Stochastic Models of Particle Trajectories in Turbulent Atmosphere Flows by Using the LANGEVIN Equations

Proceedings ◽  
2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Youssra El Qasemy ◽  
Abdelfatah Achahbar ◽  
Abdellatif Khamlichi
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Classical Method ◽  
Degradation Mechanism ◽  
Transformation Process ◽  
Langevin Equations ◽  
Turbulence Data ◽  
Power Curves ◽  
And Diffusion ◽  
Fluctuating Wind

The stochastic behavior of wind speed is a particular characteristic of wind energy production, which affects the degradation mechanism of the turbine, resulting in stochastic charging on the wind turbine. A model stochastic is used in this study to evaluate the efficiency of wind turbine power of whatever degree given fluctuating wind turbulence data. This model is based on the Langevin equations, which characterize, by two coefficients, drift and diffusion functions. These coefficients describe the behavior of the transformation process from the input wind speed to the output data that need to be determined. For this present work, the computation of drift and diffusion functions has been carried out by using the stochastic model to assess the output variables in terms of the torque and power curves as a function of time, and it is compared by the classical method. The results show that the model stochastic can define the efficiency of wind turbine generation more precisely.

scholarly journals Dynamic response improvement of hybrid system by implementing ANN-GA for fast variation of photovoltaic irradiation and FLC for wind turbine

2015 ◽  
Vol 64 (2) ◽  
pp. 291-314 ◽  
Author(s):  
Maziar Izadbakhsh ◽  
Alireza Rezvani ◽  
Majid Gandomkar
Keyword(s):  
Fuzzy Logic ◽  
Wind Speed ◽  
Dynamic Response ◽  
Wind Turbine ◽  
Hybrid System ◽  
Pitch Angle ◽  
Fuzzy Logic Controller ◽  
Control Method ◽  
Control Technique ◽  
Power Curves

Abstract In this paper, dynamic response improvement of the grid connected hybrid system comprising of the wind power generation system (WPGS) and the photovoltaic (PV) are investigated under some critical circumstances. In order to maximize the output of solar arrays, a maximum power point tracking (MPPT) technique is presented. In this paper, an intelligent control technique using the artificial neural network (ANN) and the genetic algorithm (GA) are proposed to control the MPPT for a PV system under varying irradiation and temperature conditions. The ANN-GA control method is compared with the perturb and observe (P&O), the incremental conductance (IC) and the fuzzy logic methods. In other words, the data is optimized by GA and then, these optimum values are used in ANN. The results are indicated the ANN-GA is better and more reliable method in comparison with the conventional algorithms. The allocation of a pitch angle strategy based on the fuzzy logic controller (FLC) and comparison with conventional PI controller in high rated wind speed areas are carried out. Moreover, the pitch angle based on FLC with the wind speed and active power as the inputs can have faster response that lead to smoother power curves, improving the dynamic performance of the wind turbine and prevent the mechanical fatigues of the generator

scholarly journals Application of the Nacelle Transfer Function by a Nacelle-Mounted Light Detection and Ranging System to Wind Turbine Power Performance Measurement

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1087 ◽  
Author(s):  
Dongheon Shin ◽  
Kyungnam Ko
Keyword(s):  
Wind Speed ◽  
Transfer Function ◽  
Performance Measurement ◽  
Wind Turbine ◽  
Cumulative Distribution ◽  
Light Detection And Ranging ◽  
Power Curve ◽  
Power Performance ◽  
Light Detection ◽  
Power Curves

