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Готові списки джерел за темами / Wind farm estimation

Добірка наукової літератури з теми "Wind farm estimation"

Автор: Grafiati

Опубліковано: 7 липня 2024

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Статті в журналах з теми "Wind farm estimation":

1

Celeska, Maja. "EQUIVALENT WIND FARM POWER CURVE ESTIMATION." Journal of Electrical Engineering and Information Technologies 2, no. 2 (2017): 105–11. http://dx.doi.org/10.51466/jeeit172105c.

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2

Celeska, Maja. "EQUIVALENT WIND FARM POWER CURVE ESTIMATION." Journal of Electrical Engineering and Information Technologies 2, no. 2 (2017): 105–11. http://dx.doi.org/10.51466/jeeit172105c.

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3

Annoni, Jennifer, Christopher Bay, Kathryn Johnson, Emiliano Dall'Anese, Eliot Quon, Travis Kemper, and Paul Fleming. "Wind direction estimation using SCADA data with consensus-based optimization." Wind Energy Science 4, no. 2 (June 20, 2019): 355–68. http://dx.doi.org/10.5194/wes-4-355-2019.

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Abstract. Wind turbines in a wind farm typically operate individually to maximize their own performance and do not take into account information from nearby turbines. To enable cooperation to achieve farm-level objectives, turbines will need to use information from nearby turbines to optimize performance, ensure resiliency when other sensors fail, and adapt to changing local conditions. A key element of achieving a more efficient wind farm is to develop algorithms that ensure reliable, robust, real-time, and efficient operation of wind turbines in a wind farm using local sensor information that is already being collected, such as supervisory control and data acquisition (SCADA) data, local meteorological stations, and nearby radars/sodars/lidars. This article presents a framework for developing a cooperative wind farm that incorporates information from nearby turbines in real time to better align turbines in a wind farm. SCADA data from multiple turbines can be used to make better estimates of the local inflow conditions at each individual turbine. By incorporating measurements from multiple nearby turbines, a more reliable estimate of the wind direction can be obtained at an individual turbine. The consensus-based approach presented in this paper uses information from nearby turbines to estimate wind direction in an iterative way rather than aggregating all the data in a wind farm at once. Results indicate that this estimate of the wind direction can be used to improve the turbine's knowledge of the wind direction. This estimated wind direction signal has implications for potentially decreasing dynamic yaw misalignment, decreasing the amount of time a turbine spends yawing due to a more reliable input to the yaw controller, increasing resiliency to faulty wind-vane measurements, and increasing the potential for wind farm control strategies such as wake steering.

4

ARINAGA, Shinji, Masaaki SHIBATA, Shigeto HIRAI, Toshiya NANAHARA, Takamitsu SATO, and Koji YAMAGUCHI. "Estimation of Fluctuating Output in Wind Farm." Proceedings of the JSME annual meeting 2004.3 (2004): 293–94. http://dx.doi.org/10.1299/jsmemecjo.2004.3.0_293.

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5

Becker, Marcus, Dries Allaerts, and Jan-Willem van Wingerden. "Ensemble-Based Flow Field Estimation Using the Dynamic Wind Farm Model FLORIDyn." Energies 15, no. 22 (November 16, 2022): 8589. http://dx.doi.org/10.3390/en15228589.

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Wind farm control methods allow for a more flexible use of wind power plants over the baseline operation. They can be used to increase the power generated, to track a reference power signal or to reduce structural loads on a farm-wide level. Model-based control strategies have the advantage that prior knowledge can be included, for instance by simulating the current flow field state into the near future to take adequate control actions. This state needs to describe the real system as accurately as possible. This paper discusses what state estimation methods are suitable for wind farm flow field estimation and how they can be applied to the dynamic engineering model FLORIDyn. In particular, we derive an Ensemble Kalman Filter framework which can identify heterogeneous and changing wind speeds and wind directions across a wind farm. It does so based on the power generated by the turbines and wind direction measurements at the turbine locations. Next to the states, this framework quantifies uncertainty for the resulting state estimates. We also highlight challenges that arise when ensemble methods are applied to particle-based flow field simulations. The development of a flow field estimation framework for dynamic low-fidelity wind farm models is an essential step toward real-time dynamic model-based closed-loop wind farm control.

6

Meglic, Antun, and Ranko Goic. "Impact of Time Resolution on Curtailment Losses in Hybrid Wind-Solar PV Plants." Energies 15, no. 16 (August 17, 2022): 5968. http://dx.doi.org/10.3390/en15165968.

