Wind Farm Efficiency Research Articles

A Linear Theory of Wind Farm Efficiency and Interaction

Abstract We investigate the role of gravity waves (GW), farm shape, and wind direction on the efficiency and interaction of wind farms using a two-layer linearized dynamical model with Rayleigh friction. Five integrated diagnostic quantities are used: total wind deficit, the first moment of vorticity, turbine work, disturbance kinetic energy, and vertical energy flux. The work done on the atmosphere by turbine drag is balanced by dissipation of disturbance kinetic energy. A new definition of wind farm efficiency is proposed based on “turbine work.” While GWs do not change the total wind deficit or the vorticity pattern, they alter the spatial pattern of wind deficit and typically make a wind farm less efficient. GWs slow the winds upwind and reduce the wake influence on nearby downstream wind farms. GWs also propagate part of the disturbance energy upward into the upper atmosphere. We applied these ideas to the proposed 45 km × 15 km wind energy areas off the coast of New England. The proximity of these farms allows GWs to play a significant role in farm interaction, especially in winter with northwesterly winds. The governing equations are solved directly and by using fast Fourier transforms (FFT). The computational speed of the linear FFT model suggests its future use in optimizing the design and day-by-day operation of these and other wind farms. Significance Statement When a wind farm is generating electricity, the drag of the wind turbines slows the regional winds. As wind farms grow larger and more closely spaced, this wind reduction will limit the efficiency of wind farms and their economic return. In this paper we analyze an idealized mathematical model of the atmospheric response to wind farm drag including nonlocal gravity wave effects. We propose a new definition of farm efficiency based on the atmospheric disturbance that a farm creates. We also propose a fast Fourier transform (FFT) method for carrying out these estimates.

Powernet: A novel method for wind power predictive analytics using Powernet deep learning model

Sustainable energy is a significant power generation resource for a cleaner and CO2 free environment. Out of different renewable energies out there, wind energy is a rapidly growing sector and integrated into the power grid. However, uncertainty, stochastic, and non-stationary nature of meteorological features, on which wind power depends, makes it difficult to predict accurately. The efficiency of wind farms and the power grid is directly proportional to efficient wind power predictive analytics. This study describes a hybrid model named Powernet for improving the predicted accuracy in the field of wind power analytics. The improved hybrid model is a combination of Convolution 1 Dimensional (Conv-1D) and Bidirectional Long Short-Term Memory (BiLSTM) models. First, Conv-1D layers extract the spatial features of timestamped data sequentially. Then, the output generated by multiple convolution operations at the nested layers is embedded with BiLSTM to work on the temporal characteristics of wind power data. The nesting of spatial and temporal extractors generates a novel architecture, Powernet for wind power forecasting from raw data. The effectiveness of Powernet has been validated on the real-time wind power National Renewable Energy Laboratory dataset. Also, error and computational analysis have been conducted for short-term wind power forecasting with an ensemble of long short-term memory-based models. The comparative analysis demonstrates that the proposed model Powernet achieves better prediction than traditional deep learning standalone and hybrid models. Also, the statistical models are compared to show that the raw data need to be pre-processed when conventional models are applied. However, Powernet does not need the overhead of pre-processing for generating better predictions.

Diurnal impact of atmospheric stability on inter-farm wake and power generation efficiency at neighboring onshore wind farms in complex terrain

Enhancing the power production efficiency of large-scale wind farms is challenging for onshore wind power due to the variability of atmospheric conditions. This work aims to study the impact of atmospheric stability on the inter-farm wake pattern, topographic effect, and power production changes at two neighboring wind farms in Hami City, Xinjiang of China over complex terrain. The results show a discernible diurnal cycle property. At night in a stable layer with hindered mixing, the inter-farm wake persists around 20 km downstream, and the greatest relative wind deficit increases by 8%. However, during the daytime unstable layer with elevated turbulence, shows a limited deficit and recovers about 6 km downstream. Accordingly, a mean relative power production reduction of 17% is subjected to the downwind farm at night, about a factor of 3 greater than that at daytime, when the mean value is only 6%. Interestingly, the largest power production losses are experienced with a larger range between maximum and minimum before dawn and after dusk due to the notable inter-farm wake with a streaky structure related to the large coherent buoyant caused by the tendency of heat flux over the complex terrain. Moreover, the topographic speed-up in the stable layer outperforms that in unstable conditions.

