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Delegates are invited to meet and discuss with the poster presenters in this topic directly after the session 'Remote sensing: From toys to tools?' taking place on Wednesday, 12 March 2014 at 14:15-15:45. The meet-the-authors will take place in the poster area.

John Medley ZephIR lidar, United Kingdom
Co-authors:

(1) ZephIR lidar, Ledbury, United Kingdom (2) Fraunhofer IWES, Bremerhaven, Germany

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Presenter's biography

Biographies are supplied directly by presenters at EWEA 2014 and are published here unedited

John Medley is a Chartered Engineer with over 20 years experience of making and analyzing electromagnetic measurements. He has an Honours Degree in Mathematics and a Masters in Mathematical Modelling from the Universities of Bristol and Loughborough respectively. He received the IET’s Measurement prize in 1995 while working for the UK’s National Physical Laboratory, before spending 12 years in the radar industry. Since 2011 he has worked as a data analyst with ZephIR Lidar and is especially interested in optimizing algorithms for lidar data processing.

Abstract

Correlation effects in the field classification of ground based remote wind sensors

Introduction

A classification scheme for remote sensing devices (RSDs) is included in the revision of IEC-61400-12-1 to enable a traceable assessment of uncertainty in RSD measurements. An assumption of the classification scheme is that the environmental variables against which an RSD is classified are uncorrelated. The standard acknowledges that this assumption is not always valid and mitigation is permitted. A method for mitigation is however not included. The effect of correlation between environmental variables is presented along with a method for mitigation that removes the duplicate uncertainty components caused, resulting in a more representative uncertainty assessment for the RSD.

Approach

The mitigation method is tested for the classification of a ZephIR 300 VAD scanning wind lidar at the ZephIR Ltd. tall mast test site at Pershore and the results of the mitigated classification test compared with of the unmitigated classification. The results of 44 verification tests and 20 application tests as defined in the draft revision of IEC-61400-12-1 using the mitigated and unmitigated classifications are also presented for comparison. The potential effect of correlation between environmental variables (EVs) on the uncertainty assessments in the verification and application of RSDs in different wind climates is also investigated.

Main body of abstract

Correlation between environmental variables is demonstrated to have a significant effect on the accuracy class result for the RSD following the field classification scheme in the draft revision of IEC-61400-12-1. Although correlation between environmental variables is shown not to significantly affect the results of the verification and application tests this is attributable to the similarity of the wind conditions between the classification, verification and application tests due to the use of the same test site in similar time periods. It is demonstrated however that correlation between environmental variables is likely to affect the results of the verification and application tests as wind conditions across the tests diverge, potentially producing increasingly unrepresentative uncertainty assessments. A method is applied that removes the duplicate uncertainty components caused by correlation between EVs from the classification resulting in a lower and it is proposed more representative uncertainty assessment for the RSD. An accuracy class result in the range 3 – 7 is obtained for the lidar after mitigation with associated uncertainty in horizontal wind speed of around 2%. Consistent results are obtained across 44 verification tests and 20 simulated application tests carried out against the classification. These place wind speed total uncertainty in the range 2-3% across the wind speed ranges tested in non-complex terrain in both unmitigated and mitigated tests.

Conclusion

Representative uncertainty assessments are a critical element in determining risk in wind energy projects. Measured wind data plays a central role in many applications in the wind energy industry including measurement of turbine power curves and resource assessment for prospective wind farm sites. It is proposed that applying the mitigation method described as part of the RSD classification methodology will produce more representative and consistent RSD uncertainty assessments across different test sites and wind conditions. This will provide benefit to the wind industry through improved project financing terms and promote the development of improved methodologies that incorporate remote sensing data.


Learning objectives
Delegates will learn about issues relating to the assessment of uncertainty in remote sensing measurements against cup anemometry in the field. They will understand the methods by which this can be achieved in a representative fashion and get an idea of typical uncertainty results for a specific RSD. This will enable them to incorporate improved uncertainty assessments into work involving RSDs and to assess the suitability of the use of RSDs in different applications.