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Curtailment losses for large-scale hybrid wind–solar photovoltaic (PV) plants with a single grid connection point are often calculated in 1 h time resolution, underestimating the actual curtailment losses due to the flattening of power peaks occurring in shorter time frames. This paper analyses the curtailment losses in hybrid wind–PV plants by utilising different time resolutions of wind and PV production while varying the grid cut-off power, wind/solar PV farm sizes, and shares of wind/PV capacity. Highly resolved 1 s measurements from the operational wind farm and pyranometer are used as an input to specialized wind and PV farm power production models that consider the smoothing effect. The results show that 15 min resolution is preferred over 1 h resolution for large-scale hybrid wind–PV plants if more accurate assessment of curtailment losses is required. Although 1 min resolution additionally increases the estimation accuracy over 15 min resolution, the improvement is not significant for wind and PV plants with capacity above approx. 10 MW/10 MWp. The resolutions shorter than 1 min do not additionally increase the estimation accuracy for large-scale wind and PV plants. More attention is required when estimating curtailment losses in wind/PV plants with capacity below approx. 10 MW/10 MWp, where higher underestimation can be expected if lower time resolutions are used.

7

TSUCHIYA, Manabu, Yukinari FUKUMOTO, and Takeshi ISHIHARA. "The Wind Observation and Energy Production Estimation for Offshore Wind Farm." Wind Engineers, JAWE 2008, no. 115 (2008): 119–22. http://dx.doi.org/10.5359/jawe.2008.119.

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8

S, Fredy H. Martínez, César A. Hernández S, and Fernando Martínez S. "Multivariate Wind Speed Forecasting with LSTMs for Wind Farm Performance Estimation." International Journal of Engineering and Technology 10, no. 6 (December 31, 2018): 1626–32. http://dx.doi.org/10.21817/ijet/2018/v10i6/181006025.

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9

Petkovic, Dalibor. "Estimation of wind farm efficiency by ANFIS strategy." Godisnjak Pedagoskog fakulteta u Vranju, no. 7 (2016): 91–105. http://dx.doi.org/10.5937/gufv1607091p.

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10

Farrell, W., T. Herges, D. Maniaci, and K. Brown. "Wake state estimation of downwind turbines using recurrent neural networks for inverse dynamics modelling." Journal of Physics: Conference Series 2265, no. 3 (May 1, 2022): 032094. http://dx.doi.org/10.1088/1742-6596/2265/3/032094.

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Abstract Presented in this work is a novel approach to estimate absolute lateral wake center position on the rotor plane of a waked turbine using turbine load and operating state information. The approach formulates the estimation of the absolute lateral wake position as an inverse dynamics problem and utilizes a recurrent neural network to model the inverse mapping between the wake center position and select turbine output channels. The technique is validated on experimental data collected from experiments at the Scaled Wind Farm Technology (SWiFT) facility and numerical simulations of the site in the wind farm simulator FAST.Farm. Estimator performance and analysis of optimal conditions for estimation are discussed.

Більше джерел

Дисертації з теми "Wind farm estimation":

1

Bezerra, Rufino Ferreira Paiva Eduardo. "Wind Velocity Estimation for Wind Farms." Electronic Thesis or Diss., Université Paris sciences et lettres, 2023. http://www.theses.fr/2023UPSLM046.

