Comparison of logistic functions for modeling wind turbine power curves

Villanueva, Daniel; Feijóo, Andrés

doi:10.1016/j.epsr.2017.10.028

Cited by 66 publications

(57 citation statements)

References 23 publications

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“…Because the shape of WTPC is similar to that of the cumulative logistic distribution function, the WTPC can be described as a logistic function using four parameters, 6,16 shown as Equation 3:…”

Section: Four-parameter Logistic Modelmentioning

confidence: 99%

Wind turbine generation performance monitoring with Jaya algorithm

et al. 2019

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Summary Wind turbine (WT) power curves effectively reflect the generation performance of WTs and depict the relationship between the wind speed and the WT power output. This paper aims at developing an effective method for learning the intrinsic representations of WT power curves, which are robust to external environmental changes. Based on the obtained representations, WT generation performance is monitored. In the proposed approach, data of the supervisory control and data acquisition (SCADA) system is employed to derive the representations. Parametric models of WT power curves are developed using the two‐parameter and four‐parameter logic models. The parameters of these model are identified via Jaya algorithm. To detect the changes of WT power curve model parameters over different time, multivariate control charts are employed. The effectiveness of the proposed WT generation performance monitoring approach is validated based on SCADA data collected from real commercial WTs.

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Section: Four-parameter Logistic Modelmentioning

confidence: 99%

Wind turbine generation performance monitoring with Jaya algorithm

et al. 2019

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“…In [36] the use of single logistic curve and logistic component analysis was presented focusing on the coherence between model, data and interpretation. In technical applications logistic functions are used for example in modeling a dependence of technical or exploitation parameters, e.g., power curve for wind turbine in relation to wind speed [37].…”

Section: Technology Diffusionmentioning

confidence: 99%

Long-Term Projection of Renewable Energy Technology Diffusion

2019

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The EU aims at increasing the use of renewable energy sources (RES), mainly solar-photovoltaic (PV) and wind technologies. Projecting the future, in this respect, requires a long-term energy modeling which includes a rate of diffusion of novel technologies into the market and the prediction of their costs. The aim of this article has been to project the pace at which RES technologies diffused in the past or may diffuse in the future across the power sector. This analysis of the dynamics of technologies historically as well as in modeling, roadmaps and scenarios consists in a consistent analysis of the main parameters of the dynamics (pace of diffusion and extent of diffusion in particular markets). Some scenarios (REMIND, WITCH, WEO, PRIMES) of the development of the selected power generation technologies in the EU till 2050 are compared. Depending on the data available, the learning curves describing the expected development of PV and wind technologies till 2100 have been modeled. The learning curves have been presented as a unit cost of the power versus cumulative installed capacity (market size). As the production capacity increases, the cost per unit is reduced thanks to learning how to streamline the manufacturing process. Complimentary to these learning curves, logistic S-shape functions have been used to describe technology diffusion. PV and wind generation technologies for the EU have been estimated in time domain till 2100. The doubts whether learning curves are a proper method of representing technological change due to various uncertainties have been discussed. A critical analysis of effects of the commonly applied models for a long-term energy projection (REMIND, WITCH) use has been conducted. It has been observed that for the EU the analyzed models, despite differences in the target saturation levels, predict stagnation in the development of PV and wind technologies from around 2040. Key results of the analysis are new insights into the plausibility of future deployment scenarios in different sectors, informed by the analysis of historical dynamics of technology diffusion, using to the extent possible consistent metrics.

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“…That can be thought of as it is an average of a stochastic process with a normal distribution. All the more that logistic functions suit very accurately for the curvilinearity [4,9,11,12,14]. That is why the curvilinearity is going to be generally described with exponents.…”

Section: An Exponential Model Of the Wind Turbine Power Curvementioning

confidence: 99%

“…If a sizeable number of training and testing data is available, then non-parametric techniques based on data mining techniques and neural networks perform well. The performance of the wind turbine power curve modeled using four and five parameter logistic expressions is reported to outperform the linearized segmented model and the models based on neural network, fuzzy logic and data mining algorithms [9,11,13,14]. Thus, it is expected that such a outperformance shall exist for poorer initial and antecedent data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Wind Turbine Power Curve Exponential Model with Differentiable Cut-in and Cut-out Parts

Romanuke

2018

Nauk. Visti KPI

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WIND TURBINE POWER CURVE EXPONENTIAL MODEL WITH DIFFERENTIABLE CUT-IN AND CUT-OUT PARTSBackground. The main characteristic of a wind turbine is its power curve. Getting measurement data off powerful wind turbines is a way harder than measuring characteristics of wind turbines for individual/home use. A yet significant gap is that all wind turbines have a few similarities in their power curves but they do not have a formalized description, which could help in selecting better turbines fitting specific areas (without precise measurements in a vicinity of cut-in and cut-out speeds).Objective. As there is a straight lack of mathematical description of wind turbine power curves, the goal is to obtain a model of such curves. Methods. A power curve is of seven parts. Factual power curves remotely remind trapezia with curvilinear flanks. Because of inertia, the curvilinearity is severer for those wind turbines whose output power is greater. As blades of industrial wind turbines are too massive, their inertia makes those lag effects, that could be modeled by using natural smoothness of power curves. For describing that smoothness along with the curvilinearity, we use two increasing and two decreasing exponential functions for the flanks. Results. A wind turbine power output function consists of two zero parts, one rated-out part, and the suggested four exponential parts. The cut-in parts are described with two increasing exponential functions whose exponential growth factors are equal. The cut-out parts are described with two decreasing exponential functions whose exponential decrease factors are equal also. Such equal factors ensure strong differentiability of the power curve within those parts.Conclusions. The exponential model is for a general description of the wind turbine power curve. Having differentiable cut-in and cut-out parts, it suggests the "natural smoothing" that happens in reality due to highly-inertial wind turbine blades. The model is not necessarily to be used to fit some experimental data, but rather for patterning power curves.

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Comparison of logistic functions for modeling wind turbine power curves

Cited by 66 publications

References 23 publications

Wind turbine generation performance monitoring with Jaya algorithm

Wind turbine generation performance monitoring with Jaya algorithm

Long-Term Projection of Renewable Energy Technology Diffusion

Wind Turbine Power Curve Exponential Model with Differentiable Cut-in and Cut-out Parts

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