Impact of Turbulent Time Scales on Wake Recovery and Operation

Enhancing wake recovery behind wind turbines has the potential to significantly improve the power production and efficiency of large wind farms. Rather than investigating turbine control strategies, floater motion or global turbulent quantities such as turbulence intensity, this work aims to study wake stability and recovery through a focus on the turbulent scales of the inflow. Using Large Eddy Simulations of a single turbine, sinusoidal streamwise forcing is applied to the inflow with a constant amplitude and mean flow velocity, but differing time scales between 80s and 140s. For all applied time scales the turbine wake is characterised by the rolling-up of the near wake into the periodic shedding of vortex rings, and an excitation of the applied forcing frequency resulting in velocity fluctuations in the wake several times larger than that at the inflow. For shorter time scales (80s - 90s) a more aggressive and earlier wake roll-up led to a shorter near wake region, faster overall recovery and significantly improved the expected power output from 6R downstream onwards. An inflow time period of 80s gave rise to more than a 50% increase in power output of a fictive downstream turbine placed at 14R downstream, compared to an inflow time scale of 140s.

Sensitivity of Wake Modelling Setups

Engineering wake modelling with single turbine wake models and wake superposition models (SupModel) requires the choice of two crucial settings: the type of wake superposition and the wake expansion rate parameter. Based on offshore wind farm measurements, the appropriate tuning of such wake modelling setups with respect to wind farm efficiency and their sensitivity to this tuning is investigated. For this purpose, universal and inflow-individual wake expansion rate parameters are optimised for different SupModels via minimising the deviation between simulated and measured power and to compare the results to simulations using a literature value. The work reveals a logarithmic, SupModel-independent sensitivity of the farm efficiency to the wake expansion rate, sufficient to allow equally accurate modelling performance for all setups if a matching parameter is chosen. The choice of the SupModel only matters in case of an unadapted wake expansion rate parameter. Beyond that, the results indicate a systematic flaw of wake models in reproducing farm power measurements for both weak and full wake situations with the same model tuning. Inflow-individual parameters obtained by this work mitigate this with a systematic pattern over wind speed and wind direction and can significantly decrease the modelling uncertainty over varying inflow conditions.

Wake properties and power output of very large wind farms for different meteorological conditions and turbine spacings: a large-eddy simulation case study for the German Bight

Abstract. Germany's expansion target for offshore wind power capacity of 40 GW by the year 2040 can only be reached if large portions of the Exclusive Economic Zone in the German Bight are equipped with wind farms. Because these wind farm clusters will be much larger than existing wind farms, it is unknown how they will affect the boundary layer flow and how much power they will produce. The objective of this large-eddy simulation study is to investigate the wake properties and the power output of very large potential wind farms in the German Bight for different turbine spacings, stabilities and boundary layer heights. The results show that very large wind farms cause flow effects that small wind farms do not. These effects include, but are not limited to, inversion layer displacement, counterclockwise flow deflection inside the boundary layer and clockwise flow deflection above the boundary layer. Wakes of very large wind farms are longer for shallower boundary layers and smaller turbine spacings, reaching values of more than 100 km. The wake in terms of turbulence intensity is approximately 20 km long, in which longer wakes occur for convective boundary layers and shorter wakes for stable boundary layers. Very large wind farms in a shallow, stable boundary layer can excite gravity waves in the overlying free atmosphere, resulting in significant flow blockage. The power output of very large wind farms is higher for thicker boundary layers because thick boundary layers contain more kinetic energy than thin boundary layers. The power density of the energy input by the geostrophic pressure gradient limits the power output of very large wind farms. Because this power density is very low (approximately 2 W m−2), the installed power density of very large wind farms should be small to achieve a good wind farm efficiency.

