Wind Turbine Farm Research Articles

Wind turbine power output prediction using a new hybrid neuro-evolutionary method

Short-term wind power prediction is challenging due to the chaotic characteristics of wind speed. Since, for wind power industries, designing an accurate and reliable wind power forecasting model is essential, we deployed a novel composite deep learning-based evolutionary approach for accurate forecasting of the power output in wind-turbine farms, which is developed in three stages. At the beginning stage (pre-processing), the k-means clustering method and an autoencoder are employed to detect and filter noise in the SCADA measurements. In the Next step (decomposition), in order to decompose the SCADA time-series data, we proposed a new hybrid variational mode decomposition (HVMD) method, that consists of VMD and two heuristics: greedy Nelder-Mead search algorithm (GNM) and adaptive randomised local search (ARLS). Both heuristics are applied to tune the hyper-parameters of VMD that results in improving the performance of the forecasting model. In the third phase, based on prior knowledge that the underlying wind patterns are highly non-linear and diverse, we proposed a novel alternating optimisation algorithm that consists of self-adaptive differential evolution (SaDE) algorithm and sine cosine optimisation method as a hyper-parameter optimizer and then combine with a recurrent neural network (RNN) called Long Short-term memory (LSTM). This framework allows us to model the power curve of a wind turbine on a farm. A historical dataset from supervisory control and data acquisition (SCADA) systems were applied as input to estimate the power output from an onshore wind farm in Sweden. Two short time forecasting horizons, including 10 min ahead and 1 h ahead, are considered in our experiments. The achieved prediction results supported the superiority of the proposed hybrid model in terms of accurate forecasting and computational runtime compared with earlier published hybrid models applied in this paper.

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.

Analysis of Senegal Type Vertical Axis Wind Turbines Arrangement in Wind Farm

Background: In a wind farm, the wind speed of the downstream wind turbine will be lower than the wind speed of the upstream wind turbine due to the influence of the wake. Therefore, the wake of wind turbines is one of the uncertain factors predicting the annual power generation of the wind farms. The study on the wake can effectively improve the efficiency of power generation. The arrangement of vertical axis wind turbines in wind farms is rarely studied. Therefore, it is important to study the vertical layout of wind turbines under the influence of wakes to obtain the best layout and unit spacing. Objective: The objective of this study is to obtain the optimal layout and unit distance of wind turbines in Senegal wind farms by studying the arrangement of Senegal vertical axis wind turbines in wind farms. Methods: Based on the ANSYS CFX flow field calculation module, the fluid dynamics model of the Senegal fan was established and the flow field simulation analysis was carried out. Based on the Jensen wake model and its improved model, three layout methods for the wind farm wind turbines were proposed: two units were arranged in series, two units were arranged in parallel, and three units were staggered. Through the simulation model, the wind energy utilization coefficient and wind speed of the wind turbine in the wind farm were obtained. Results: The optimal separation distance between the units was analyzed from four different angles: wind energy utilization coefficient, torque analysis, downstream tail flow and wind speed cloud contour. Finally, based on the optimal arrangement and unit distance, a triangular staggered wind farm composed of 10 units was established, and the integrated flow field characteristics of the whole wind farm were simulated and analyzed. The integrated flow field wake characteristics of the wind farm were obtained. Conclusion: In all the three arrangements, the optimum distance between the units must be three times the diameter of the wind turbine. This arrangement ensures that most of the units are unaffected by the wake, the area affected by the low-velocity wake of the wind farm is small, and the area affected by the high-speed wake is large.

Distributed Extremum-Seeking for Wind Farm Power Maximization Using Sliding Mode Control

This paper introduces a sliding-mode-based extremum-seeking algorithm aimed at generating optimal set-points of wind turbines in wind farms. A distributed extremum-seeking control is directed to fully utilize the captured wind energy by taking into consideration the wake and aerodynamic properties between wind turbines. The proposed approach is a model-free algorithm. Namely, it is independent of the model selection of the wake interaction between the wind turbines. The proposed distributed scheme consists of two parts. A dynamic consensus algorithm and an extremum-seeking controller based on sliding-mode theory. The distributed consensus algorithm is exploited to estimate the value of the total power produced by a wind farm. Subsequently, sliding-mode extremum-seeking controllers are intended to cooperatively produce optimal set-points for wind turbines within the farm. Scheme performance is tested via extensive simulations under both steady and varying wind speed and directions. The presented distributed scheme is compared with a centralized approach, in which the problem can be seen as a multivariable optimization. The results show that the employed scheme is able to successfully maximize power production in wind farms.

