OMAE 2007-29327 EMPIRICAL WIND TURBINE LOAD DISTRIBUTIONS USING FIELD DATA

In the design of land-based or offshore wind turbines for ultimate limit states, long-term loads associated with return periods on the order of the service life (20 years, usually) must be estimated. This requires statistical extrapolation from turbine loads data that may be obtained by simulation or by field tests. The present study illustrates such extrapolation that uses field data from the Blyth offshore wind farm in the United Kingdom, where a 2MW wind turbine was instrumented, and environment and loads data were recorded. From this measurement campaign, the loads data available are in two different formats: as ten-minute statistics (referred to as “summary” data) and as full time series (referred to as “campaign” data). The characteristics of the site and environment and, hence, of the turbine response as well are strikingly different for wind regimes associated with onshore winds (winds from sea to land) and offshore winds (those from land to sea). The loads data (here, only the mudline bending moment is studied) at the Blyth site are hence separated depending on wind regime. By integrating load distributions conditional on the environment with the relative likelihood of the different environmental conditions, longterm loads associated with specified return periods can be derived. This is achieved here using the peak-over-threshold method based on campaign data but derived long-term loads are compared with similar estimates based on the summary data. Offshore winds are seen to govern the long-term loads at the site. Though the influence of wave heights on turbine long-term loads is smaller than that of wind speed, there is possible resonance of tower dynamics induced by the waves; still, to first order, it is largely the wind speed and turbulence intensity that control the design loads. Predicted design loads based on the campaign data are close to those based on the summary data discussed in a separate study. INTRODUCTION Our objective is to estimate design loads for an offshore wind turbine for which the environmental and load data are available from field measurements. Using a probabilistic approach, we intend to estimate design loads associated with a target failure probability or, equivalently, a prescribed service life for the turbine. Variables describing the wind and wave environment as well as the turbine response are modeled as random; their probabilistic distributions need to be established. Available data obtained from full-scale field measurements can provide a more realistic representation of the turbine response subjected to various environmental conditions than is possible with simulation data. Field data, however, can be “limited” in the sense that they are often recorded only for a finite period of time, and may not systematically cover all the possible environmental conditions expected to occur over the life of the turbine. Hence, with such limited data, statistical techniques are often used to extrapolate the loads from observed events to rarer loads associated with prescribed safety levels. Statistical extrapolation has been used to predict both extreme and fatigue design loads for wind turbines. A recent draft from the International Electrotechnical Commission (IEC) [1] of offshore wind turbine design guidelines also recommends its use. Examples of its use in other studies related to wind turbine loads include those by Moriarty et al [2], Fitzwater and Winterstein [3], and Agarwal and Manuel [4]. Our focus here is on the bending moment at the base of the turbine tower (the mudline bending moment) as the load variable of interest. The turbine under consideration is an instrumented 2MW wind turbine at the Blyth wind farm, located about one kilometer off the northeast coast of England, and for which data were recorded for about sixteen months. Key features of the site pertinent to the present study include contrasting characteristics of the environment and 1 Copyright © 2007 by ASME response associated with winds blowing from the shore to the sea (offshore winds) versus those associated with winds blowing from the sea to the shore (onshore winds). We use the campaign data (available as ten-minute time series) to predict long-term design loads. To gain a better understanding, we separate the data into offshore and onshore wind regimes as well as divide the data in each regime according to ten-minute average wind speed, V, and significant wave height, Hs. Then, for each bin or interval representing different V-Hs combinations, “short-term” load extreme probability distributions are derived using the peakover-threshold (POT) method. Integration of these short-term load distributions with the likelihood of different V-Hs combinations in the offshore and onshore environments can help to derive the desired design load. Due to statistical uncertainty associated with the use of limited data, we discuss how non-parametric bootstrap methods can be used to establish confidence intervals on design load predictions. The results from this study based on POT data are compared with those from a separate study that employed global (or epochal) load maxima available from summary data. LOAD EXTRAPOLATION Design Load Case 1.1b of the IEC 61400-3 draft design guidelines recommends the use of statistical extrapolation methods to predict extreme turbine loads [1]. With statistical extrapolation for wind turbine extreme loads, one seeks to estimate the turbine design load, lT, associated with an acceptable failure probability, PT, or equivalently with a target service life of T years, using the following equation: