Oceanographic HF Radar Data Preservation in Wind Turbine

Trockel, J.; Trockel, D.; Whelan, C.; Updyke, T.; Roarty, H.; Taylor, P.; Muglia, M. (2025). Oceanographic HF Radar Data Preservation in Wind Turbine. Report by CODAR Ocean Sensors. Report for New York State Energy Research and Development Authority (NYSERDA).

@techreport{Trockel-2025-2082822,
author = {Trockel, J and Trockel, D and Whelan, C and Updyke, T and Roarty, H and Taylor, P and Muglia, M},
title = {Oceanographic HF Radar Data Preservation in Wind Turbine},
institution = {CODAR Ocean Sensors},
year = {2025},
month = {sep},
address = {Albany, United States},
url = {https://nationaloffshorewind.org/projects/oceanographic-hf-radar-data-preservation-in-wind-turbine-interference-mitigation/},
keywords = {Wind Energy, Fixed Offshore Wind, Floating Offshore Wind, Navigation},
}

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TY - RPRT
TI - Oceanographic HF Radar Data Preservation in Wind Turbine
AU - Trockel, J
AU - Trockel, D
AU - Whelan, C
AU - Updyke, T
AU - Roarty, H
AU - Taylor, P
AU - Muglia, M
AB - This report summarizes the key findings of this National Offshore Wind Research and Development Consortium-funded project to mitigate offshore wind turbine interference (WTI) in oceanographic radars. Evidence and analysis of each of the findings can be found in the accompanying Appendices.This WTI mitigation research and development has shed new light on the effects of WTI on high frequency (HF) radar networks, specifically the long-range systems operating in the 4.4 – 5.3 MHz band, and new mitigation methods have been developed that overcome shortcomings of previous methodologies. Furthermore, the funded mitigation efforts have improved CODAR’s WTI simulation tool through extensive testing and comparison with WTI found in radar data at Block Island.The key findings of this project include:Increasing the geometric redundancy in surface current measurements (i.e. increasing the number of observations from different directions) is the most effective way tested to mitigate WTI. Designing radar networks or adding to existing ones to increase the amount of overlapping coverage from multiple radar sites shows the greatest reduction of the effect of WTI of any WTI mitigation method tested to date.Machine learning (ML) is effective at estimating rotation rate, yaw angle, and variation in rotation rate from the WTI peaks in HF Doppler spectra for a small number of turbines.The characteristics of the WTI in HF Doppler spectra are extremely sensitive to changes in rotation rate of the wind turbines. The sensitivity of WTI characteristics to the variability of a turbine's rotation rate has blocked efforts to separate WTI from the sea echo in the frequency domain.Improved WTI simulations realistically simulate changing rotation rates within the spectral integration period.Improved WTI simulations can be used to augment datasets to train ML models for flagging.A more robust and dependable WTI flagging method is achieved by combining ML model techniques with previously developed analytical techniques (Trockel et al. 2021) to estimate turbine rotation rates. The combination of methods outperforms either method in isolation.Real-time software which incorporates the ML rotation rate estimate technique has been developed and tested for up to two turbines.Real-time software which suppresses range-Doppler bins flagged with WTI from current processing has been developed and tested. The software can run as part of the real-time processing at each HFR site.
CY - Albany, United States
DA - 2025/09//
PY - 2025
SP - 83
PB - CODAR Ocean Sensors
UR - https://nationaloffshorewind.org/projects/oceanographic-hf-radar-data-preservation-in-wind-turbine-interference-mitigation/
LA - English
KW - Wind Energy
KW - Fixed Offshore Wind
KW - Floating Offshore Wind
KW - Navigation
ER -

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Abstract

This report summarizes the key findings of this National Offshore Wind Research and Development Consortium-funded project to mitigate offshore wind turbine interference (WTI) in oceanographic radars. Evidence and analysis of each of the findings can be found in the accompanying Appendices.

This WTI mitigation research and development has shed new light on the effects of WTI on high frequency (HF) radar networks, specifically the long-range systems operating in the 4.4 – 5.3 MHz band, and new mitigation methods have been developed that overcome shortcomings of previous methodologies. Furthermore, the funded mitigation efforts have improved CODAR’s WTI simulation tool through extensive testing and comparison with WTI found in radar data at Block Island.

The key findings of this project include:

Increasing the geometric redundancy in surface current measurements (i.e. increasing the number of observations from different directions) is the most effective way tested to mitigate WTI. Designing radar networks or adding to existing ones to increase the amount of overlapping coverage from multiple radar sites shows the greatest reduction of the effect of WTI of any WTI mitigation method tested to date.
Machine learning (ML) is effective at estimating rotation rate, yaw angle, and variation in rotation rate from the WTI peaks in HF Doppler spectra for a small number of turbines.
The characteristics of the WTI in HF Doppler spectra are extremely sensitive to changes in rotation rate of the wind turbines. The sensitivity of WTI characteristics to the variability of a turbine's rotation rate has blocked efforts to separate WTI from the sea echo in the frequency domain.
Improved WTI simulations realistically simulate changing rotation rates within the spectral integration period.
Improved WTI simulations can be used to augment datasets to train ML models for flagging.
A more robust and dependable WTI flagging method is achieved by combining ML model techniques with previously developed analytical techniques (Trockel et al. 2021) to estimate turbine rotation rates. The combination of methods outperforms either method in isolation.
Real-time software which incorporates the ML rotation rate estimate technique has been developed and tested for up to two turbines.
Real-time software which suppresses range-Doppler bins flagged with WTI from current processing has been developed and tested. The software can run as part of the real-time processing at each HFR site.