To examine the applicability of the nacelle transfer function (NTF) derived from nacelle light detection and ranging (LIDAR) measurements to wind turbine power performance testing without a met mast, wind turbine power performance measurement was carried out at the Dongbok wind farm on Jeju Island, South Korea. A nacelle LIDAR was mounted on the nacelle of a 2-MW wind turbine to measure wind conditions in front of the turbine rotor, and an 80-m-high met mast was installed near another wind turbine to measure the free-stream wind speed. The power measurement instruments were installed in the turbine tower base, and wind speeds measured by the nacelle anemometer of the turbine were collected by the SCADA (Supervisory control and data acquisition) system. The NTF was determined by the table method, and then the power curve drawn using the NTF by the nacelle LIDAR (PCNTF, NL) was compared with the power curves drawn in compliance with International Electrotechnical Commission (IEC) standards, 61400-12-1 and 61400-12-2. Next, the combined standard uncertainties of the power curves were calculated to clarify the magnitude of the components of the uncertainties. The uncertainties of annual energy production (AEP) were also estimated by assuming that wind speed is a Rayleigh cumulative distribution. As a result, the PCNTF, NL was in good agreement with the power curves drawn in accordance with the IEC standards. The combined standard uncertainty of PCNTF, NL was almost the same as that of the power curve based on IEC 61400-12-2.

scholarly journals Nacelle power curve measurement with spinner anemometer and uncertainty evaluation

2017 ◽  
Vol 2 (1) ◽  
pp. 97-114 ◽  
Author(s):  
Giorgio Demurtas ◽  
Troels Friis Pedersen ◽  
Rozenn Wagner
Keyword(s):  
Wind Speed ◽  
Transfer Function ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Energy Production ◽  
Power Curve ◽  
Power Performance ◽  
Mean Wind Speed ◽  
Set Up ◽  
Power Curves

Abstract. The objective of this investigation was to verify the feasibility of using the spinner anemometer calibration and nacelle transfer function determined on one reference wind turbine, in order to assess the power performance of a second identical turbine. An experiment was set up with a met mast in a position suitable to measure the power curve of the two wind turbines, both equipped with a spinner anemometer. An IEC 61400-12-1-compliant power curve was then measured for both wind turbines using the met mast. The NTF (nacelle transfer function) was measured on the reference wind turbine and then applied to both turbines to calculate the free wind speed. For each of the two wind turbines, the power curve (PC) was measured with the met mast and the nacelle power curve (NPC) with the spinner anemometer. Four power curves (two PCs and two NPCs) were compared in terms of AEP (annual energy production) for a Rayleigh wind speed probability distribution. For each wind turbine, the NPC agreed with the corresponding PC within 0.10 % of AEP for the reference wind turbine and within 0.38 % for the second wind turbine, for a mean wind speed of 8 m s−1.

Dynamic Response Analysis of Large Wind Turbine Blade Based on Davenport Wind Speed Model

2011 ◽  
Vol 347-353 ◽  
pp. 2330-2336
Author(s):  
Jian Ping Zhang ◽  
Dong Liang Li ◽  
Yu Liu ◽  
He Len Wu ◽  
Jian Xing Ren ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Vibration Amplitude ◽  
Wind Turbine Blade ◽  
Von Mises Stress ◽  
Growth Trend ◽  
Element Analysis ◽  
Von Mises ◽  
Fluctuating Wind

Fluctuating wind speed spectrum, which is closer to the actual working conditions, was simulated by davenport wind speed model, and displacement and stress distribution of blade under fluctuating wind speed were calculated by finite element analysis software. The numerical results indicate that the growth trend of vibration amplitude for whole blade at flapping direction is nonlinear along the wingspan. The max von-mises stress appears when the vibration amplitude of tip reaches the maximum, and it is mainly concentrated in the central part of the blade. The stress at trailing edge and tip is smaller than the central part. Above results provide a reference for the strength safety design of wind turbine blade.,Fluctuating wind speed spectrum, which is closer to the actual working conditions, was simulated by davenport wind speed model, and displacement and stress distribution of blade under fluctuating wind speed were calculated by finite element analysis software. The numerical results indicate that the growth trend of vibration amplitude for whole blade at flapping direction is nonlinear along the wingspan. The max von-mises stress appears when the vibration amplitude of tip reaches the maximum, and it is mainly concentrated in the central part of the blade. The stress at trailing edge and tip is smaller than the central part. Above results provide a reference for the strength safety design of wind turbine blade.

