected a thin composite laminate with high-loss factor damping (0.09) to apply to the turbine walls. Eigenfrequency models identified significant amplification between the 3rd and 8th weld. The acoustic-structural interaction model was modified to examine the effects of covering different parts of the tower. A 3-mm thick section of damping material with appropriate elastic parameters and a damping factor of 0.09 was placed on the tower wall. The comparison of damped tower sections shows the sound pressure level 20 m from the base of the tower for models with different amounts of damping laminate. After a cost-benefit analysis, the turbine manufacturer was able to choose the minimal amount of damping laminate to install for satisfactory reduction in noise levels.

This wind project demonstrated several uses of the software. Eigenfrequency analysis complimented data collected in a vibration study and allowed determining complex modal shapes of high-order resonances. Models showed gear-teeth vibrations moving from gearbox, through isolators, and into the tower wall. Once the models provided understanding of the modal shapes of the amplifying skin frequencies, it was possible to target areas close to the top of the tower with a damping laminate. Finally, the software helped develop an acoustic-structural interaction model that showed how different amounts of coverage with the damping laminate affected the sound level outside the tower. This model let the turbine OEM select a cost-effective solution which satisfied regulatory requirements and allowed the continued operation of the wind turbines without impinging on residential communities. WPE

Windpower Engineering & Development

Wind Trends from AWS Truepower provides a weather snapshot at multiple heights above ground for every hour.

During the second quarter of 2011, nearly all of the contiguous United States experienced normal to above-normal wind speeds compared to the long-term average for the same quarter. The greatest deviations occurred throughout the Rocky Mountains and southern Great Plains (over 25% above normal in some locations). Regions experiencing significantly below-normal (by 5% to 10%) wind speeds were limited to southern Georgia and Florida. During April, the El Niño/Southern Oscillation (ENSO) weakened considerably but still remained in a negative phase (La Niña). Meanwhile, a strongly positive North Atlantic Oscillation (NAO) and moderately negative Pacific-North

American pattern (PNA) developed. In response to this pattern, a mostly zonal storm track persisted throughout much of the continental United States, maintaining widespread normal or above-normal conditions. Portions of the Pacific Northwest, Rocky Mountains, Southern Plains and Appalachians experienced wind speed deviations that were 20% or more above normal during this month.

The ENSO index weakened throughout May and June and entered a neutral phase. During this period, the PNA transitioned into a slightly positive phase while the NAO entered into a moderate negative phase. In May the storm track shifted northward over much of the eastern United States and promoted relatively weak wind speeds throughout the Ohio River Valley, Mid-Atlantic, and Northeast. All other regions experienced normal or above-normal wind speeds. In June the storm track became more zonal and wind farms throughout the much of the United States were windier than average. Below-normal wind speeds were limited to small areas of the West Coast and New England.

The year ending 30 June 2011 (Q2 2011) exhibited near- or above-normal wind speeds throughout much of North America, with significantly above-normal wind speeds prevailing throughout much of the Rocky Mountains and Southern Plains (up to 15% above normal in select local areas). This 12-month period is sharply different from the previous year (ending 30 June 2010), when about half of North America experienced average or below-average wind speeds. Data for this analysis came from AWS Truepower’s Wind Trends product, a validated database of weather conditions dating back to 1997. Wind Trends provides a weather snapshot at multiple heights above ground for every hour. Maps, data and monthly reports for wind resource deviations, updated on a monthly basis, are available by subscription to windNavigator Asset Management.

AWS Truepower
info@awstruepower.com

Windpower Engineering & Development

In this webcast, a brief overview of state of art simulations tools available from Altair Engineering will be presented. Following the introduction of the tools, two of the most influential simulation technologies will be discussed. Namely, multibody dynamics (MotionSolve) and computational fluid dynamics (AcuSolve). The webcast proceeds with a discussion of case studies that demonstrate areas in which these technologies have been successfully applied to wind power engineering.