Analytical solution for the cumulative wake of wind turbines in wind farms

Abstract

Mountain waves can impact wind power generation

Abstract. Mountains can modify the weather downstream of the terrain. In particular, when stably stratified air ascends a mountain barrier, buoyancy perturbations develop. These perturbations can trigger mountain waves downstream of the mountains that can reach deep into the atmospheric boundary layer where wind turbines operate. Several such cases of mountain waves occurred during the Second Wind Forecast Improvement Project (WFIP2) in the Columbia River basin in the lee of the Cascade Range bounding the states of Washington and Oregon in the Pacific Northwest of the United States. Signals from the mountain waves appear in boundary layer sodar and lidar observations as well as in nacelle wind speeds and power observations from wind plants. Weather Research and Forecasting (WRF) model simulations also produce mountain waves and are compared to satellite, lidar, and sodar observations. Simulated mountain wave wavelengths and wave propagation speeds (group velocities) are analyzed using the fast Fourier transform. We found that not all mountain waves exhibit the same speed and conclude that the speed of propagation, magnitudes of wind speeds, or wavelengths are important parameters for forecasters to recognize the risk for mountain waves and associated large drops or surges in power. When analyzing wind farm power output and nacelle wind speeds, we found that even small oscillations in wind speed caused by mountain waves can induce oscillations between full-rated power of a wind farm and half of the power output, depending on the position of the mountain wave's crests and troughs. For the wind plant analyzed in this paper, mountain-wave-induced fluctuations translate to approximately 11 % of the total wind farm output being influenced by mountain waves. Oscillations in measured wind speeds agree well with WRF simulations in timing and magnitude. We conclude that mountain waves can impact wind turbine and wind farm power output and, therefore, should be considered in complex terrain when designing, building, and forecasting for wind farms.

Numerical Modelling of Neutral Atmospheric Boundary Layer Flow Through Heterogeneous Forest Canopies in Complex Terrain (A Case Study of a Swedish Wind Farm)

This paper exposes the risk of generalization of wind conditions from a single met-mast measurement to be representative of the actual flow field in a wind farm situated in complex terrain. The Large-Eddy Simulation of the neutral atmospheric flow for a mid-western Sweden wind farm is performed. The site-specific complex topography and the forest properties are extracted from the Airborne Laser Scanning data. To emphasize the impact of the local topography and surface roughness on the wind field, the wind turbines are not included in the numerical simulations. The predicted wind speeds are compared with the nacelle-mounted anemometers taken from the wind farm's turbine SCADA data where a sufficient degree of match is observed. The results show that mean wind speed, shear exponent and turbulence intensity vary at each wind turbine location justifying the need for turbine-specific assessment of the wind resource in a wind farm located in forested complex terrain. The inter-turbine difference in wind resource is quantified in terms of the difference in turbine-specific mechanical loads. The results show that not only inter-turbine loads varying significantly in the wind farm, but the turbine loads also differ significantly if a homogeneous assumption is made for the forest.

Simulation of Two In-Line Wind Turbines Using an Incompressible Finite Volume Solver Coupled with a Blade Element Model

The present study addresses the first steps of development and validation of a coupled Finite Volume-Blade Element Model (FV-BEM) simulation tool dedicated to offshore wind turbine farm modeling. The fluid domain is solved using the incompressible formulation of the CFD solver Grid-flow. The turbines are taken into account using FAST (from NREL) and their effects are imposed into the fluid domain through an actuator line model (ALM) for which specific enhancements are proposed. The Grid-flow solver and its two-way coupling with the aero-elastic modules from FAST is introduced and detailed. The simulation of the case of two in-line wind turbines using the FV-BEM coupling proposed is presented, together with comparisons against experimental results for validation purposes. Finally, a discussion on performances, advantages and limitations of the formulation proposed is provided.

Dynamic analysis and sizing optimization of a pumped hydroelectric storage-integrated hybrid PV/Wind system: A case study

The intermittent nature of variable renewable energy resources in conjunction with the fluctuating energy demand of load require using an efficient long-term energy storage means. Pumped Hydroelectric Storage (PHS) has proved its commercial viability as electricity storage technology and eligibility to be coupled with the Renewable Energy Systems (RESs). This paper proposes a simple and efficient procedure for optimal sizing of PHS-integrated hybrid PV/Wind power system for providing sustainable supply of electricity to an urban community in Brack city (27°32'N,14°17'E), Libya. The sizing procedure is considered as a constrained optimization problem. The constraints are developed to consider the operating conditions and the output uncertainty of RESs. The sizing algorithm and optimization procedure are explicitly described. Different operating scenarios were considered to identify the optimal size of the hybrid PV/Wind energy system and PHS. Using the measured date of climate conditions and load consumption, the energy production for different sizes of solar PV arrays and wind turbine farms were estimated by System Advisory Model (SAM) software. Based on the Levelized Cost Of Energy (LCOE), the optimum power capacity ratio of PV array to wind turbines farm was 1:5. For the system of optimal size, PHS contribute with a share of 15 % in covering the annual load energy. The obtained results revealed that coupling the hybrid RESs with PHS is cost competitive and reliable alternative for providing sustainable energy supply to urban areas of suitable topographical conditions and high potential of renewable energy resources.