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Cette thèse propose des algorithmes pour estimer la vitesse et la direction du vent pour des éoliennes et des parcs éoliens.Tout d'abord, nous proposons des méthodes basées sur les données pour estimer la vitesse effective du rotor (REWS) sans nécessiter la connaissance de certains paramètres physiques de l'éolienne, qui pourraient être inconnus de l'opérateur. Nous fournissons deux méthodes basées sur les données, l'une basée sur la régression par processus gaussien et l'autre combinant la régression par processus gaussien avec un observateur grand gain.Ensuite, en nous basant sur cette estimation locale de la REWS, au niveau d'une éolinenne, nous abordons la question de l'estimation du vent en écoulement libre au niveau du parc éolien.Nous commençons par nous concentrer sur l'estimation de la vitesse du vent, pour une direction du vent connue. Pour un parc éolien de géométrie simple, nous démontrons qu'une mesure locale de la vitesse perturbée par la présence des éoliennes peut être utilisée pour estimer la vitesse du vent en écoulement libre. Nous fondons notre méthodologie d'estimation sur une modélisation simplifiée de l'effet de sillage qui consiste en des équations aux dérivées partielles hyperboliques du premier ordre en cascade, et dont la vitesse de transport est la vitesse du vent en écoulement libre. Nous proposons d'utiliser une solution analytique de ces équations, impliquant des retards de transport, pour effectuer une estimation de la mesure locale et mettre à jour l'estimation de la vitesse du vent en écoulement libre. Nous démontrons formellement la convergence de cette estimation et illustrons numériquement l'efficacité de cette méthode.Enfin, nous passons à une configuration plus générale où à la fois la vitesse et la direction du vent en écoulement libre sont inconnues. Nous proposons d'utiliser une modélisation bidimensionelle du sillage et de nous appuyer sur une méthode basée sur l'optimisation. Le problème d'identification que nous formulons se révèle être particulièrement difficile en raison de l'apparition de retards de transport, mais nous montrons comment contourner cette difficulté en considérant une valeur moyenne de l'historique de la vitesse du vent en écoulement libre. Des résultats de simulation obtenus avec le simulateur FAST.Farm illustrent l'intérêt de la méthode proposée
This thesis designs algorithms to estimate the wind speed and direction for wind turbines and wind farms.First, we propose data-based methods to estimate the Rotor Effective Wind Speed (REWS) for a single turbine without prior knowledge of certain physical parameters of the turbine that might be unknown to an operator.We provide two data-based methods, based respectively on Gaussian Process Regression (GPR) and on an combination of GPR with high-gain observers.Second, grounding on this REWS estimation at the local level of one turbine, we address the question of estimating the free-flow wind at the level of a wind farm.We start by focusing on wind speed estimation, for a given known wind direction. For a wind farm with a simple geometry, we prove that a local speed measurement disturbed by the presence of the turbines can be used to estimate the free-flow wind speed. We ground our estimation methodology on a simplified wake model, which consists of first-order hyperbolic partial differential equations, the transport speed of which is the free-flow wind speed. We propose to use an analytical solution of these equations, involving transport delays, to perform an estimate of the local measurement and to update the free-flow wind speed estimate. We formally prove the convergence of this estimate and numerically illustrate the efficiency of this method.Finally, we move to a more general setup where both the free-flow wind speed and direction are unknown. We propose to use a two-dimensional wake model and to rely on an optimization-based method. This identification problem reveals to be particularly challenging due to the appearance of transport delays, but we illustrate how to circumvent this issue by considering an average value of the free flow wind speed history. Simulation results obtained with the simulator FAST.Farm illustrate the interest of the proposed method

2

Hines, Paul. "WindSim Study of Hybrid Wind Farm in Complex Terrain." Thesis, Högskolan på Gotland, Institutionen för kultur, energi och miljö, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-216994.

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A annual nergy production analysis was undertaken to compare wind resources and annual energy production as estimated by WAsP and Windsim. Nordex Sverige AB has designed a wind farm with the help of WAsP and this study will involve the examination of this site with Windsim. Two site formations are of interest, one with the same class of turbine and one with a mix of two turbine types. The study is interested in the effect on annual energy production as estimated by the different software of employing a hybrid layout using wind turbines of different height.The results showed that whilst initial estimations of total energy production without wake losses appear very similar between WAsP and Windsim the ways in which the software are treating individual turbines within the planned farm can be quite different because of different physics. The analysis of the „hybrid‟ turbine layout showed significant increases in estimated annual energy production when a turbine with a higher tower and larger rotor diameter was used in a hybrid arrangement. Estimated annual energy losses on the turbines that were not changed in favour of a larger turbine were small. However, no great benefit in estimated turbine efficiency was achieved through the mixing of turbine types with different hub heights. The gains in annual energy production estimated by both software are however significant with increased production of 18 % across the entire farm when comparing the „hybrid‟ layout to a farm based on only the smaller of the two turbine types.

3

Hellström, Erik. "Development of a model for estimation of wind farm production losses due to icing." Thesis, Uppsala universitet, Luft-, vatten och landskapslära, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-207382.

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Wind turbines operating in cold climate are exposed to periods of icing which lowers the plantprofitability by affecting the annual production. The loss of production has two components:The first (and most important) component is reduced power during operation due to disturbedaerodynamic properties of the blades. The second component is increased standstill. During this thesis project, methods to estimate production losses of a wind farm due to icinghave been developed, as well as a software tool to facilitate the use of these methods and thepresentation of the results. A method based on common metrological data and availableproduction data was desired, as modelling ice-related losses is expensive and may be inaccurate. The methods developed are based on using measured data for each turbine, such as activepower, temperature, wind direction and wind speed, and through this data describe theindividual turbine’s performance during different conditions. Production losses were thenestimated by comparing actual and expected power output (for the given wind speed). Thethesis then expanded on this basic concept by using reanalysis and mesoscale modelled data,which offers greater variety in the way estimating the losses may be performed, as well as theoption to derive losses for periods not covered by the production data. It was also important to develop a flexible and portable method that could incorporate newseasons of data or estimate losses for different wind farms with a completely differentconfiguration of turbines. The methods are developed using data from a wind farm in northern Sweden, consisting of 40Vestas V90 turbines and constructed a few years ago. It was found that eastern position in the wind farm and turbine altitude correlates with higherice-related losses, and that easterly winds relate to higher such losses than westerly winds. Thelosses during operation were estimated to 6.4 % of annual possible production and stops due toicing to 2.1 % of the total time. The losses figures are comparable to an earlier study performedin 2011 based on the same wind farm. The possibility of anti- or deicing systems for the wind farm and the profitability of such aninvestment should be further investigated as the wind farm is expected to continue operation fortwenty years or more.

