Modeling Wind Speed Based on Fractional Ornstein-Uhlenbeck Process; Energies; Vol. 14, iss. 17
| Parent link: | Energies Vol. 14, iss. 17.— 2021.— [5561, 15 p.] |
|---|---|
| Ente Autore: | |
| Altri autori: | , , , , , |
| Riassunto: | Title screen The primary task of the design and feasibility study for the use of wind power plants is to predict changes in wind speeds at the site of power system installation. The stochastic nature of the wind and spatio-temporal variability explains the high complexity of this problem, associated with finding the best mathematical modeling which satisfies the best solution for this problem. In the known discrete models based on Markov chains, the autoregressive-moving average does not allow variance in the time step, which does not allow their use for simulation of operating modes of wind turbines and wind energy systems. The article proposes and tests a SDE-based model for generating synthetic wind speed data using the stochastic differential equation of the fractional Ornstein-Uhlenbeck process with periodic function of long-run mean. The model allows generating wind speed trajectories with a given autocorrelation, required statistical distribution and provides the incorporation of daily and seasonal variations. Compared to the standard Ornstein-Uhlenbeck process driven by ordinary Brownian motion, the fractional model used in this study allows one to generate synthetic wind speed trajectories which autocorrelation function decays according to a power law that more closely matches the hourly autocorrelation of actual data. In order to demonstrate the capabilities of this model, a number of simulations were carried out using model parameters estimated from actual observation data of wind speed collected at 518 weather stations located throughout Russia. |
| Lingua: | inglese |
| Pubblicazione: |
2021
|
| Soggetti: | |
| Accesso online: | http://earchive.tpu.ru/handle/11683/70747 https://doi.org/10.3390/en14175561 |
| Natura: | MixedMaterials Elettronico Capitolo di libro |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=666465 |
MARC
| LEADER | 00000naa0a2200000 4500 | ||
|---|---|---|---|
| 001 | 666465 | ||
| 005 | 20250215172813.0 | ||
| 035 | |a (RuTPU)RU\TPU\network\37669 | ||
| 035 | |a RU\TPU\network\36977 | ||
| 090 | |a 666465 | ||
| 100 | |a 20211227d2021 k||y0rusy50 ba | ||
| 101 | 0 | |a eng | |
| 102 | |a CH | ||
| 135 | |a drcn ---uucaa | ||
| 181 | 0 | |a i | |
| 182 | 0 | |a b | |
| 200 | 1 | |a Modeling Wind Speed Based on Fractional Ornstein-Uhlenbeck Process |f S. G. Obukhov, A. Ibrahim, D. Yu. Davydov [et al.] | |
| 203 | |a Text |c electronic | ||
| 300 | |a Title screen | ||
| 320 | |a [References: 31 tit.] | ||
| 330 | |a The primary task of the design and feasibility study for the use of wind power plants is to predict changes in wind speeds at the site of power system installation. The stochastic nature of the wind and spatio-temporal variability explains the high complexity of this problem, associated with finding the best mathematical modeling which satisfies the best solution for this problem. In the known discrete models based on Markov chains, the autoregressive-moving average does not allow variance in the time step, which does not allow their use for simulation of operating modes of wind turbines and wind energy systems. The article proposes and tests a SDE-based model for generating synthetic wind speed data using the stochastic differential equation of the fractional Ornstein-Uhlenbeck process with periodic function of long-run mean. The model allows generating wind speed trajectories with a given autocorrelation, required statistical distribution and provides the incorporation of daily and seasonal variations. Compared to the standard Ornstein-Uhlenbeck process driven by ordinary Brownian motion, the fractional model used in this study allows one to generate synthetic wind speed trajectories which autocorrelation function decays according to a power law that more closely matches the hourly autocorrelation of actual data. In order to demonstrate the capabilities of this model, a number of simulations were carried out using model parameters estimated from actual observation data of wind speed collected at 518 weather stations located throughout Russia. | ||
| 461 | |t Energies | ||
| 463 | |t Vol. 14, iss. 17 |v [5561, 15 p.] |d 2021 | ||
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a wind energy | |
| 610 | 1 | |a wind speed model | |
| 610 | 1 | |a stochastic differential equations | |
| 610 | 1 | |a fractional Brownianmotion | |
| 610 | 1 | |a time-series modeling | |
| 610 | 1 | |a энергия ветра | |
| 610 | 1 | |a скорость ветра | |
| 610 | 1 | |a дифференциальные уравнения | |
| 610 | 1 | |a броуновское движение | |
| 610 | 1 | |a моделирование | |
| 610 | 1 | |a временные ряды | |
| 701 | 1 | |a Obukhov |b S. G. |c specialist in the field of electric power engineering |c Associate Professor of Tomsk Polytechnic University, Candidate of technical sciences |f 1963- |g Sergey Gennadievich |3 (RuTPU)RU\TPU\pers\37391 |9 20309 | |
| 701 | 1 | |a Ibrahim |b A. |g Ahmed | |
| 701 | 1 | |a Davydov |b D. Yu. |c Specialist in the field of electric power engineering |c Assistant of the Department of Tomsk Polytechnic University |f 1990- |g Denis Yurjevich |3 (RuTPU)RU\TPU\pers\47062 | |
| 701 | 1 | |a Alharbi |b T. |g Talal | |
| 701 | 1 | |a Ahmed |b E. |g Emad | |
| 701 | 1 | |a Ali |b Z. M. |g Ziad | |
| 712 | 0 | 2 | |a Национальный исследовательский Томский политехнический университет |b Инженерная школа энергетики |b Отделение электроэнергетики и электротехники |3 (RuTPU)RU\TPU\col\23505 |
| 801 | 2 | |a RU |b 63413507 |c 20220513 |g RCR |g RCR | |
| 856 | 4 | |u http://earchive.tpu.ru/handle/11683/70747 | |
| 856 | 4 | |u https://doi.org/10.3390/en14175561 | |
| 942 | |c CF | ||