Novel MPPT Control in Permanent Magnet Synchronous Generator System for Battery Energy Storage

2011 ◽  
Vol 110-116 ◽  
pp. 5179-5183 ◽  
Author(s):  
Tow Leong Tiang ◽  
Dahaman Ishak
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Synchronous Generator ◽  
Maximum Power ◽  
Lead Acid Battery ◽  
Permanent Magnet Synchronous Generator ◽  
Generator System ◽  
Mppt Controller ◽  
Battery Energy ◽  
Fluctuating Wind

This paper presents a novel sensorless maximum power point tracking (MPPT) control strategy for capturing the maximum energy from the fluctuating wind speed that being used in the stand-alone small scale variable speed wind turbine generator system (VSWTGS). The generated electricity from the wind turbine systems is used to charge battery energy storage. The whole system including the wind turbine, permanent magnet synchronous generator (PMSG), power converter, filter and lead acid battery has been simulated in Matlab/SimPowerSystem simulation software. The MPPT controller is developed to function as a wind speed estimator to generate an appropriate duty cycle for controlling power MOSFET switch in of the boost converter in order to capture maximum power in variable wind speed. From the simulation results, the power converters and filters are showing good performance in charging the lead acid battery. Besides, the novel MPPT controller is capable of extracting the maximum power from the fluctuating wind speed and exhibits good performance in both steady state and transient condition.

scholarly journals Wind Turbine Power Curves Based on the Weibull Cumulative Distribution Function

2018 ◽  
Vol 8 (10) ◽  
pp. 1757 ◽  
Author(s):  
Neeraj Bokde ◽  
Andrés Feijóo ◽  
Daniel Villanueva
Keyword(s):  
Distribution Function ◽  
Wind Speed ◽  
Wind Turbine ◽  
Software Package ◽  
Cumulative Distribution Function ◽  
Cumulative Distribution ◽  
Weibull Distributions ◽  
Wind Speeds ◽  
Power Curves ◽  
Logistic Functions

The representation of a wind turbine power curve by means of the cumulative distribution function of a Weibull distribution is investigated in this paper, after having observed the similarity between such a function and real WT power curves. The behavior of wind speed is generally accepted to be described by means of Weibull distributions, and this fact allows researchers to know the frequency of the different wind speeds. However, the proposal of this work consists of using these functions in a different way. The goal is to use Weibull functions for representing wind speed against wind power, and due to this, it must be clear that the interpretation is quite different. This way, the resulting functions cannot be considered as Weibull distributions, but only as Weibull functions used for the modeling of WT power curves. A comparison with simulations carried out by assuming logistic functions as power curves is presented. The reason for using logistic functions for this validation is that they are very good approximations, while the reasons for proposing the use of Weibull functions are that they are continuous, simpler than logistic functions and offer similar results. Additionally, an explanation about a software package has been discussed, which makes it easy to obtain Weibull functions for fitting WT power curves.

Statistical calculation of thrust curve of a wind turbine based on available power curve and general specifications data

2021 ◽  
Author(s):  
Evgeny Atlaskin ◽  
Irene Suomi ◽  
Anders Lindfors
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Farm ◽  
Wind Farms ◽  
Power Curve ◽  
Wake Effect ◽  
Thrust Coefficient ◽  
Simple Method ◽  
Wind Speeds ◽  
Power Curves

<p>Power curves for a substantial number of wind turbine generators (WTG) became available in a number of public sources during the recent years. They can be used to estimate the power production of a wind farm fleet with uncertainty determined by the accuracy and consistency of the power curve data. However, in order to estimate power losses inside a wind farm due to wind speed reduction caused by the wake effect, information on the thrust force, or widely used thrust coefficient (Ct), is required. Unlike power curves, Ct curves for the whole range of operating wind speeds of a WTG are still scarcely available in open sources. Typically, power and Ct curves are requested from a WTG manufacturer or wind farm owner under a non-disclosure agreement. However, in a research study or in calculations over a multitude of wind farms with a variety of wind turbine models, collecting this information from owners may be hardly possible. This study represents a simple method to define Ct curve statistically using power curve and general specifications of WTGs available in open sources. Preliminary results demonstrate reasonable correspondence between simulated and given data. The estimations are done in the context of aggregated wind power calculations based on reanalysis or forecast data, so that the uncertainty of wake wind speed caused by the uncertainty of predicted Ct is comparable, or do not exceed, the uncertainty of given wind speed. Although the method may not provide accurate fits at low wind speeds, it represents an essential alternative to using physical Computational Fluid Dynamics (CFD) models that are both more demanding to computer resources and require detailed information on the geometry of the rotor blades and physical properties of the rotor, which are even more unavailable in open sources than power curves.</p>