4

Novanda, Happy. "Monitoring of power quality indices and assessment of signal distortions in wind farms." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/monitoring-of-power-quality-indices-and-assessment-of-signal-distortions-in-wind-farms(403a470c-279a-4b00-94dc-eaa2507dc579).html.

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Power quality has become one of major concerns in the power industry. It can be described as the reliability of the electric power to maintain continuity operation of end-use equipment. Power quality problems are defined as deviation of voltage or current waveforms from the ideal value. The expansion plan of wind power generation has raised concern regarding how it influences the voltage and current signals. The variability nature of wind energy and the requirements of wind power generation increase the potential problems such as frequency and harmonic distortions. In order to analyze and mitigate problems in wind power generation, it is important to monitor power quality in wind farm. Therefore, the more accurate and reliable parameter estimation methods suitable for wind power generation are needed. Three parameter estimation methods are proposed in this thesis to estimate the unknown parameters, i.e. amplitude and phase angle of fundamental and harmonic components, DC component and system frequency, during the dynamic change in wind farm. In the first method, a self-tuning procedure is introduced to least square method to increase the immunity of the algorithm to noise. In the second method, nonrecursive Newton Type Algorithm is utilised to estimate the unknown parameters by obtaining the left pseudoinverse of Jacobian matrix. In the last technique, unscented transformation is used to replace the linearization procedure to obtain mean and covariance which will be used in Kalman filter method. All of the proposed methods have been tested rigorously using computer simulated data and have shown their capability to track the unknown parameters under extreme distortions. The performances of proposed methods have also been compared using real recorded data from several wind farms in Europe and have demonstrated high correlation. This comparison has verified that UKF requires the shortest processing time and STLS requires the longest.

5

Nord, Erika. "Cost estimation of wind farms' internal grids." Thesis, KTH, Elektriska energisystem, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-47831.

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When establishing new wind farms there are a lot of different stakeholders that have different demands and ambitions.The local grid in a wind farm constitutes of about 10% of the total investment cost and therefore it is of importance that it is optimized both regarding losses and costs. In a wind farm project a system analysis is ordered thatsummarizes information such as cable layout, electric data and losses. A drawback is that this analysis is given in a later part of the project, when most decisions already have been made due to permits. If the analysis shows that there are unnecessarily high losses in the system it can be too late to make changes.The aim of this Master’s thesis project is to develop a method that makes the essential calculations so that an estimation of losses and its costs together with the investment costs can be made at an early stage. The first part of the thesis consisted of developing a program using this method with the requests above to, in the next stage, compare the results the method acquires with a reference system analysis of an existing wind farm. From this comparison conclusions were made whether the method is usable and in which ways it resembles and differs from the more advanced method used in a program today.The results show that the method makes a good estimation of losses. Deviations between the developed method and the reference analysis are due to that different approaches are made when calculating certain losses and also the depth of the calculations. Furthermore there is no description of how precise the calculations in the reference report were made so approximations can be a source of error.The conclusion is that this method can be used to get an early estimation of the losses and the corresponding costs of the local grid.

6

Tücer, Renas. "INVESTIGATION OF POTENTIAL REASONS TO ACCOUNT FOR THE UNDERPERFORMANCE OF AN OPERATIONAL WIND FARM." Thesis, Uppsala universitet, Institutionen för geovetenskaper, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-299538.