Wind Speed Distribution Direct Approximation by Accumulative Statistics of Measurements and Root-Mean-Square Deviation Control

Abstract In order to accurately estimate wind farm output and subsequently optimise it, a method of wind speed distribution approximation is suggested. The method is based on period-by-period accumulation of wind speed measurements, transforming them into empirical probabilities, and observing the moving approximation to the expected power produced by the wind turbine or entire wind farm. A year is a minimal term during which wind statistics are to be accumulated. The sufficient validity and reliability of the wind speed distribution approximation is supported by controlling root -mean-square deviations and maximal absolute deviations with respect to the moving average of the expected power. The approximation quality can be regulated by adjusting constants defining the requirements to the moving deviations.

A Spatio-Temporal Multiscale Neural Network Approach for Wind Turbine Fault Diagnosis With Imbalanced SCADA Data

The supervisory control and data acquisition (SCADA) systems are widely installed on wind turbines (WTs) in major wind farms, which produce a large amount of sensory data that can be used for fault diagnosis of WTs. However, these SCADA data are naturally multivariate time series, which represent complex temporal correlations within each sensor variable and spatial correlations between different sensor variables. To effectively capture spatio-temporal correlations in SCADA data, we propose a new spatio-temporal multiscale neural network (STMNN). The proposed STMNN model contains two parallel feature extraction modules: first, a multiscale deep echo state network module to extract temporal multiscale features; second, a multiscale residual network module to extract spatial multiscale features. Additionally, as the WTs are in a normal working state most of the time, there are a large amount of normal SCADA data and few failure SCADA data. To address the data imbalance problem of SCADA data and enhance the fault diagnosis performance, instead of cross-entropy loss, the STMNN model adopts focal loss as loss function. Our proposed STMNN method can provide an end-to-end fault diagnosis solution with imbalanced SCADA data, and is evaluated through experiments on an SCADA dataset from a real wind farm. The experimental results and comparative analysis have proved the effectiveness of our proposed STMNN model in practical applications.

Numerical analysis of the effect of the mutual interaction between closely separated variable pitch vertical axis wind turbines

Recent researches have proven that mutual interaction between vertical axis wind turbines (VAWTs) results in enhancement in the average power coefficient. Efficient VAWT clusters have been created to provide wind farms having higher power densities compared to conventional horizontal vertical axis wind turbine farms. The created clusters adopted fixed pitch VAWTs in studying the mutual interaction in close vicinity. This paper extends the investigation of the mutual effect between variable pitch VAWTs in closely oriented turbine clusters. A numerical study is performed using commercial Fluent ANSYS code in order to study the effect of gap distances, phase shifts, and oblique angles for co-rotating and counter-rotating arrangements of two variable pitch VAWTs. The results showed improvement in the performance of two turbine clusters up to 26% compared to isolated turbines. Three turbine clusters are also tested numerically based on the results of the two turbine clusters. The created three turbine cluster represents a basic unit to construct a more efficient wind farm. The results of the developed three turbine cluster showed an increase in the average power coefficient by 38% higher than that of isolated turbines.

Short-term predictions of multiple wind turbine power outputs based on deep neural networks with transfer learning

This paper proposes a novel deep and transfer learning (DETL) framework, which enables a more efficient development of data-driven wind power prediction models for a group of wind turbines. In DETL, a transfer learning scheme is developed to boost computations in modeling wind power generation processes from a data-driven perspective and derive latent features for conducting power predictions. To perform the transfer learning, a new data organization scheme, which separates a batch of wind turbine datasets into a source domain and multiple target domains, is adopted. Based on the source domain, the DETL attempts to extract homogeneous characteristics of multiple wind turbine system dynamics via developing a base Auto-encoder (AE), whose architecture is adaptively determined. Next, the DETL aims to specify heterogeneous characteristics among individual wind turbine system dynamics via learning target domains, which converts the base AE model into multiple customized AE models. Finally, the customized AE model representing system dynamics of each wind turbine is extended to conduct multi-step wind power predictions by additionally incorporating temporal features and prediction targets. Field data collected from 50 wind turbines in commercial wind farms are utilized to verify the proposed DETL. Computational experiments validate that the DETL outperforms conventional training methods on developing a batch of prediction models with a higher prediction accuracy and faster training speed.