Pseudo-2D RANS: A LiDAR-driven mid-fidelity model for simulations of wind farm flows

Next-generation models of wind farm flows are increasingly needed to assist the design, operation, and performance diagnostic of modern wind power plants. Accuracy in the descriptions of the wind farm aerodynamics, including the effects of atmospheric stability, coalescing wakes, and the pressure field induced by the turbine rotors are necessary attributes for such tools as well as low computational costs. The Pseudo-2D RANS model is formulated to provide an efficient solution of the Navier–Stokes equations governing wind-farm flows installed in flat terrain and offshore. The turbulence closure and actuator disk model are calibrated based on wind light detection and ranging measurements of wind turbine wakes collected under different operative and atmospheric conditions. A shallow-water formulation is implemented to achieve a converged solution for the velocity and pressure fields across a farm with computational costs comparable to those of mid-fidelity engineering wake models. The theoretical foundations and numerical scheme of the Pseudo-2D RANS model are provided, together with a detailed description of the verification and validation processes. The model is assessed against a large dataset of power production for an onshore wind farm located in North Texas showing a normalized mean absolute error of 5.6% on the 10-min-averaged active power and 3% on the clustered wind farm efficiency, which represent 8% and 24%, respectively, improvements with respect to the best-performing engineering wake model tested in this work.

Large Eddy Simulation of the Layout Effects on Wind Farm Performance Coupling With Wind Turbine Control Strategies

Abstract Large eddy simulation (LES) coupling with wind turbine control strategies is newly developed to quantitatively study the layout effects on wind farm performance. The turbine rotor is parameterized with an actuator line method (ALM), and the five-region generator-torque control and the proportional-integral (PI) pitch control are newly introduced to regulate the operation of the wind turbine. First, a dynamic inflow boundary condition is designed to validate the current simulation framework. The validation results show that the simulated power curve agrees well with the real power curve of the wind turbine, and the maximum power error of the simulation only accounts for 5% of the rated power. Then, to study the layout effects, four kinds of wind farm arrangements are designed by varying the alignment method and the turbine spacing. The results show that the staggered arrangement and increasing the stream-wise spacing are beneficial to reduce the velocity deficit. The power comparison results show that the staggered arrangement has obvious advantages among the four cases, and it increases the capacity factor (CF) by 25% and improves the wind farm efficiency by about 50% compared with the aligned arrangement. The present simulation framework can be used to optimize the turbine layout for the potential wind farms.

Ocean waves could give offshore wind turbines a boost

Study of floating wind turbines in unsteady wave conditions aims to improve the efficiency and lifespan of offshore windfarms.

Enhanced Chaotic Manta Ray Foraging Algorithm for Function Optimization and Optimal Wind Farm Layout Problem

Manta ray foraging optimization (MRFO) algorithm is relatively a novel bio-inspired optimization technique directed to given real-world engineering problems. In this present work, wind turbines layout (WTs) inside a wind farm, is considered as a real nonlinear optimization problem. In spite of the better convergence of MRFO, it gets stuck into local optima for large problems. The chaotic sequences are among the performed techniques used to tackle this shortcoming and improve the global search ability. Therefore, ten chaotic maps have been embedded into MRFO. To affirm the performance of the suggested chaotic approach CMRFO, it was first assessed using the IEEE CEC-2017 benchmark functions. This examination has been systematically compared to eight well-known optimization algorithms and the original MRFO. The non-parametric Wilcoxon statistical analysis significantly demonstrates the superiority of CMRFO as it ranks first in most test suites. Secondly, the MRFO and its best enhanced chaotic version were tested on the complex problem of finding the optimal locations of wind turbines within a wind farm. Besides, The application of the CMRFO to the wind farm layout optimization (WFLO) issue aims to minimize the cost per unit power output, and increase the electrical power engendered by all WTs and wind-farm efficiency. Two representative scenarios of the problem have been dealt with a square-shaped farm installed on an area of 2x2km, including variable wind direction with steady wind speed, and both wind direction and speed are variable. The WFLO outcomes reveal the CMRFO capability to find the optimal locations of WTs, that generate a maximum power for a minimum cost compared to three stochastic approaches and other previous studies. At last, the suggested CMRFO with Singer chaotic sequence has been successfully enhanced by accelerating the convergence, and providing better accuracy to find the global optimum.

