Document Type

Article

Rights

This item is available under a Creative Commons License for non-commercial use only

Disciplines

2.2 ELECTRICAL, ELECTRONIC, INFORMATION ENGINEERING, 2.7 ENVIRONMENTAL ENGINEERING

Publication Details

Journal of Wind Engineering and Industrial Aerodynamics; Vol.121 (2013) p70–81

d.o.i.org/10.1016/j.jweia.2013.8.001

Abstract

The urban terrain and the associated morphological complexities therein, present significant challenges for the deployment of small wind turbines. In particular, a considerable amount of uncertainty is attributable to the lack of understanding concerning how turbulence within urban environments affects turbine productivity. Current wind turbine power output measurements (particularly for small/micro wind turbines) are based on an average wind speed over an observation period; with limited accountability of the variability of wind speed within the observation time frame. This paper however, presents two models that can instead accurately consider such wind speed variation and how it affects the turbine, based solely on the observed mean wind speed and standard deviation within successive (10 minutes) time intervals. These models are predicated on an appreciation of the industry standard metric, turbulence intensity (TI), in conjunction with the power curve of a 2.5kW wind turbine. Simple ‘look-up’ tables collating how the turbine’s power curve is affected by varying TI are used so that a novel methodology for estimating the turbine’s electrical performance is achievable. Ultimately, the two models presented afford an opportunity to provide an indicative real-world wind speed distribution based on the two standard measurements. The first approach is an adaptation of a model originally derived to quantify the degradation of power performance of wind farm turbines, using a Gaussian probability distribution to simulate turbulence (and more specifically, turbulence intensity (TI)). Such Gaussian modelling has potential however, for disproportionately high and asymptotic TI, associated, for example, with gusting within low mean wind speed observation windows. Furthermore, the approach requires an accurate turbine power curve. The second approach overcomes these limitations through the novel application of the Weibull Distribution, a widely accepted means to probabilistically describe wind speed. Both models are tested at an urban and suburban location in Dublin City, Ireland, where sonic anemometry is positioned at approximately 1.5 times the average height of buildings at the respective locations. Both observation sites represent two distinct urban landscapes. The instrumentation is positioned specific to their surrounding locations and, record the three dimensional wind vectors at a temporal resolution of 10Hz. The hypotheses presented here consider an idealised electrical performance of the turbine, with results suggesting that both approaches can replicate very accurately this idealised basis.

DOI

10.1016/j.jweia.2013.8.001

Share

COinS