A stochastic Markov chain model for simulating wind speed time series at Tangiers, Morocco

Nfaoui, H.; Essiarab, H.; Sayigh, A.A.M.

doi:10.1016/s0960-1481(03)00143-5

Cited by 83 publications

(52 citation statements)

References 5 publications

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“…For the stochastic energy model, no classical probability law could be suitably fitted to solar radiation empirical probabilities. On the other hand, the literature on climatology and renewable energy is already well established, using historical data, descriptive Markov chain models for various forms of environmental energy, such as solar radiation [9], [18], [23], wind speed [17] and ambient temperature, or autoregressive process models [22]. More exactly, [23] proposes a firstorder stationary discrete time Markov chain model for each month of the year, due to the big monthly variations, built from traces taken over a period of 20 years.…”

Section: Modeling Backgroundmentioning

confidence: 99%

Stochastic Modeling and Analysis for Environmentally Powered Wireless Sensor Nodes

Şuşu¹,

Acquaviva²,

Atienza³

et al. 2008

Proceedings of the 6th Intl Symposium on Modeling and Optimization

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Abstract-Environmental energy is becoming a feasible alternative for many low-power systems, such as wireless sensor nodes. Designing an environmentally powered device faces several challenges: choosing the exact type of the energy harvester, the energy storage elements and determining the duty cycle of the application. With harvesting, the design process becomes even more difficult because it also has to take into account the unpredictability of the energy source.The contribution of this paper is a methodology that facilitates the analysis of energy harvesting nodes. The existing modeling strategies for battery powered systems are not suitable because they do not capture the uncertainty of the power source. Also, the metrics of interest for battery powered devices are different, as opposed to the harvesting powered ones: in the former case we search to maximize the system lifetime, while in the latter case a more expressive goal is to increase the system availability.

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Section: Modeling Backgroundmentioning

confidence: 99%

Stochastic Modeling and Analysis for Environmentally Powered Wireless Sensor Nodes

Şuşu¹,

Acquaviva²,

Atienza³

et al. 2008

Proceedings of the 6th Intl Symposium on Modeling and Optimization

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“…On the other hand, there have been many research related to the stochastic property of wind speed in a specified wind farm, see [14,15,16,17], etc. Especially, [14] and [15] have analyzed the time series data of wind speed by applying the Markov process.…”

Section: Introductionmentioning

confidence: 99%

“…Especially, [14] and [15] have analyzed the time series data of wind speed by applying the Markov process. Most work only concentrate on the static information of wind speed for choosing farm, or generate wind speed for testing.…”

Section: Introductionmentioning

confidence: 99%

“…Motivated by the research of [14] and [15], the stochastic property of the average wind speed V s (t) can be presented as a Markov process, which is based on the transitional probability matrices of various time steps and sample datas. Most often, a first-order continuous-time Markov chain implies preservation of statistical parameters and especially the first-order autocorrelation coefficient in the synthetic sequences.…”

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MixedH₂/H_∞pitch control of wind turbine with a Markovian jump model

Lin

Liu

et al. 2017

International Journal of Control

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Abstract-This paper proposes a Markovian jump model and the corresponding H 2 /H ∞ control strategy for the wind turbine driven by the stochastic switching wind speed, which can be used to regulate the generator speed in order to harvest the rated power while reducing the fatigue loads on the mechanical side of wind turbine. Through sampling the low-frequency wind speed data into separate intervals, the stochastic characteristic of the steady wind speed can be represented as a Markov process, while the high-frequency wind speed in the each interval is regarded as the disturbance input. Then, the traditional operating points of wind turbine can be divided into separate subregions correspondingly, where the model parameters and the control mode can be fixed in each mode. Then, the mixed H 2 /H ∞ control problem is discussed for such a class of Markovian jump wind turbine working above the rated wind speed to guarantee both the disturbance rejection and the mechanical loads objectives, which can reduce the power volatility and the generator torque fluctuation of the whole transmission mechanism efficiently. Simulation results for a 2 MW wind turbine show the effectiveness of the proposed method.