Site Selection for an Offshore Wind Power Station Under a Fuzzy Environment

The Malaysian government’s support for offshore wind power production has led to an increase in a few proposals. An important factor in the overall efficiency of any offshore wind farm is the site selection process, which is a multi-criteria decision-making (MCDM) task. However, classical MCDM techniques often fail to choose a suitable site because of three main challenges. First, compensation is regarded as a problem in the processing of information. Second, data usage and data leakage are often ignored in the decision-making process. Third, interaction difficulty in fuzzy environments is easily ignored. This study provides a framework for making site selection decisions for offshore wind farms while addressing the constraints. Fuzzy VIKOR is used in the second stage of the AHP process to analyze the site’s results with respect to evaluation criteria for offshore wind farms. A comprehensive index system, which incorporates the veto criteria and evaluation criteria for selecting offshore wind power station sites, is devised. Then, the system is used to transmit imprecise information to decision makers by using a triangular fuzzy set. Likelihood-based valued comparisons indicate that imprecise choice information can be correctly used, and issues of information loss can be logically avoided. A case study of Malaysia is used to demonstrate the validity and practicality of the site selection technique. This research offers a theoretical basis for accurate offshore wind power evaluation in Malaysia.

Offshore wind farm wake modelling using deep feed forward neural networks for active yaw control and layout optimisation

Offshore wind farm modelling has been an area of rapidly increasing interest over the last two decades, with numerous analytical as well as computational-based approaches developed, in an attempt to produce designs that improve wind farm efficiency in power production. This work presents a Machine Learning (ML) framework for the rapid modelling of wind farm flow fields, using a Deep Neural Network (DNN) neural network architecture, trained here on approximate turbine wake fields, calculated on the state-of-the-art wind farm modelling software FLORIS. The constructed neural model is capable of accurately reproducing single wake deficits at hub-level for a 5MW wind turbine under yaw and a wide range of inlet hub speed and turbulence intensity conditions, at least an order of magnitude faster than the analytical wake-based solution method, yielding results with 1.5% mean absolute error. A superposition algorithm is also developed to construct flow fields over the whole wind farm domain by superimposing individual wakes. A promising advantage of the present approach is that its performance and accuracy are expected to increase even further when trained on high-fidelity CFD or real-world data through transfer learning, while its computational cost remains low.

Measuring the long run technical efficiency of offshore wind farms

Offshore wind energy has emerged as an attractive alternative to conventional resources to meet the Paris agreement commitment. The present paper studies the long run capacity of offshore wind farms to transform kinetic energy into electricity. We start estimating the technical efficiency of twenty-six farms over a thirteen years interval using a fully parametric and a semiparametric stochastic frontier model. The latter allows the factors of production to impact non-linearly on the quantity of electricity produced, those reducing the possibility of committing a functional misspecification error. Our results suggest that fully parametric specifications fails to identify the non-linear effect of labour cost on volumes of electricity produced. Then, we regress the estimated technical efficiency over the farm age, while controlling for the technological change of the wind power industry, to single out the resilience of the technical efficiency to aging. According to our calculations, technical efficiency ranges from 83% to 98% and it does not decline with age. This result shades light on the capacity of offshore wind farms to be a long term solution of the energy transition.