3 Bullet Points of What Participants Can Expect to Learn:

1. Computer Simulation Technologies that will help deliver optimal wind turbine design and as a result improve turbine power output and overall operating efficiency and performance

2. State of Art Simulation Technologies for Wind Turbine Designers and Engineers

3. Reduce Time to Market and Reduce Dependency on Physical Testing

REGISTER BELOW

Windpower Engineering & Development

3TIER’s second quarter map indicates departures from long-term mean wind speeds that range from -20 to 20% and provides an indication of how wind projects should have performed relative to their long-term production average based on their location.

A renewable energy risk analysis released a wind performance map for the second quarter of 2011. The map illustrates wind speeds above seasonal averages for most of the continental U.S. 3TIER’s second quarter map indicates departures from long-term mean wind speeds that range from -20 to 20% and provides an indication of how wind projects should have performed relative to their long-term production average based on their location. This type of analysis lets financiers and owners perform portfolio analysis across regions and quickly view the effects of weather anomalies on both existing and proposed investments.
Looking at wind speeds during the second quarter of 2011, the pattern is 5 to 10% above normal for nearly the entire US. Especially strong positive wind anomalies centered over the southern Mississippi Valley and the southern Rocky Mountains. Some areas within these regions exceeded 25% above normal wind speeds when averaged over all of April, May, and June.
The only areas that experienced wind speeds below average were North Dakota along the Canadian border, isolated areas of California, and along the Carolina coast, and through Florida. These areas saw minor negative anomalies of about 5% with the exception of Florida where wind speeds were up to 15% below normal.
Climate conditions across North America during the second quarter of 2011 were influenced by a weakening of La Niña that occurred this past winter in the tropical Pacific Ocean, leading to neutral El Niño/Southern Oscillation (ENSO) conditions by the end of the quarter. This quarter was also affected by a gradual transition in the Arctic Oscillation (AO) and North Atlantic Oscillation (NAO) from a positive state at the start of the quarter to a negative state at the end.
However, the dominant feature of the second quarter across the US was a stronger pressure gradient and thus much higher than normal winds over the south-central part of the country. The unusually strong pressure gradient resulted from three anomalous patterns that developed during the quarter: an upper-level trough over the northwestern United States, associated low surface pressure in the northern Great Plains, and high surface pressure over the Southeast.

The wind performance map was created by comparing output from 3TIER’s continually updated meteorological dataset with wind conditions averaged over the period 1969 to 2008 from the same dataset. Wind speed values were computed using a numerical weather prediction (NWP) model run at a 15 km resolution and adjusted using available observations. The underlying datasets for the company’s wind performance maps provide clients with operational intelligence for every location within a region and are available in nearly all regions worldwide.

3Tier
www.3tier.com

Windpower Engineering & Development

GAS NATURAL FENOSA

 GNF engineers are says to have used the software to develop a set of core models that analyze available data, forecast results, and optimize generation plans. Each software model accesses a central database for historical power consumption and price data, weather forecasts, and parameters for each power plant. Optimization Toolbox  was applied to minimize production cost among several plants given a set of constraints, including carbon caps and maximum capacity. Statistics Toolbox let the engineers develop and assess price-simulation scenarios.
MATLAB Compiler let the team created standalone programs from each model that run automatically day and night, letting developers more easily manage updates to the models and access to the models for a variety of end users providing improved management of updates and control of access to models. The team also used Simulink to model the behavior of generators in the GNF infrastructure.
“Our market changes quickly, so we need to know how to promptly respond to changes in regulatory standards or in the structure of the electricity industry, as well as to other factors, such as increased production of renewable energy,” said Isaac Pérez, head of the Iberian Electricity Markets Technical Office. “We tried a commercial software package without development and customization capabilities, but it did not address the numerous problems we needed to solve. In our circumstances, closed systems don’t work well. We needed an open platform that would let us develop our own algorithms and computations.”
“A company like GNF, with a generating capacity in Spain of over 15,000 MW  and a portfolio of assets including a variety of generating technologies, must accurately predict different variables affecting the markets where it operates,” said Juan Nasarre, managing director of MathWorks for Spain and Portugal. “The software let the company detect better business opportunities, reduce generating costs, and therefore improve its sales margin.”

MathWorks
mathworks.com

Windpower Engineering & Development