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Wind farms are costly projects and prior to the construction, comprehensive wind resource assessment processes are carried out in order to predict the future energy yield with a reliable accuracy. These estimations are made to constitute a basis for the financial assessment of the project. However, predicting the future always accommodates some uncertainties and sometimes these assessments might overestimate the production. Many different factors might account for a discrepancy between the pre-construction wind resource assessment and the operational production data. This thesis investigates an underperforming wind farm in order to ascertain the reasons of a discrepancy case. To investigate the case, the relevant data and information along with the actual production data of three years are shared with the author. Prior to the construction, a wind resource assessment was carried out by an independent wind consultancy company and the work overestimated the annual energy production (AEP) by 19.1% based on the average production value of available three years. An extensive literature review is performed to identify the possible contributing causes of the discrepancy. The data provided is investigated and a new wind resource assessment is carried out. The underestimation of the wind farm losses are studied extensively as a potential reason of the underperformance. For the AEP estimations, WAsP in WindPro interface and WindSim are employed. The use of WindSim led to about 2-2.5% less AEP estimations compared to the results of WAsP. In order to evaluate the influence of long term correlations on the AEP estimations, the climatology datasets are created using the two different reanalysis datasets (MERRA and CFSR-E) as long term references. WindSim results based on the climatology data obtained using the MERRA and CFSR-E datasets as long term references overestimated the results by 10.9% and 8.2% respectively.

7

Yu, Xi. "Modelling offshore wind farm operation and maintenance with view to estimating the benefits of condition monitoring." Thesis, University of Strathclyde, 2016. http://digitool.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=27387.

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Offshore wind energy is progressing rapidly and playing an increasingly important role in electricity generation. Since the Kyoto Protocol in February 2005, Europe has been substantially increasing its installed wind capacity. Compared to onshore wind, offshore wind allows the installation of larger turbines, more extensive sites, and encounters higher wind speed with lower turbulence. On the other hand, harsh marine conditions and the limited access to the turbines are expected to increase the cost of operation and maintenance (O&M costs presently make up approximately 20-25% of the levelised total lifetime cost of a wind turbine). Efficient condition monitoring has the potential to reduce O&M costs. In the analysis of the cost effectiveness of condition monitoring, cost and operational data are crucial. Regrettably, wind farm operational data are generally kept confidential by manufacturers and wind farm operators, especially for the offshore ones. To facilitate progress, this thesis has investigated accessible SCADA and failure data from a large onshore wind farm and created a series of indirect analysis methods to overcome the data shortage including an onshore/offshore failure rate translator and a series of methods to distinguish yawing errors from wind turbine nacelle direction sensor errors. Wind turbine component reliability has been investigated by using this innovative component failure rate translation from onshore to offshore, and applies the translation technique to Failure Mode and Effect Analysis for offshore wind. An existing O&M cost model has been further developed and then compared to other available cost models. It is demonstrated that the improvements made to the model (including the data translation approach) have improved the applicability and reliability of the model. The extended cost model (called StraPCost+) has been used to establish a relationship between the effectiveness of reactive and condition-based maintenance strategies. The benchmarked cost model has then been applied to assess the O&M cost effectiveness for three offshore wind farms at different operational phases. Apart from the innovative methodologies developed, this thesis also provides detailed background and understanding of the state of the art for offshore wind technology, condition monitoring technology. The methodology of cost model developed in this thesis is presented in detail and compared with other cost models in both commercial and research domains.

8

Jones, Esther Lane. "Spatial ecology of marine top predators." Thesis, University of St Andrews, 2017. http://hdl.handle.net/10023/12278.

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Species distribution maps can provide important information to focus conservation efforts and enable spatial management of human activities. Two sympatric marine predators, grey seals (Halichoerus grypus) and harbour seals (Phoca vitulina), have overlapping ranges but contrasting population dynamics around the UK; whilst grey seals have generally increased, harbour seals have shown significant regional declines. A robust analytical methodology was developed to produce maps of grey and harbour seal usage estimates with corresponding uncertainty, and scales of spatial partitioning between the species were found. Throughout their range, both grey and harbour seals spend the majority of their time within 50 km of the coast. The scalability of the analytical approach was enhanced and environmental information to enable spatial predictions was included. The resultant maps have been applied to inform consent and licensing of marine renewable developments of wind farms and tidal turbines. For harbour seals around Orkney, northern Scotland, distance from haul out, proportion of sand in seabed sediment, and annual mean power were important predictors of space-use. Utilising seal usage maps, a framework was produced to allow shipping noise, an important marine anthropogenic stressor, to be explicitly incorporated into spatial planning. Potentially sensitive areas were identified through quantifying risk of exposure of shipping traffic to marine species. Individual noise exposure was predicted with associated uncertainty in an area with varying rates of co-occurrence. Across the UK, spatial overlap was highest within 50 km of the coast, close to seal haul outs. Areas identified with high risk of exposure included 11 Special Areas of Conservation (from a possible 25). Risk to harbour seal populations was highest, affecting half of all SACs associated with the species. For 20 of 28 animals in the acoustic exposure study, 95% CI for M-weighted cumulative Sound Exposure Levels had upper bounds above levels known to induce Temporary Threshold Shift. Predictions of broadband received sound pressure levels were underestimated on average by 0.7 dB re 1μPa (± 3.3). An analytical methodology was derived to allow ecological maps to be quantitatively compared. The Structural Similarity (SSIM) index was enhanced to incorporate uncertainty from underlying spatial models, and a software algorithm was developed to correct for internal edge effects so that loss of spatial information from the map comparison was limited. The application of the approach was demonstrated using a case study of sperm whales (Physeter macrocephalus, Linneaus 1758) in the Mediterranean Sea to identify areas where local-scale differences in space-use between groups and singleton whales occurred. SSIM is applicable to a broad range of spatial ecological data, providing a novel tool for map comparison.

