Striking the right balance between costs and benefits when designing a measurement campaign has always been a challenge. Nowadays the situation is more complex due to:

• sophisticated instrumentation options (and their limitations);
• wind farms of larger spatial extent located in more diverse climates;
• advanced flow modelling.

It is no longer a straightforward process deciding on the optimal measurement strategy to minimise uncertainties in the energy assessment for a specific project. Assessing the resulting financial benefit is just as challenging. The interpretation of the data for site classification and thus choice of turbine has also become more complex.

Learning objectives

• Evaluate the most efficient use of instrumentation for a specific site
• Understand and quantify the connection between measurement uncertainty and project economics and loads
• Make a more accurate choice of turbine type
• Express uncertainty variations across the site as the basis for cost-efficient measurement campaigns

In recent years substantial datasets of historic power performance tests have been assembled and these deliver a clear message: wind turbine performance in real world conditions can depart considerably from performance in idealised test conditions. A renewed sense of realism has been awakened in the resource assessment community which has led to the adoption of various methods for transposing an ideal warranted power curve into a real world power curve, i.e. a curve which represents a true central estimate of the power delivered in the site-specific wind speed, air density, turbulence, wind shear, inflow etc. These methods remain embryonic and there is much work left to do. Can the industry converge on accepted and standardised methods? Is it feasible to apply these new methods in everyday resource assessment calculations? Can these methods be shown truly to improve upon the simpler approaches they are superseding?

Learning objectives

• Understand why the use of real world power curves is important
• Apply the Inner-Outer range concept
• Apply the turbulence renormalisation method
• Apply the rotor equivalent wind speed (RESW) method
• Apply the power matrix (proxy) method
• Get an update of the progress of the Power Curve Working Group (PCWG)

With a new version of IEC 61400-11 published this year, it may be thought that wind turbine noise emission is well understood. However, wind farm developers still receive complaints from residents about 'amplitude modulation' (AM) noise, a subject not covered by the IEC standard. Amplitude modulation will therefore be one of the main topics in this session. This session will also explore different sound propagation models and the impact of environmental conditions on propagation. Another topic for this session is how to optimise wind turbines and wind farms for sites with noise restrictions.

Learning objectives

• Understand why it is important to consider noise for resource assessment
• Understand how a wind farm as a whole can be optimised to maximise performance with noise constraints
• Understand what amplitude modulation is and why it is a concern for residents near wind farms
• Understand the role that propagation modelling plays in the prediction of far field noise

No new long-term correction methods have appeared for years and it is possible that current techniques are optimal. There are several issues which affect any long-term correction analysis:

… from the mundane: the optimum reference period? how do we measure success? re-analysis data or ground based stations? non-integer years of data?

… to the exotic: atmospheric stability, climate change decadal variations, sun spots activity and solar cycles.

It is likely that long-term correction techniques which consider these may provide more reliable predictions than has previously been possible.

This session describes new long-term correction methodologies and compares the results with those of conventional methods. Innovative techniques to improve the representativeness of long-term data series are discussed, different long-term data series are compared and conclusions on the decadal-scale variability of the wind speed are presented. The overall objective of this session is to give insight on how these developments contribute to a greater certainty in future wind speed predictions.

Learning objectives

• Evaluate innovative methods to improve the representativeness of long-term data series and the overall accuracy of long-term extrapolations
• Compare new long-term correction methods to traditional methods
• Understand how a more accurate description of the decadal-scale variability of the wind speed contributes to the reduction of the uncertainty in the long-term corrected wind speed

The use of remote sensing within the wind industry has developed significantly since these techniques were first adopted. The new opportunities to make measurements that have been made available have themselves influenced the aims and objectives of the measurements, as it has become possible to consider assessing aspects of wind that were previously overlooked due to an inability to acquire data with more limited instruments. This has led to an industry-wide learning process, as new applications have emerged in response to the measurement opportunities made available by remote sensing, and more effective methods for meeting existing requirements of measurement campaigns have been identified. This session provides an opportunity both to review industry progress in making the most of remote sensing and to look ahead to the possiblities that are now emerging.

The accurate prediction of the wake effects within and between wind farms is vital to wind farm design and to provide a prediction of the energy output. To date, the vast majority of wind farms have been designed using engineering models which have been tuned and validated using experimental data. As wind farms become larger, empirical correction upon empirical correction are being developed upon the basis of scarce and perhaps erroneous experimental data. Perhaps this is the appropriate time to question if this is the right and only approach.

In the session advanced models and observations will be described and discussed.

Learning objectives:

• Describe what wakes are, how they can be seen in observations and how they are modelled
• Identify different models, state-of-the-art and more classical ones
• See wakes in observations and understand the related issues

Recent advances in the software and computational resources available to the wind industry have opened a new frontier; the ‘model chain’. A single approach to such a concept has yet to emerge as the industry standard, although a general idea of a dynamic process across progressively smaller scales is emerging. This session intends to give delegates a broad view of how research institutes and private companies are dealing with this challenge, what the most promising approaches are and which range of applications is foreseen for the coming years.

Learning objectives

• Understand some of the challenges of multi-scale modelling
• Get a first glimpse of current approaches in this topic
• Talk directly to the main players in this research field

The growth of wind energy is increasingly impacting upon and limited by the grid infrastructure of many countries. Accurate short-term forecasting of wind power can help to maintain the growth of the sector. System operators are able to avoid many balancing issues if accurate production forecasts are available. Deviation charges of wind farm owners could be greatly reduced using new techniques. Accurate forecasts can also bring significant benefits when planning turbine maintenance, especially offshore.

Learning objectives

Delegates will learn about topics including:

• Economic impact of forecast accuracy
• Making wind a firm resource: the role of forecasting
• Using forecasting models to schedule maintenance activities
• Dealing with the real world: best practices for including extreme conditions into the forecast models
• Forecasting challenges for 2014-2020, quick-to-market tools and R&D