Techno-Economic Analysis and Planning for the Development of Large Scale Offshore Wind Farm in India

2020 ◽  
Vol 10 (2) ◽  
pp. 257-268
Author(s):  
Mohammad Mushir Riaz ◽  
Badrul Hasan Khan
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Large Scale ◽  
Wind Farm ◽  
Offshore Wind ◽  
Maintenance Cost ◽  
Offshore Wind Farm ◽  
Emission Analysis ◽  
Suitable Site ◽  
Power Curves

Despite India's great potential for offshore wind energy development, no offshore wind farm exists in the country. This study aims to plan a large scale offshore wind farm in the south coastal region of India. Seven potential sites were selected for the wind resource assessment study to choose the most suitable site for offshore wind farm development. An optimally matched wind turbine was also selected for each site using the respective power curves and wind speed characteristics. Weibull shape and scale parameters were estimated using WAsP, openwind, maximum likelihood (MLH), and least square regression (LSR) algorithms. The maximum energy-carrying wind speed and the most frequent wind speed were determined using these algorithmic methods. The correlation coefficient (R2) indicated the efficiency of these methods and showed that all four methods represented wind data at all sites accurately; however, openwind was slightly better than MLH, followed by LSR and WAsP methods. The coastal site, Zone-B with RE power 6.2 M152 wind turbine, was found to be the most suitable site for developing an offshore wind farm. Furthermore, the financial analysis that included preventive maintenance cost and carbon emission analysis was also done. Results show that it is feasible to develop a 430 MW wind farm in the region, zone B, by installing seventy RE power 6.2 M152 offshore wind turbines. The proposed wind farm would provide a unit price of Rs. 6.84 per kWh with a payback period of 5.9 years and, therefore, would be substantially profitable.

scholarly journals Wind Turbines Optimal Operation at Time Variable Wind Speeds

2020 ◽  
Vol 10 (12) ◽  
pp. 4232
Author(s):  
Mihaela-Codruta Ancuti ◽  
Sorin Musuroi ◽  
Ciprian Sorandaru ◽  
Marian Dordescu ◽  
Geza Mihai Erdodi
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Optimal Operation ◽  
Time Variable ◽  
Total Power ◽  
Proportional Integral ◽  
Wind Speeds ◽  
Variable Wind ◽  
Power Curves ◽  
Large Moment

The wind turbine’s operation is affected by the wind speed variations, which cannot be followed by the wind turbine due to the large moment of the power plant’s inertia. The method proposed in this paper belongs to the wind turbine power curves (WTPC) approach, which expresses the power curve of the permanent magnet synchronous generator (PMSG) by a set of mathematical equations. The WTPC research papers published before now have not taken into consideration the total power plant inertia at time-variable wind speeds, when the wind turbine’s optimal operation is very difficult to be reached, and its efficiency is thus threatened. The study is based on a wind turbine having a large moment of total inertia, and demonstrates, through extensive simulation results, that the optimal values of the PMSG’s power can be determined based on the kinetic motion equation. This PMSG’s optimal power represents an ideal time-varying curve, and the wind turbine should be controlled so as to closely follow it. For this purpose, proportional integral (PI) and proportional integral derivative (PID) type-based control methods were implemented and analyzed, so that the PMSG’s power oscillations could be reduced, and the PMSG’s angular speed value made comparable to the optimal one, meaning that the wind turbine operates within the optimal operation area, and is efficient. The simulations are actually the numerical solutions obtained by using the Scientific Workplace simulation environment, and they are based on the wind speed measurements collected from a wind farm located in Dobrogea, Romania.

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