9

Mendon?a, Ricardo Barros de. "Modelagem de usinas e?licas atrav?s de um processo de Markov e t?cnicas de confiabilidade para a estimativa anual da energia produzida." Universidade Federal do Rio Grande do Norte, 2009. http://repositorio.ufrn.br:8080/jspui/handle/123456789/15314.

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Made available in DSpace on 2014-12-17T14:55:43Z (GMT). No. of bitstreams: 1 RicardoBM_DISSERT.pdf: 1094194 bytes, checksum: b8e5943b9e567c5466093b97b36b90c2 (MD5) Previous issue date: 2009-12-09
This study aims to use a computational model that considers the statistical characteristics of the wind and the reliability characteristics of a wind turbine, such as failure rates and repair, representing the wind farm by a Markov process to determine the estimated annual energy generated, and compare it with a real case. This model can also be used in reliability studies, and provides some performance indicators that will help in analyzing the feasibility of setting up a wind farm, once the power curve is known and the availability of wind speed measurements. To validate this model, simulations were done using the database of the wind farm of Macau PETROBRAS. The results were very close to the real, thereby confirming that the model successfully reproduced the behavior of all components involved. Finally, a comparison was made of the results presented by this model, with the result of estimated annual energy considering the modeling of the distribution wind by a statistical distribution of Weibull
Este trabalho tem por objetivo, utilizar um modelo computacional que considera as caracter?sticas estat?sticas do vento e as caracter?sticas de confiabilidade de uma turbina e?lica, tais como taxas de falha e de reparo, representando a usina e?lica por um processo de Markov, para determina??o da estimativa anual da energia gerada e compar?-la com um caso real. Este modelo tamb?m pode ser utilizado em estudos de confiabilidade, al?m de fornecer alguns indicadores de desempenho, que ajudar?o na an?lise de viabilidade de implanta??o de uma usina e?lica, uma vez conhecida a curva de pot?ncia do aerogerador e dispondo-se de medi??es anemom?tricas da velocidade do vento. Para a valida??o deste modelo, foram feitas simula??es utilizando o banco de dados da usina e?lica de Macau da PETROBRAS. Os resultados obtidos foram bem pr?ximos do real, confirmando, assim, que o modelo reproduziu com sucesso o comportamento de todos os componentes envolvidos. Finalmente, foi feita uma compara??o dos resultados apresentados por este modelo, com o resultado da energia anual estimada considerando a modelagem do comportamento do vento por uma distribui??o estat?stica de Weibull

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阮詠修. "Estimation of Wind Energy and Maximum Power Generation of Offshore Wind Farms in Changhua Region Using Various MCP Methods." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/h385ey.

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Анотація:

碩士
國立彰化師範大學
機電工程學系
106
Taiwan is densely populated and the redevelopment of the wind farm in the onshore area is limited. Compared to the onshore wind farm, the offshore wind farm is richer in resources and has not yet been developed. It has obtained a more reliable assessment of the wind energy in the western Taiwan sea area. Follow-up on the development of wind farms in the western coast of Taiwan is even more important. The geographical position of the Taiwan Strait is unique. According to a survey conducted by an international research institute, the average wind energy density in the coastal areas of Taiwan exceeds 750 W/m^2, especially wind speeds of over 7 meters per second, which are rare in the world. With an area of 2,300 square kilometers, Changhua Offshore Wind Farm has 4GW of huge wind capacity. Accounting for 56% of the total wind power generation in Taiwan. The investment and development of an offshore wind farm is in the tens of billions to 100 billion. The assessment of wind energy at the site is particularly important, and it will be a key to investment profitability. Offshore wind power generation in Taiwan is still in its infancy. At present, there is not much research on wind power in Changhua wind farm. This paper is different from satellite observation or numerical simulation of wind energy, using the Taipower company has just completed the construction of wind energy data collected by the Meteorological Observation Tower, using a variety of MCP (Measure correlate Predict) method, to regress to Changfeng offshoe wind farm ten years of wind The data can be used to analyze the wind energy of Changhua site more accurately and objectively and estimate the wind farm power generation.