EVALUATION OF WIND POTENTIAL FROM A LOCATION AND LAYOUT OF THE TURBINES

The paper presents a study related to the methodology for evaluating the wind potential of a chosen location, with the help of modern software solutions for analysis and specialized design. The values presented in the study show the differences in the efficiency of wind farms depending on their location, the available input data package and their accuracy being a determining factor in the quality of optimization and design of the wind farm.

A novel decomposition-ensemble prediction model for ultra-short-term wind speed

Accurate ultra-short-term wind speed prediction is of great significance to the power generation efficiency of wind farms, and also has a good application prospect in the field of meteorology. In this study, a novel prediction model based on empirical mode decomposition and improved sparrow search algorithm optimized reinforced long short-term memory neural network is proposed. The implementation process of the proposed prediction model is: (a) the ultra-short-term wind speed is decomposed by empirical mode decomposition algorithm. (b) the decomposed components are predicted by reinforced long short-term memory neural network. (c) the hyperparameters of long short-term memory neural network are optimized by designed improved sparrow search algorithm. (d) the predicted values of each reinforced long short-term memory model are superimposed to get the final predicted value. The actual ultra-short-term wind speed data is taken as the research object. R2, RMSE, RRMSE, MRMSE, MAE, MAPE, TIC, IA, Pearson’s test, prediction error distribution box-plot, and Taylor diagram are used to judge the performance indexes of the designed prediction model. In case studies, the performance of the designed prediction model is compared with standard LSTM, EMD and standard LSTM, EMD and standard SSA optimized reinforced LSTM, and other state-of-the-art models. The results show that the proposed ultra-short-term wind speed prediction model achieves the best prediction performance and prediction accuracy.

Employing power spectral density method for investigating atmospheric stability impacts on power generation of a wind farm

ABSTRACT Theoretically, the output power of a wind farm is mainly under influence of wind speed as an atmospheric parameter. However, there are other factors that impact wind power generation. Atmospheric stability is one these features. The main contribution of this study is to investigate the impact of atmospheric stability on efficiency of Manjil wind farm in Iran for summer of 2004. For accomplishing the task, wind speed distributions and power generation amounts of the farm for three different months of the summer were investigated. The investigation showed that in the first and second month wind speed values for 82.3 and 87% of the times were more than 8.8 m/s, respectively. However, power generation in the first month was 4.96% more than the second one. Considering the impact of atmospheric stability on power production, the stability was inspected by employing wind shear exponent as a common metric and Power Spectral Density method as a rather new approach. The results showed that in the first month the atmosphere was instable for 91% of the times. This percent for the second month was 82%. The results support the idea that atmospheric instability has positive impact on efficiency of the wind farm.

Evaluation model on uncertainty of the wind turbine state

It is beneficial to improve the production efficiency and quality of wind farm to accurately evaluate the state of wind turbines (WTs) and formulate operation and maintenance strategies. By analyzing the energy conversion relationship of WTs from wind energy to electric energy, the uncertainty factors affecting the state of WTs are studied, two comprehensive states of efficiency and performance in the production and operation of WTs are proposed, and the efficiency cloud model of wind energy conversion and the performance cloud model of electric energy production are accordingly established. Based on the cloud model eigenvalues, the deviation and stability index for evaluating the state of WTs are constructed, and the unified evaluation method for WTs is given. The problem of making direct comparison and evaluation between WTs with different capacities is solved. Through application of SCADA data of multiple WTs with different wind farms and different capacities in a whole year, it shows that the evaluation model in this paper has strong applicability and high accuracy. And it can be used not only to evaluate the running state of WTs, but also to select the WT type for new wind farm.

