Wind Farm Model Research Articles

Small‐disturbance stability of a weakly grid‐connected wind farm with multiple PMSGs dominated by the DC‐link voltage dynamics

This study investigates the model-free assessment of small-disturbance stability determined by the DC-link voltage dynamics in a weakly grid-connected wind farm with M permanent magnet synchronous generators (PMSGs). A reduced-order model of the PMSG wind farm is established, which is the approximate representation of the wind farm dominated by the DC-link voltage dynamics. Based on the reduced-order model, limit of small-disturbance stability of the PMSG wind farm determined by the DC-link voltage dynamics is derived. Calculation of derived stability limit does not need to have the parametric model and can be used to approximately assess the small-disturbance stability of the PMSG wind farm. Hence, the assessment made by using the derived stability limit is model-free and useful in practice when it is usually difficult to establish the parametric model of the wind farm. In addition, the derived stability limit clearly indicates and explains that when the grid connection weakens or/and the number of PMSGs increases, the DC-link voltage dynamics are more likely to cause the instability of the PMSG wind farm. In the study, an example AC grid with a grid-connected PMSG wind farm is presented to demonstrate and evaluate the model-free assessment of small-disturbance stability of the wind farm.

Correlation modeling of multiple wind farms based on piecewise cloud representation and regular vine copulas

The accuracy of correlation modeling for multiple wind farms will directly affect the assessment results of absorption capacity in electric utilities. Due to the rapid increase in installed capacity of wind power in recent years, there are often multiple wind farms with complex correlations in a region, and traditional correlation models are inaccurate and highly computational. In order to enhance the accuracy of correlation modeling for multiple wind farms, this paper combines the piecewise cloud representation and regular vine (R-vine) copulas for classification modeling purposes. The piecewise cloud representation is used to divide the multiple wind farms’ data into different categories, and the correlation models of different categories are established based on the R-vine copulas. A case of the SCADA system record data of 6 wind farms in Northwest China has been adopted to evaluate the effectiveness compared with its competitors. Case studies have demonstrated that the proposed method not only has good performances on modeling the correlation better than the traditional method but also has strong robustness, especially in the case that the correlation of different wind farms is inconsistent. Most importantly, this model is able to extract the correlations of different features in multiple wind farms’ data and then targeted modeling.

Simulation and Characteristics Analysis of Multiple Wind Generators in Large-Scale Wind Farms Based on Simplified Model

In the view of the high complexity and a large amount of data of the electromagnetic transient model for the single wind generator, it is difficult to realize the multi-unit simulation modeling of large-scale wind farms by power system simulation software. In this paper, the simplified models of single direct drive and doubly-fed wind generator system are proposed, respectively. In order to study the output characteristics of the wind generator system, the components with small inertia constant in the electromagnetic transient model are neglected, and the shafting model, the converter model, and the control loops are simplified and reduced, respectively. Based on the study of the single electromagnetic transient model of wind generators, the simplified simulation models are built by the PSCAD (Power Systems Computer Aided Design) simulation platform, which are carried out under the conditions of constant wind speed, step wind speed, and fault. The output characteristics of the simplified models under different working conditions are compared in detail models. The simulation results show that, within the allowable calculation accuracy range, the dynamic response curves of the single simplified model and the electromagnetic transient model are consistent. The simulation speed can be significantly improved, the time consumption can be reduced, and the simulation speed can be increased more obviously when the number of simulation models increases. Therefore, it can be applied to the simulation research of multi-wind generators in large-scale wind farms.