Книги з теми "Wind farm estimation":

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Suvire, Gastn Orlando, ed. Wind Farm - Technical Regulations, Potential Estimation and Siting Assessment. InTech, 2011. http://dx.doi.org/10.5772/673.

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Частини книг з теми "Wind farm estimation":

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Glazunova, Anna, and Elena Aksaeva. "State Estimation of Grid-Connected Wind Farm." In Energy Ecosystems: Prospects and Challenges, 166–77. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-24820-7_15.

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Peña-Sanchez, Y., M. Penalba, and V. Nava. "Faulty wind farm simulation: An estimation/control-oriented model." In Trends in Renewable Energies Offshore, 679–85. London: CRC Press, 2022. http://dx.doi.org/10.1201/9781003360773-76.

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Legaz, Asier, Miroslav Zivanovic, Xabier Iriarte, Aitor Plaza, and Alfonso Carlosena. "Modal Frequency and Damping Estimation of Wind Turbines: Analysis of a Wind Farm." In Lecture Notes in Civil Engineering, 595–605. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-61425-5_57.

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Wang, W., A. Kamath, H. Bihs, and C. Pákozdi. "High-efficiency wind-farm-scale wave force estimation for preliminary design of offshore wind installations." In Trends in Renewable Energies Offshore, 699–706. London: CRC Press, 2022. http://dx.doi.org/10.1201/9781003360773-78.

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Sobolewski, Robert Adam. "Implication of Availability of an Electrical System of a Wind Farm for the Farm’s Output Power Estimation." In Dependability Engineering and Complex Systems, 419–30. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39639-2_37.

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Cornejo-Bueno, L., J. Acevedo-Rodríguez, L. Prieto, and S. Salcedo-Sanz. "A Hybrid Ensemble of Heterogeneous Regressors for Wind Speed Estimation in Wind Farms." In Intelligent Distributed Computing XII, 97–106. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99626-4_9.

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Marques, Joana, Luísa Rodrigues, Maria João Silva, Joana Santos, Regina Bispo, and Joana Bernardino. "Estimating Bird and Bat Fatality at Wind Farms: From Formula-Based Methods to Models to Assess Impact Significance." In Biodiversity and Wind Farms in Portugal, 151–204. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60351-3_7.

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Rosa, Luís, Tiago Neves, Diana Vieira, and Miguel Mascarenhas. "Camera-Trapping Versus Conventional Methodology in the Assessment of Carcass Persistence for Fatality Estimation at Wind Farms." In Wind Energy and Wildlife Impacts, 165–77. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05520-2_11.

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Coelho, Ricardo. "Power Curve Estimation of Wind Farms with Imprecise Data by Fuzzy Quadratic Programming." In Computational Intelligence Methodologies Applied to Sustainable Development Goals, 175–88. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97344-5_12.

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"Wind Turbine Sound Power Estimation." In Wind Farm Noise: Measurement, Assessment, 157–79. Chichester, UK: John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781118826140.ch4.

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Тези доповідей конференцій з теми "Wind farm estimation":

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Chiodo, E., D. Lauria, and C. Pisani. "Wind farm production estimation under multivariate wind speed distribution." In 2013 International Conference on Clean Electrical Power (ICCEP). IEEE, 2013. http://dx.doi.org/10.1109/iccep.2013.6586940.

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Littler, T., B. Fox, and D. Flynn. "Measurement-based estimation of wind farm inertia." In 2005 IEEE Russia Power Tech. IEEE, 2005. http://dx.doi.org/10.1109/ptc.2005.4524432.

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Chiodo, E., and D. Lauria. "On-line estimation of wind farm transient stability." In 2009 International Conference on Clean Electrical Power (ICCEP). IEEE, 2009. http://dx.doi.org/10.1109/iccep.2009.5211968.

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Xia, Yiqing, Yosuke Matsumoto, Iman Yousefi, Kazuyoshi Oouchi, Shunsuke Kaneko, Michio Nittouji, Kenji Fujii, and Kaho Machida. "Structural Load Estimation of Downstream Wind Turbines in an Offshore Wind Farm." In ASME 2022 41st International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/omae2022-80883.

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Abstract In this study, three IEA Wind 15MW offshore reference wind turbines with monopile support structures, are placed at a distance of eight rotor diameters in the prevailing wind direction. FAST. Farm responses are simulated under different wind conditions and sea states. The outputs suggest that for downstream turbines the effect of partial wake impingement is higher on tower bending moments than on blade bending moments. The downstream wind turbines have higher energy spectra around the tower natural frequency, which may potentially affect the fatigue loads and cut down the turbine lifetime. This study provides references for structural load analysis of downstream wind turbines in large-scale fixed-bottom offshore wind farms.