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“…Its stochastic modelling is a complicated task because of its strong variability in time and land terrains. Over a year, wind speed is periodic, showing seasonal variations; however, hourly average wind speed is a stochastic process with a Weibull probability density function; whereas within minutes, it follows a Gaussian distribution [10].Different methods have been employed for time series characterisation of wind processes. Traditionally Weibull distribution is widely used to represent wind speed series at a given site [4,[11][12][13].…”

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Characterisation of wind speed series and power in Durban

Legesse¹,

Saha²,

Carpanen³

2017

J. energy South. Afr.

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Both the planning and operating of a wind farm demand an appropriate wind speed model of its location. The model also helps predict the dynamic behaviour of wind turbines and wind power potential in the location. This study characterises the wind speed series and power in Durban (29.9560°S, 30.9730°E) IntroductionDepletion of fossil fuel reserves, global warming, security concerns, and rising commodity prices are pushing the world to go green. Much attention is currently given to the development of renewable energy, among which harnessing wind energy is the cheapest alternative [1][2][3]. Feasibility studies related to wind power require an appropriate wind speed model of a site [4,5]. These models are also important in planning and operating wind turbines. Hence, the wind speeds of a specified site should be appropriately characterised to determine wind energy potential and attain comprehensive results in the investigations of the dynamics of the wind turbines [4,6,7]. Moreover, a good characterisation of wind speed helps transmission system operators in scheduling their power dispatch [4]. Wind is a random stochastic process whose dynamic behaviour can be represented by a stochastic model [8]. Naturally, it depends on pressure gradient, waves, jet streams, and local weather conditions [9]. Its stochastic modelling is a complicated task because of its strong variability in time and land terrains. Over a year, wind speed is periodic, showing seasonal variations; however, hourly average wind speed is a stochastic process with a Weibull probability density function; whereas within minutes, it follows a Gaussian distribution [10].Different methods have been employed for time series characterisation of wind processes. Traditionally Weibull distribution is widely used to represent wind speed series at a given site [4,[11][12][13]. Normal, Gamma, Lognormal or combination of these distributions with Weibull distribution [4,7,[14][15][16], empirical wavelet [17] or Kernel density method [18] can also be used to model wind speed series. Shokrzadeh et al. [19] and Kazemi and Goudarzi [20] employed advanced parametric and nonparametric and least square approximation methods to forecast wind power. A typical distribution may not necessarily represent the cumulative wind behaviour of all locations in a region [7]. Thus, the wind speed for a particular location needs to be modelled. The above distributions cannot be used, however, when chronology is considered [4]. A rough observation on the raw wind speed data from Durban demonstrated that the current wind speed depended on the previous wind speed, indicating that chronology should be considered in modelling the wind speeds. Evolutionary algorithms such as genetic algorithm and local search technique are also used in wind speed modelling [21] despite their time consuming procedure [4]. The Markov chain model, which retains chronology and consumes less time, could, therefore, be employed to synthesise wind speed time series for dynamic simulation and wind power f...

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A stochastic Markov chain model for simulating wind speed time series at Tangiers, Morocco

Cited by 83 publications

References 5 publications

Stochastic Modeling and Analysis for Environmentally Powered Wireless Sensor Nodes

Stochastic Modeling and Analysis for Environmentally Powered Wireless Sensor Nodes

MixedH₂/H_∞pitch control of wind turbine with a Markovian jump model

Characterisation of wind speed series and power in Durban

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A stochastic Markov chain model for simulating wind speed time series at Tangiers, Morocco

Cited by 83 publications

References 5 publications

Stochastic Modeling and Analysis for Environmentally Powered Wireless Sensor Nodes

Stochastic Modeling and Analysis for Environmentally Powered Wireless Sensor Nodes

MixedH2/H∞pitch control of wind turbine with a Markovian jump model

Characterisation of wind speed series and power in Durban

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MixedH₂/H_∞pitch control of wind turbine with a Markovian jump model