A Mathematical model to estimate the wind power using three parameter Weibull distribution

Seshaiah, C. V.; Sukkiramathi, K.

doi:10.12989/was.2016.22.4.393

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…Hence, in this study, a stand-alone wind-driven RO plant without a battery is considered. The available wind speed and its associated wind power generated from 100 turbines are shown in Figure 1 using a typical wind power model [28,29]. The profile of the wind power shown in Figure 1b will be utilized in the entire simulation presented in this study.…”

Section: Description Of the Desalination Plantmentioning

confidence: 99%

Optimal Control of a Reverse Osmosis Plant for Brackish Water Desalination Driven by Intermittent Wind Power

Esrafili

2022

Membranes

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This work addresses the design of an online control system that continuously regulates a reverse osmosis (RO) desalination plant driven by wind power aided by water storage tanks. The control objective is to produce the exact hourly water demand in the presence of wind power intermittency, disturbances, and operational limitations. The manipulated variables are the RO feed pressure and the active number of RO vessels. The control system helped to decrease the annual water deficit by 20% under nominal conditions and when the plant is under the influence of disturbances. Moreover, the control system managed to decrease the annual water deficit by 73% when the plant operated under a shortage of an active number of wind turbines and RO vessels. The loss of redistributed production ratio (LPb) and the loss of raw production ratio (LP) were used as the controlled variables representing the proposed control objective. LPb was superior to LP by creating conservative control actions that produce the required water demand without violating the required water purity.

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Section: Description Of the Desalination Plantmentioning

confidence: 99%

Optimal Control of a Reverse Osmosis Plant for Brackish Water Desalination Driven by Intermittent Wind Power

Esrafili

2022

Membranes

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“…The Weibull distribution can describe wind speed probability distribution with the single-peaked type relatively accurately. However, in areas covered by an oceanic climate, the actual wind speed probability distribution would likely have a double-peak shape [15]. A single distribution model, such as the Weibull with only one peak, does not accurately fit the actual double-peak wind speed distribution data.…”

Section: Introductionmentioning

confidence: 99%

A novel method for studying the wind speed probability distribution and estimating the average wind energy density

Wang,

Zhang

2024

Eng. Res. Express

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Traditional distribution models generally have large fitting errors at low wind speeds and poor fitting effects at multi-peak wind speed distributions. In this paper, a novel approach is proposed to fit different wind speed distributions, introducing a Gumbel distribution into common hybrid distribution models. The model parameters are solved by a combination of snake optimizer and nonlinear least squares (SO-NLS), using the optimal values obtained by the nonlinear least squares method as a set of initial input vectors for the snake optimizer. Simulation experiments were conducted using multi-peak wind speed distribution datasets with varying characteristics, comparing the fitting performance of the improved hybrid models against the conventional Weibull, Normal, and Rayleigh hybrid models. The results show that the proposed approach improved the model fit effects, particularly at low wind speeds, in all five experimental datasets. In most cases, the overall fitting effects were also improved. Furthermore, the validity and superiority of the improved hybrid models were further verified by comparing the estimated average wind energy density. Meanwhile, the experimental results also verified that SO-NLS not only yielded better optimization results but also accelerated the convergence speed than the snake optimizer. The improvements presented in this study effectively address the problem of large fitting errors at the low wind speed sections of the distribution, providing a theoretical basis for wind farm planning and design.

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“…During a short period (e.g., 15 min) and in all locations of the atmospheric surface layer, it is important to know the range of wind speed values as well as the probability that the wind speed will exceed a limit. These have, for example, practical applications in the design and operation of wind turbines (e.g., [1]). The reliable estimation of extreme wind speeds is important for wind energy applications, such as when the wind speed exceeds a limit then it is undesirable, because wind farms provide negligible power for wind values above their cut-off limits.…”

Section: Introductionmentioning

confidence: 99%

Development of an Algorithm for Prediction of the Wind Speed in Renewable Energy Environments

Efthimiou

Barmpas

Tsegas

et al. 2021

Fluids

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The aim of this work is to develop an algorithm that is able to provide predictions of wind speed statistics (WSS) in renewable energy environments. The subject is clearly interesting, as predictions of storms and extreme winds are important for decision makers and emergency response teams in renewable energy environments, e.g., in places where wind turbines could be located, including cities. The goal of the work is achieved through two phases: (a) During the preparation phase, the construction of a big WSS database based on computational fluid dynamics (CFD) is carried out, which includes flow fields of different wind directions in all grid numerical points; (b) In the second phase, the algorithm is used to find the records in the WSS database with the closest meteorological conditions to the meteorological conditions of interest. The evaluation of the CFD model (including both RANS and LES turbulence methodologies) is performed using the experimental data of the MUST (Mock Urban Setting Test) wind tunnel experiment.

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A Mathematical model to estimate the wind power using three parameter Weibull distribution

Cited by 6 publications

References 13 publications

Optimal Control of a Reverse Osmosis Plant for Brackish Water Desalination Driven by Intermittent Wind Power

Optimal Control of a Reverse Osmosis Plant for Brackish Water Desalination Driven by Intermittent Wind Power

A novel method for studying the wind speed probability distribution and estimating the average wind energy density

Development of an Algorithm for Prediction of the Wind Speed in Renewable Energy Environments

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