A new wake-merging method for wind-farm power prediction in presence of heterogeneous background velocity fields

TL;DR

This paper introduces a novel wake-merging method that accounts for heterogeneous background velocity fields in wind-farm power prediction, improving accuracy over traditional homogeneous assumptions.

Contribution

The study develops a momentum-conserving wake-merging technique that integrates with various wake models and outperforms existing methods in heterogeneous conditions.

Findings

The new method performs similarly to linear superposition in homogeneous conditions.

It shows improved accuracy with spatially varying background velocities.

Coupled with Gaussian and double-Gaussian models, it yields the most precise predictions.

Abstract

Many wind farms are placed near coastal regions or in proximity of orographic obstacles. The meso-scale gradients that develop in these zones make wind farms operating in velocity fields that are rarely uniform. However, all existing wake-merging methods in engineering wind-farm wake models assume a homogeneous background velocity field in and around the farm, relying on a single wind-speed value usually measured several hundreds of meters upstream of the first row of turbines. In this study, we derive a new momentum-conserving wake-merging method capable of superimposing the waked flow on a heterogeneous background velocity field. We couple the proposed wake-merging method with four different wake models, i.e. the Gaussian, super-Gaussian, double-Gaussian and Ishihara model, and we test its performance against LES data, dual-Doppler radar measurements and SCADA data from the Horns Rev, London Array, and Westermost Rough farm. Next to this, as an additional point of reference, the standard Jensen model with quadratic superposition is also included. Results show that the new method performs similarly to linear superposition of velocity deficits in homogeneous conditions but it shows better performance when a spatially varying background velocity is used. The most accurate estimates are obtained when the wake-merging method is coupled with the double-Gaussian and Gaussian single-wake model. The Ishihara model also shows good agreements with observations. In contrast to this, the Jensen and super-Gaussian wake model underestimate the farm power output for all wind speeds, wind directions and wind farms considered in our analysis.