An equivalent model for sub-synchronous oscillation analysis in direct-drive wind farms with VSC-HVDC systems

Field experiences have shown that sub-synchronous oscillation (SSO) can occur in direct-drive wind farms with VSC-HVDC systems. Due to the complexity of the detailed wind farm model, a dynamic equivalent model, with a reasonable order reduction of the detailed model and still reflecting inside-wind-farm and wind-farm-grid SSO characteristics is essential. In this paper, based on the principle that similar matrices have identical eigenvalues, the SSO analysis of an N-machine wind farm with VSC-HVDC system is conducted by simplifying it into two single-machine systems. The modeling method of the two single-machine systems is presented. Four case studies are presented to verify the effectiveness of the proposed model when compared with the detailed model in various scenarios. The proposed model is also benchmarked with the output multiplication-based equivalent model (OMM). Comparison results show that although the system order is reduced significantly, the proposed simplified equivalent model can still reflect inside-wind-farm and wind-farm-grid SSO modes in various scenarios. Meanwhile, the rationality of the OMM in terms of the wind-farm-grid SSO analysis is verified theoretically.

Modelling and comparison analysis of grid‐connected DFIG‐based wind farm in weak grid

This study proposes a generic method for modelling and comparison analysis of grid-connected double-fed induction generator (DFIG)-based wind farms in a weak grid. A detailed model of DFIG in a weak grid is established and used as a benchmark. Specifically, the detailed model consists of the mechanical part, generator part, rotor side converter, and phase-locked loop (PLL). Two simplified models of grid-connected DFIG-based wind farms in a weak grid are compared with the detailed model to investigate the mechanism of sub-synchronous oscillation (SSO) as well as the influential factors through linear system analysis. The first simplified model ignores shafting dynamics, stator flux dynamics, and PLL dynamics. The same assumptions are used in the latter, except that PLL dynamics are considered. Time-domain simulation and eigenvalue analysis are used to verify the accuracy of the simplified models. The block diagrams of the linearised system are derived based on the proposed model, and the oscillation mechanism is explained by linear system analysis. Through eigenvalue analysis and the root locus method, the influences of the parameters of the DFIG and its controllers on the system stability are identified and analysed.

Parameters Identification of Equivalent Model of Permanent Magnet Synchronous Generator (PMSG) Wind Farm Based on Analysis of Trajectory Sensitivity

As wind farms have great influences on power system stability, it is essential to develop an adaptive as well as robust equivalent model of it. In this paper, a detailed equivalent model of PMSG wind farm and initialization method is developed. The trajectory sensitivity of parameters is analyzed. Then, the key parameters are estimated using improved Genetic Learning Particle Swarm Optimization (GLPSO) hybrid algorithm with phasor measurement unit (PMU). The description and generalization capability, stability for parameter identification of the equivalent model under wake effects, and when some wind turbines are off-line or wind speed is unknown after an event are analyzed. The maximum differences between the values of estimated parameters and their real ones are less than 10% for the proportional magnification constant of DC voltage controller Kp2 and grid side current controller Kp3. The convergence rate and global optimization performance of the improved GLPSO hybrid algorithm is 0.5 times higher than the classical particle swarm optimization algorithm (PSO) and genetic algorithm (GA).

Dynamic Flow Modelling for Model-Predictive Wind Farm Control

We aim to improve wind farm control for power output by building on the results from WFSim for the development of a dynamic wind farm model. This model will be part of a closed-loop, economic model-predictive control approach for wind farms. It is constructed from first principles using open-source tools to be suitable for adjoint-based optimisation of turbine yaw angles.In a steady-state inflow configuration with two turbines, the new control model matches power expectations from high fidelity simulations in SOWFA to within 15 %. Under time-varying wind directions, it shows time delays in wake direction as inflow changes propagate through the farm with the wind speed, although the dynamics still differ from the SOWFA reference. The model runs flow simulations for a wind farm with a 3 x 3 array of turbines at a real-time order of magnitude on a regular laptop computer.The new control model shows dynamic flow behaviour as wake changes propagate through the wind farm. Some further adjustments are necessary to accurately model three-dimensional flow in two dimensions. With more validation of the wake dynamics, it will be suitable for application in a new closed-loop wind farm controller.