Northern gannets (Morus bassanus) are strongly affected by operating offshore wind farms during the breeding season

Northern gannets (Morus bassanus) have been ranked as one of the most vulnerable species in terms of collision with offshore wind farm (OWF) turbines, and strong avoidance of OWFs has been documented for this species. Gannets increasingly encounter OWFs within the ranges of their largest breeding colonies along the European coasts. However, information on their actual reactions to OWFs during the breeding season is lacking. We investigated the possible effects of OWFs located 23–35 km north of the colony on Helgoland in the southern North Sea on breeding gannets. GPS tags were applied to 28 adult gannets breeding on Helgoland for several weeks over 2 years. Most gannets (89%) predominantly avoided the OWFs in both years, but 11% frequently entered them when foraging or commuting between the colony and foraging areas. Flight heights inside the OWFs were close to the rotor-blade zone, especially for individuals predominantly avoiding the OWFs. Gannets preferred distances of 250–450 m to the turbines when being inside the OWF. A point process modelling approach revealed that the gannets resource selection of the OWF area compared with the surroundings (outside OWF = up to 15 km from the OWF border) was reduced by 21% in 2015 and 37% in 2016. This study provides the first detailed characterisation of individual reactions of gannets to OWFs during the breeding season and one of the first comprehensive studies of OWF effects on this species based on telemetry data. The documented effects need to be considered during the planning processes for future OWFs, especially those located close to large seabird breeding colonies.

Data‐driven modeling for fatigue loads of large‐scale wind turbines under active power regulation

AbstractAdvanced control methods for coordinatively optimizing active power dispatch and fatigue load distribution of wind turbines (WTs) in mountain wind farms, require the fatigue load modeling that has not been fully studied. This study proposes a data‐driven modeling method for fatigue loads of large‐scale WT under active power regulation. Firstly, load simulations based on Monte Carlo approach are conducted to overcome the uncertainty of fatigue load caused by turbulent wind. Subsequently, the target components of WT are selected according to the fatigue load dataset distribution at various power setpoints and the sensitivity to active power. Specifically, the Jarque–Bera statistic is used to evaluate the distribution characteristics of datasets; the single‐value processing determines the valid value through the maximum probability of the Gaussian kernel density distribution of the dataset; the Morris screening is employed to select the valid value with high sensitivity to active power regulation. Afterwards, arbitrary polynomial chaos expansion and support vector regression are performed to establish the data model of fatigue loads, respectively. Finally, the proposed method is applied into a 1.5‐MW commercial WT model designed by the manufacturer through the Bladed software. The obtained data‐driven model establishes a basis of simultaneously optimizing active power dispatch and fatigue load distribution, making it possible to reduce energy cost in mountain wind farms.

Integrated wind farm layout and control optimization

Abstract. The objective of this paper is to investigate the joint optimization of wind farm layout and wind farm control in terms of power production. A successful fulfilment of this goal requires the following: (1) an accurate and fast flow model, (2) selection of the minimum set of design parameters that rules or governs the problem, and (3) selection of an optimization algorithm with good scaling properties. For control of the individual wind farm turbines with the aim of wind farm production optimization, the two most obvious strategies are wake steering based on active wind turbine yaw control and wind turbine derating. The present investigation is limited to wind turbine derating. A high-speed linearized computational fluid dynamics (CFD) Reynolds-averaged Navier–Stokes (RANS) solver models the flow field and the crucial wind turbine wake interactions inside the wind farm. The actuator disc method is used to model the wind turbines, and utilizing an aerodynamic model, the design space of the optimization problem is reduced to only three variables per turbine – two geometric and one carefully selected variable specifying the individual wind turbine derating setting for each mean wind speed and direction. The full design space is spanned by these (2N+NdNsN) parameters, where N is the number of wind farm turbines, Nd is the number of direction bins, and Ns is the number of mean wind speed bins. This design space is decomposed into two subsets, which in turn define a nested set of optimization problems to achieve a significantly faster optimization procedure compared to a direct optimization based on the full design space. Following a simplistic sanity check of the platform functionality regarding wind farm layout and control optimization, the capability of the developed optimization platform is demonstrated on a Swedish offshore wind farm. For this particular wind farm, the analysis demonstrates that the expected annual energy production can be increased by 4 % by integrating the wind farm control into the design of the wind farm layout, which is 1.2 % higher than what is achieved by optimizing the layout only.

