Application of Auto-Regressive Models to U.K. Wind Speed Data for Power System Impact Studies

Hill, David; McMillan, David; Bell, Keith; Infield, David

doi:10.1109/tste.2011.2163324

Cited by 159 publications

(106 citation statements)

References 26 publications

Supporting

Mentioning

106

Contrasting

Order By: Relevance

“…There are many types of wind field model being considered for use in power system impact studies; a comprehensive literature review of wind speed and wind power models is provided in [7].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Although very basic, this acts as a good reference to compare other more complex models. Vector Auto-Regressive models with detrending of annual and diurnal trends and ARMA models have managed to capture some of the seasonal and diurnal effects required of long term simulations for power system planning [4,7].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Although more detailed than statistical models, the added complexity has been considered inappropriate for power system impact studies [8].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Wind field models have tended to be validated by comparing the auto-correlation, probability density functions of the annual wind speed distributions and sometimes variability of the wind [8,9]. The Root Mean Square error (RMSe) in forecast predictions is also a common metric.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A model that can simulate data can overcome these problems [7]. Synthesised data can be used to fill previously missing data points, or replace erroneous data points, and can also be applied to different time periods or even to locations where data has not previously been recorded giving a better representation of wind speeds across countries.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Validating wind field models for power system impact studies

Plumley¹,

Hill²,

McMillan³

et al. 2012

International Conference on Sustainable Power Generation and Supply (SUPERGEN 2012)

View full text Add to dashboard Cite

To integrate wind power into the electrical power system studies need to be made of its impact. Wind plants are stochastic in nature and this makes them differ from traditional plants. One method to model the stochastic nature of wind plants is to create a wind field model and simulate wind data that can then be used to determine power outputs. There are a large number of models available, however often these models are not adequately validated. This paper proposes a number of criteria that all wind field models should be validated against and the methods that can be used to do this.

show abstract

“…There are many types of wind field model being considered for use in power system impact studies; a comprehensive literature review of wind speed and wind power models is provided in [7].…”

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

“…Although more detailed than statistical models, the added complexity has been considered inappropriate for power system impact studies [8].…”

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Validating wind field models for power system impact studies

Plumley¹,

Hill²,

McMillan³

et al. 2012

International Conference on Sustainable Power Generation and Supply (SUPERGEN 2012)

View full text Add to dashboard Cite

show abstract

Statistical Methods

2023

Handbook of Wind Resource Assessment

View full text Add to dashboard Cite

Hybrid multiscale wind speed forecasting based on variational mode decomposition

Ali

Khan

Rehman

2017

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

SummarySuccessful integration of wind energy in an electrical grid system requires prior information of the generated wind power from a turbine or a wind farm. That is a challenging task given the unpredictable, intermittent, and nonlinear nature of wind speed. We present a novel method for short-term wind speed prediction by using a hybrid approach based on variational mode decomposition (VMD) in conjunction with both linear and nonlinear prediction models. The VMD is a fully data-adaptive signal decomposition method that offers significant improvement over existing data-adaptive techniques, such as empirical mode decomposition, in terms of its sound mathematical framework and accuracy of its decomposition. In our work, VMD is used to decompose wind speed data into multiple intrinsic narrow band components to facilitate their forecasting/prediction. We report results from extensive experiments performed on a large wind speed data set obtained from multiple sites in Pakistan to demonstrate the efficacy of the proposed method.

show abstract

Application of Auto-Regressive Models to U.K. Wind Speed Data for Power System Impact Studies

Cited by 159 publications

References 26 publications

Validating wind field models for power system impact studies

Validating wind field models for power system impact studies

Statistical Methods

Hybrid multiscale wind speed forecasting based on variational mode decomposition

Contact Info

Product

Resources

About