Small wind turbines in turbulent (urban) environments: A consideration of normal and Weibull distributions for power prediction

Sunderland, Keith; Woolmington, Thomas; Blackledge, Jonathan; Conlon, Michael

doi:10.1016/j.jweia.2013.08.001

Cited by 96 publications

(56 citation statements)

References 26 publications

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“…However The TI model is currently the only benchmark with the ability to influence power prediction. The next section is a synopsis of some of the authors previous work in this area [16].…”

Section: Comparative Resultsmentioning

confidence: 99%

“…Another underlying principle on which the TI model is based is that wind speeds are considered to be normal (Gaussian) in nature within the industrial standard 10 minute sampling period [15]. The authors have previously demonstrated that this not always the case and as an alternative proposed a methodology incorporating a dynamic Weibull Probability Density Function (PDF) [16] . This limitation of the Normal Turbulence Model (NTM) can be highlighted by considering a hypothetical but typical urban datum of a 10 minute averaged wind speed of 2m/s with a TI of 50%.…”

Section: Quantification Of Turbulencementioning

confidence: 99%

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The progressive development of turbulence statistics and its impact on wind power predictability

Woolmington¹,

Sunderland²,

Blackledge³

et al. 2014

Energy

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Wind resource assessment is a critical parameter in a diverse range of considerations within the built environment. Engineers and scientists, engaging in building design, energy conservation/application and air-quality/air-pollution control measures, need to be cognisant of how the associated wind resource imposes increased complexities in their design and modelling processes. In this regard, the morphological heterogeneities within these environments, present significant challenges to quantifying the resource and its turbulent characteristics. This paper presents three aspects of turbulence assessment within the built environment. Firstly, an analysis of how turbulence is currently quantified is considered. The industry standard, Turbulent Intensity (TI) is compared with a proposed alternative metric described as Turbulent Fourier Dimension modelling (T Df ). Secondly, the application of the turbulence assessment is considered with respect to how TI affects the productivity of small/micro wind turbines in complex environments though Gaussian distribution analysis. Finally, an extended discussion on current developments such as the concept of a turbulence rose and the ongoing development of statistical modelling is presented.

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“…However The TI model is currently the only benchmark with the ability to influence power prediction. The next section is a synopsis of some of the authors previous work in this area [16].…”

Section: Comparative Resultsmentioning

confidence: 99%

Section: Quantification Of Turbulencementioning

confidence: 99%

The progressive development of turbulence statistics and its impact on wind power predictability

Woolmington¹,

Sunderland²,

Blackledge³

et al. 2014

Energy

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“…The details of technical parameters and costs of PV module are provided in Table 1 (Sunderland et al, 2013).…”

Section: Pv Systemmentioning

confidence: 99%

Optimal Planning and Design of an Environmentally Friendly Hybrid Energy System for Rural Electrification in Iraq

Aziz¹,

Khudhier²

2017

American Journal of Applied Sciences

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Current power systems create environmental effects because of the using of fossil fuels, specially coal, since CO 2 is released to the atmosphere. Contrary to conventional energy sources, renewable energy sources produce a clean energy which is in general free of pollution, environmentally sustainable and technologically efficient. There is an increased interest in solar energy that can generate electricity with no greenhouse gas emissions. Photovoltaic systems are used to convert the sunlight into electricity. The goal of this research is to design an optimum off-grid photovoltaic/diesel hybrid energy system for a remote hamlet in Al-Kuwayr region north of Iraq. HOMER software is used to implement the techno-economic and environmental feasibility study of the proposed system. It simulates all feasible system which fulfill the load of the chosen sites under specific conditions of the available renewable resources. The analysis of the optimum hybrid energy system is presented to estimate the standalone electrification and compare it with grid expansion. The simulation results show that the optimum hybrid power system consists of 25&50 kW diesel generators, 60 kW photovoltaic arrays and 40 kW converter with no batteries. The power system is capable of providing up to 64 kW peak load. Furthermore, the off-grid photovoltaic/diesel system is found to be more frugal than grid expansion.

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“…Equation 1 of this paper (taken from the IEC6400-2 standard) is based on open terrain data with wind speeds in the range 10-25 m/s [18], yet such speeds are unlikely to be experienced in urban applications. It also implicitly assumes that wind speeds have a Gaussian distribution and while Panofsy and Dutton [38] have shown this to be appropriate for open terrain applications, such an assumption is questionable within the complex urban environment [39]. Thus I 15 as a measure of the characteristic turbulence intensity for small wind turbine applications in built-environment is inappropriate and a lower characteristic intensity i.e., I 5 or I 10; should be investigated as an alternative metric for the characteristic turbulence intensity for urban wind regimes.…”

Section: Turbulence Intensitymentioning

confidence: 99%

Rooftop wind monitoring campaigns for small wind turbine applications: Effect of sampling rate and averaging period

et al. 2015

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Tabrizi, A., Whale, J., Lyons, T. and Urmee, T. (2015) Rooftop wind monitoring campaigns for small wind turbine applications:Effect of sampling rate and averaging period. AbstractSmall wind turbines are often sited in more complex environments than the open terrain sites assumed in relevant installation guidelines or in the international small wind turbine design standard IEC61400-2. The built environment is an example of such a complex environment and installation of small wind turbines on the rooftops of high buildings has been suggested by architects and project developers as a potential means of incorporating sustainable energy generation into building design. In the absence of guidelines for installing wind turbines in the built environment, two key wind measurement parameters are the rate at which a data acquisition system (DAQ) samples the sensor, and the period over which the sampled data is averaged.This paper presents the results of the effect of sampling rate and averaging period on turbulence measurements from a monitoring system on a building rooftop, in order to inform the process of developing guidelines. The results will inform the development of a Recommended Practice of wind resource assessment in the built environment, via the International Energy Agency Task 27. The key finding of the paper is that, in general, 10Hz sampling and 10 minutes averaging period give upper estimates for turbulence intensity and maximum values of the turbulence power spectra. Using these conservative values in the design of the turbine may be the best approach to ensure that the turbine can handle both the fatigue loads and resonance due to gusts. Turbulence intensity for longitudinal wind component [-] v I Turbulence intensity for lateral wind component [-] w I Turbulence intensity for vertical wind component [-]

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Small wind turbines in turbulent (urban) environments: A consideration of normal and Weibull distributions for power prediction

Cited by 96 publications

References 26 publications

The progressive development of turbulence statistics and its impact on wind power predictability

The progressive development of turbulence statistics and its impact on wind power predictability

Optimal Planning and Design of an Environmentally Friendly Hybrid Energy System for Rural Electrification in Iraq

Rooftop wind monitoring campaigns for small wind turbine applications: Effect of sampling rate and averaging period

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