Towards optimal layout design of vertical-axis wind-turbine farms: Double rotor arrangements

Designing an optimal wind farm layout requires fundamental knowledge of the interaction of wind turbines in an arrangement. In this paper, extensive high-fidelity CFD simulations are performed to investigate the influence of relative spacing, i.e., distance (R) and angle (Φ), in double rotor arrangements of co-rotating Darrieus H-type vertical axis wind turbines (VAWTs) on their aerodynamic performance. The relative spacing varies within 1.25d ≤ R ≤ 10d (d: turbine diameter) and −90° ≤ Φ ≤ +90°. The turbines operate at their optimal tip speed ratio. The analysis is focused on the individual and overall power performance of the turbines and their aerodynamics. Unsteady Reynolds-Averaged Navier-Stokes (URANS) simulations, validated with experiments, are employed. It is found that an optimal region exists in which a higher overall power coefficient (CPoverall) compared to the CP of an isolated solo rotor (CPSolo) can be achieved. This region corresponds to compact rotor arrangements, i.e. R/d ≤ 3d with Φ ≥ +45° and Φ ≤ −45°, yielding a maximum 1.8% increment in CPoverall/CPSolo at R/d = 1.25 and Φ = +75°. Detailed flow analysis reveals that in the optimal spacing, a narrow passage between the two rotors is formed within which the flow accelerates, forming a high-velocity region. The downstream turbine benefits from its blade(s) passing through this region and consequently yields higher CP values. The findings highlight the high potential for compact VAWT farms with high power density and support the optimal layout design of VAWT farms.

Optimal maintenance planning and resource allocation for wind farms based on non-dominated sorting genetic algorithm-ΙΙ

The complex structure and harsh working environment of wind turbines cause frequent failures and unavailability of these turbines in wind farms. To promote the long-term stable development of wind power and enhance its market competitiveness, the reduction of operation and maintenance costs is particularly important, which are estimated to account for approximately 1/3 of the total life cycle cost. With the continuous increase in the size and number of wind turbines, wind farm maintenance tasks and resources are increasing and becoming unpredictable. The realization of the dynamic scheduling of maintenance tasks and resources under various constraints has become vital. In this study, an optimal multi-objective model of maintenance planning and resource allocation for wind farms is established. The maintenance tasks are obtained according to the preset maintenance strategy and current operating status of the wind turbine components. The dynamic requirements of maintenance planning and resource allocation for different wind farms in adjacent areas are periodically generated, and the Non-dominated sorting genetic algorithm-ΙΙ (NSGA-ΙΙ) is adopted to conduct a combinatorial optimization process. The validity of the proposed model are verified by a corresponding case study, along with a comparative analysis with other optimization algorithms and a sensitivity study of different parameters.

Comprehensive Optimization for Fatigue Loads of Wind Turbines in Complex-Terrain Wind Farms

In complex-terrain wind farms (WFs), a serious uneven distribution of fatigue loads increases the operation and maintenance cost of wind power generation. Therefore, this study proposes a comprehensive optimization for fatigue loads of wind turbine (WT) components. The optimization is performed at two levels: WT and WF. The central controller of WF redispatches the active power reference for each WT, and the local controller of WT is performed to appropriately reduce the generator speed. For WT level, a variance-based sensitivity analysis is performed to assess the effect of generator speed and active power on component fatigue loads. The analysis results show a high sensitivity of all fatigue loads of components to the generator speed, while the load sensitivity of blade (Mz) and tower (My) to active power is considerably high. For WF level, a novel Multi-objective adaptive Yin-Yang pair optimization algorithm is proposed to coordinatively optimize fatigue load distribution and active power dispatch. The Pareto Front of optimized fatigue load deviations is obtained for the target components, which correspond to a series of optimization solutions. Then, weighted analysis is employed to evaluate the optimal solutions, and the solution with the smallest weighted value is selected as the appropriate solution. Finally, the simulation test using the real data from the complex-terrain WF is conducted, and the comparison results confirm the effectiveness of the proposed optimization approaches.

Yaw induced wake deflection-a full-scale validation study

Aerodynamic wake interactions between turbines located in wind power plants cause both a loss in power production and an increase in fatigue loading of the wind farm turbines. Yaw induced active wake deflection is one possible wind farm control strategy, which can be applied to mitigate wake effects on nearby downstream located wind turbines. In the present study three flow models of different fidelities are applied to mimic a full-scale study of wake deflection recorded by an advanced synchronised setup of two long-range pulsed scanning lidars. The investigated case studies encompass a base case with (approximately) zero yaw setting supplemented by two non-zero yaw cases of 17.5° and -14.5°, respectively. The model results are compared mutually as well as with the result of the full-scale measurement campaign.