The Intelligent Operation and Maintenance Management System for Offshore Wind Farms

Introduction Aiming at the operation & maintenance and safety management of offshore wind farm， the intelligent operation and maintenance management system for offshore wind farms is proposed. Method Through the intelligent dispatching system of offshore wind power， multi-source tracking and boundary warning system of offshore wind power radar， and operation supervision system of offshore wind farm wind turbine platform， the intelligent operation and maintenance management system of offshore wind farm was built. Result Through the offshore wind power intelligent dispatching system of the onshore centralized control center， personnel safety management and ship dispatching can be realized. Through the offshore wind power radar multi-source tracking and boundary warning system， the full-range tracking of ships in the ocean area is realized， and the safety of the wind turbines and submarine cables of the offshore wind farm can be ensured. Through the wind turbine platform's offshore wind farm fan platform operation supervision system， the comprehensive management of wind turbine operators on the wind turbine platform can be assured. Conclusion Therefore， it can realize the overall management of personnel， ships and wind turbines in offshore wind farms， it effectively ensures the safety management of personnel and ship scheduling， improves the safety of wind turbines and submarine cables of offshore wind farms， and realizes the intelligent operation and maintenance efficiency of offshore wind farms， which is expected to be applied and promoted in the project.

ОПТИМИЗАЦИЯ КАБЕЛЬНОЙ СЕТИ СБОРА МОЩНОСТИ МОРСКИХ ВЕТРОЭЛЕКТРОСТАНЦИЙ С ПРИМЕНЕНИЕМ ПАРАМЕТРИЗОВАННОГО ЭВРИСТИЧЕСКОГО АЛГОРИТМА

The article discusses an approach to solving the problem of optimizing the routing of infield power cables layout to improve the efficiency and cost-effectiveness of offshore wind farms. Optimization seeks to reduce the total cost of the infield collection system while bearing in mind the constraints including use of sufficiently sized cables and the required absence of cable crossings in the circuit diagram. The problem is a degree-constrained capacitated minimum spanning tree (DCMST) problem with dependent node costs. Search for solu-tion is based on an integrated approach that uses a hybrid optimization algorithm, which combines a parameterized savings heuristic and particle swarm optimization to optimize the parameters of the primary algorithm, ultimately enabling better solutions. Several tests have been performed to compare the constructed circuit diagrams against solutions yielded by other algorithms; tests showed the proposed approach to significantly improve the efficiency of the constructed circuits as demonstrated in a series of tests and evaluated by comparison with other methods, as well as by comparing the efficiency and cost-effectiveness of the optimized routing against the actual layout of the Walney 1 offshore wind farm.

A Hybrid Intelligent Model for the Condition Monitoring and Diagnostics of Wind Turbines Gearbox

Wind turbines (WTs) are often operated in harsh and remote environments, thus making them more prone to faults and costly repairs. Additionally, the recent surge in wind farm installations have resulted in a dramatic increase in wind turbine data. Intelligent condition monitoring and fault warning systems are crucial to improving the efficiency and operation of wind farms and reducing maintenance costs. Gearbox is the major component that leads to turbine downtime. Its failures are mainly caused by the gearbox bearings. Devising condition monitoring approaches for the gearbox bearings is an effective predictive maintenance measure that can reduce downtime and cut maintenance cost. In this paper, we propose a hybrid intelligent condition monitoring and fault warning system for wind turbine's gearbox. The proposed framework encompasses the following: a) clustering filter- (based on power, rotor speed, blade pitch angle, and wind speed signals)-using the automatic clustering model and ant bee colony optimization algorithm (ABC), b) prediction of gearbox bearing temperature and lubrication oil temperature signals- using variational mode decomposition (VMD), group method of data handling (GMDH) network, and multi-verse optimization (MVO) algorithm, and c) anomaly detection based on the Mahalanobis distances and wavelet transform denoising approach. The proposed condition monitoring system was evaluated using 10 min average SCADA datasets of two 2 MW on-shore wind turbines located in the south of Sweden. The results showed that this strategy can diagnose potential anomalies prior to failure and inhibit reporting alarms in healthy operations.