Offshore wind turbine clutter characteristics and identification in operational C‐band weather radar measurements

Miguel A. Rico‐Ramirez,Will Hall,Stefan Krämer

doi:10.1002/qj.2959

Abstract

The aim of this work is to update ground‐clutter classification methods in weather radar rainfall measurements to more accurately identify clutter pixels from wind farms. Measurements from two dual‐polarised weather radars, based in the United Kingdom, will be used to determine the characteristics of multiple wind farms in the North Sea and the Irish Sea. Currently 21 of the top 25 largest offshore wind farms are located in these regions. The extensive area occupied by the wind farms creates problems for weather radars located in the neighbouring European countries. Datasets of wind‐farm, precipitation and ground‐clutter pixels were aggregated from Thurnham Radar measurements to form novel membership functions that can be used in a fuzzy logic classification system to identify wind‐farm clutter. When only ground‐clutter datasets were used for classification, areas of the radar scans taken up by wind‐farm clutter were misclassified as rainfall. The inclusion of wind‐farm measurements led to an increase in the ability of the algorithm to detect these pixels as clutter, as the Heidke Skill Score increased from 67.4 to 97.8%. However there was a slight increase in the number of precipitation pixels incorrectly classified as clutter, with the false alarm rate increasing from 0.05 to 1.24% when all variables are used. The algorithm performed slightly better when applied to another radar on Hameldon Hill, showing promise for application to the UK network without recalibration of membership functions.

Highlights

The North Sea contains two of the top three largest wind farms worldwide, called the London Array and Greater Gabbard
Turbines within these offshore farms reach heights of 150 m including the blades and motor hub. These can cause substantial problems to weather radars due to backscatter effects from the radar main beam and side lobes leading to the appearance of nonmeteorological echoes known as clutter (Harrison, 2012). This effect is compounded by the vast size of the farms with the London Array having an area of 100 km2 leading to the contamination of over 200 pixels (1◦ by 600 m size) in scans from the nearest weather radar, located in Thurnham, near London, United Kingdom
Methods using an amalgamation of Sea Clutter (SC), Ground Clutter (GC) and Wind Farm clutter (WF) in Probability Density Functions (PDF) are compared to using GC and WF amalgamated with SC separate in PDFs

Summary

Introduction

The North Sea contains two of the top three largest wind farms worldwide, called the London Array and Greater Gabbard. Hood et al (2010) utilise fuzzy logic in addition to distinguishing features from Doppler radar data including spectral flatness and clutter phase alignment to identify the wind turbine locations, even during anomalous propagation They mention that the step would be to analyse the effectiveness of polarimetric measurements for wind-farm identification. Hubbert et al (2009) developed a fuzzy logic algorithm to remove standard propagation and anomalous propagation ground-clutter echoes using a novel clutter mitigation decision approach; they mention that this could not be applied to other clutter types, such as wind turbines Some algorithms, such as Berenguer et al (2006), apply a mask over the known areas of sea and land in order to use different algorithms for the two masks. The aim is to create a robust algorithm based on fuzzy logic to quickly classify the wind-farm clutter pixels in real time that can be applied to other operational weather radars within the UK network

Data and statistical methods

Classifying wind-farm clutter

Clutter in multiple elevations

Thurnham validation

Findings

Hameldon Hill validation