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Mifsud, Michael D., Robert N. Farrugia, and Tonio Sant. "Investigating the Influence of MCP Uncertainties on the Energy Storage Capacity Requirements for Offshore Windfarms." In ASME 2019 2nd International Offshore Wind Technical Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/iowtc2019-7504.

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Abstract Recent studies have shown that the intermittency of wind energy can be mitigated by means of an energy storage system (ESS). Energy can be stored during periods of low energy demand and high wind availability to then be utilised during periods of high energy demand. Measure-Correlate-Predict (MCP) methodologies are used to predict the wind speed and direction at a wind farm candidate site, hence enabling the estimation of the power output from the wind farm. Once energy storage is integrated with the wind farm, it is no longer only a matter of estimating the power output from the windfarm, but it is also important to model the behaviour of the ESS in conjunction with the energy demand. The latter is expected to depend, amongst other factors, on the reliability of the MCP methodology used. This paper investigates how different MCP methodologies influence the projected time series behaviour and the capacity requirements of ESS systems coupled to offshore wind farms. The analysis is based on wind data captured by a LiDAR system installed at a coastal location and from the Meteorological Office at Malta International Airport in the Maltese Islands. Different MCP methodologies are used to generate wind speed and direction time series at a candidate offshore wind farm site for various array layouts. The latter are then used in WindPRO® to estimate the time series power production for each MCP methodology and wind farm layout. This is repeated with actual wind data, such that the percentage error in energy yield from each MCP methodology is quantified, and the more reliable methodology could be identified. While it is evident that the integration of storage will reduce the need for wind energy curtailment, the reliability of the MCP methodology used is found to be crucial for proper estimation of the behaviour of the ESS.

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Tizgui, Ijjou, Hassane Bouzahir, Fatima El Guezar, and Brahim Benaid. "Estimation of electricity production for a Moroccan wind farm." In 2016 5th International Conference on Electronic Devices, Systems and Applications (ICEDSA). IEEE, 2016. http://dx.doi.org/10.1109/icedsa.2016.7818555.

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Li, Honglin, Cong Feng, and Jie Zhang. "A Multi-Fidelity Gaussian Process Regression Method for Probabilistic Wind Farm Power Curve Estimation." In ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/detc2023-114762.

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Abstract Accurate estimation of the power curve for wind turbines or wind farms is crucial to ensure their efficient operation and management. However, conventional methods for power curve estimation rely either on expensive and infrequent measurements or on low-quality numerical simulations. Moreover, the majority of previous studies on power curve estimation for wind turbines or wind farms focused on deterministic estimation, which provides a point estimate of the relationship between wind speed and power generation. Nevertheless, the deterministic approach fails to consider the inherent uncertainty associated with wind energy production resulting from varying turbine characteristics. This can lead to inaccurate power generation estimation and suboptimal decisions regarding energy management. In this paper, a kernel density estimation (KDE) based Multi-Fidelity Gaussian Process Regression (MFGPR) model is proposed to fuse theoretical power curve data and the ground true measurements to create a mapping of wind speed and wind power. By conducting a case study on an actual wind farm in China, the efficacy of the proposed MFGPR model was demonstrated in characterizing the variability of wind power. The probabilistic MFGPR model was also able to generate confidence intervals that encompassed the measured power, thereby improving the accuracy and confidence in wind power estimation or wind resource assessment. Overall, the proposed MFGPR model offers a reliable approach to integrate high-fidelity ground measurements and theoretical power curve data, resulting in precise wind resource assessment and power estimation.

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Groch, Matthew, and Hendrik J. Vermeulen. "Multi-Point Locational Wind Speed Estimation from Meso-Scale Wind Speeds for Wind Farm Applications." In 2019 9th International Conference on Power and Energy Systems (ICPES). IEEE, 2019. http://dx.doi.org/10.1109/icpes47639.2019.9105445.

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Nath, Angshu Plavan, Santanu Paul, Zakir Hussain Rather, and Sadhan Mahapatra. "Estimation of Offshore Wind Farm Reliability Considering Wake Effect and Wind Turbine Failure." In 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia). IEEE, 2019. http://dx.doi.org/10.1109/isgt-asia.2019.8880887.

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Karami, Farzad, Yujie Zhang, Mario A. Rotea, Federico Bernardoni, and Stefano Leonardi. "Real-time Wind Direction Estimation using Machine Learning on Operational Wind Farm Data." In 2021 60th IEEE Conference on Decision and Control (CDC). IEEE, 2021. http://dx.doi.org/10.1109/cdc45484.2021.9683613.

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