Avaliação da esteira de turbulência gerada por um parque eólico teórico na região do Pampa Gaúcho utilizando o modelo WRF

The main objective of this study is to analyze the influence of a wind farm on the variables that control the flow in the atmospheric boundary layer. The simulated period was the whole year of 2008, using a control simulation performed with the Weather Research and Forecasting model (WRF), and the wind farm model (the WRF model with the module Fitch, which parameters the influence of wind turbines on atmospheric flow). Both simulations using the Yonsei-University (YSU) boundary layer parameterization. From the control simulation is made the validation of the model, using observational data collected in two automatic stations of the National Institute of Meteorology (INMET) in the cities of Alegrete-RS and Quaraí-RS. The wind farm idealized in this work has 100 wind generators of 3 MW of power with 120 m in height and with rotor measuring 125 m in diameter. Although the wind speed responds adequately, the temperature and turbulence of near-surface runoff decrease. Analysis of the dependence of near-surface turbulence with vertical stability indicates that the turbulence being generated by the turbines is not reaching the surface. This problem may be related to the chosen boundary layer parameterization.

A hierarchical, physical and data-driven approach to wind farm modelling

Abstract This paper analyses and models a wind farm using large amounts of measured data collected from every turbine in the farm over a full year. Spectral analysis of the power output of all turbine pairs in the wind farm first shows that their coherence is dependent on the distance between these turbine pairs. This gives a physical basis for a simple, hierarchical approach to wind farm modelling by aggregating turbines into groups based on their proximity. Comparison of the resulting wind farm models with measurement shows that the chosen number of aggregated turbine groups is a trade-off between modelling accuracy and use of more measured data. Further spectral analysis then reveals three distinct frequency regions in the wind farm power output. At the lowest frequencies, all the models and the measurements have similar power generation because the wind farm is compact relative to the length scales in the wind, and the scaling from any individual turbine to the entire wind farm is simply the number of turbines. At high frequencies, the turbines are uncorrelated because the length scales in the wind are now short, with another simple scaling for the total power generation. A third, transitional region sits between these two limits, with each turbine in the wind farm on average is more correlated with closer turbines and less correlated with more distant turbines.

Dynamic equivalent method of PMSG‐based wind farm for power system stability analysis

This study presents a dynamic equivalent method of wind farms with permanent magnetic synchronous generator (PMSG) wind turbines for power system stability analysis. First, a novel clustering indicator is set up based on the morphology similarity distance and cosine similarity between trajectories with the measurable output characteristics time series of wind turbines, and the K-means algorithm is applied to cluster the PMSGs. Combining silhouette coefficient, the robustness analysis of the clustering method under different scenarios is adopted. In each cluster, a trajectory sensitivity based key parameters selection is introduced to reduce the control parameters to be identified. Moreover, a multi-objective adaptive function is established and the multi-controllers aggregated parameters are optimised by adaptive evolutionary particle swarm optimisation algorithm. Thus, a wind farm model represented by multi equivalent wind turbines is obtained. The IEEE 39-bus system with a PMSG-based wind farm is used as test system to verify the validity of the proposed dynamic equivalent approach in time domain and frequency domain, respectively. It has been demonstrated that the proposed model can reduce the model complexity and improve the calculation efficiency of wind farms.

Stability analysis for direct‐drive permanent magnet synchronous generator based wind farm integration system considering wind speed

The interaction between wind farm and power grid may cause the stability issue in the wind farm integration system. Practically, the integration system contains many collector feeders, and one feeder connects a large number of wind turbine generators. Moreover, frequency coupling effect is introduced in all parts of the wind farm system due to the asymmetrical controller of the wind turbine converter. So, it is complicated to analyse the stability of the whole integration system. This study proposes the modular admittance model for wind farm, which is easy to expand for modelling and analysis of the whole integration system. In addition, the operation condition of wind turbine generators in wind farm varies with the prevailing wind speed. The system stability may alter constantly according to the wind speed. How to effectively evaluate the system stability in the entire wind speed operation space is also a challenge. Thus, for wind farm system, this study develops an approach with two-variable admittance model and criterion based on magnitude and phase contours plot to overcome this problem. The proposed admittance model and stability analysis approach are verified by simulation.

Model Order Reduction of Multi-Terminal Direct-Current Grid Systems

This paper proposes a methodology to develop a linear low-order model of a multi-terminal direct-current (MTDC) system connected to practically-sized offshore wind farm systems. The method individually linearizes the model of wind farm systems and subsequently performs model order reduction (MOR) using iterative rational Krylov algorithm (IRKA). This is, therefore, a modular approach and expandable to any number of wind farm connections. On the other hand, the model order and non-linearity of other components are retained. Effectiveness of the proposed method is demonstrated on an MTDC system connected to two large-scale wind farm models by comparing the responses obtained from the full order model (FOM) and reduced order model (ROM). From the simulation studies, it can be seen that the ROM obtained from proposed method is able to maintain high degree accuracy of FOM responses at a much faster simulation time, and hence this can help power system operation planning. Various control functionalities and strategies, different operating conditions and faults of the MTDC system are also considered to investigate robustness of the proposed method.

Wind resource assessment and wind farm modeling in Mossobo-Harena area, North Ethiopia

Although Ethiopia does not have significant fossil fuel resource, it is endowed with a huge amount of renewable energy resources such as hydro, wind, geothermal, and solar power. However, only a small portion of these resources has been utilized so far and less than 30% of the nation’s population has access to electricity. The wind energy potential of the country is estimated to be up to 10 GW. Yet less than 5% of this potential is developed so far. One of the reasons for this low utilization of wind energy in Ethiopia is the absence of a reliable and accurate wind atlas and resource maps. Development of reliable and accurate wind atlas and resource maps helps to identify candidate sites for wind energy applications and facilitates the planning and implementation of wind energy projects. The main purpose of this research is to assess the wind energy potential and model wind farm in the Mossobo-Harena site of North Ethiopia. In this research, wind data collected for 2 years from Mossobo-Harena site meteorological station were analyzed using different statistical software to evaluate the wind energy potential of the area. Average wind speed and power density, distribution of the wind, prevailing direction, turbulence intensity, and wind shear profile of the site were determined. Wind Atlas Analysis and Application Program was used to generate the generalized wind climate of the area and develop resource maps. Wind farm layout and preliminary turbine micro-sitting were done by taking various factors into consideration. The IEC wind turbine class of the site was determined and an appropriate wind turbine for the study area wind climate was selected and the net annual energy production and capacity factor of the wind farm were determined. The measured data analysis conducted indicates that the mean wind speed at 10 and 40 m above the ground level is 5.12 and 6.41 m/s, respectively, at measuring site. The measuring site’s mean power density was determined to be 138.55 and 276.52 W/m2 at 10 and 40 m above the ground level, respectively. The prevailing wind direction in the site is from east to south east where about 60% of the wind was recorded. The resource grid maps developed by Wind Atlas Analysis and Application Program on a 10 km × 10 km area at 50 m above the ground level indicate that the selected study area has a mean wind speed of 5.58 m/s and a mean power density of 146 W/m2. The average turbulence intensity of the site was found to be 0.136 at 40 m which indicates that the site has a moderate turbulence level. According to the resource assessment done, the area is classified as a wind Class IIIB site. A 2-MW rated power ENERCON E-82 E2 wind turbine which is an IEC Class IIB turbine with 82 m rotor diameter and 98 m hub height was selected for estimation of annual energy production on the proposed wind farm. 88 ENERCON E-82 E2 wind turbines were properly sited in the wind farm with recommended spacing between the turbines so as to reduce the wake loss. The rated power of the wind farm is 180.4 MW and the net annual energy production and capacity factor of the proposed wind farm were determined to be 434.315 GWh and 27.48% after considering various losses in the wind farm.

Improving wind farm flow models by learning from operational data

Abstract. This paper describes a method to improve and correct an engineering wind farm flow model by using operational data. Wind farm models represent an approximation of reality and therefore often lack accuracy and suffer from unmodeled physical effects. It is shown here that, by surgically inserting error terms in the model equations and learning the associated parameters from operational data, the performance of a baseline model can be improved significantly. Compared to a purely data-driven approach, the resulting model encapsulates prior knowledge beyond that contained in the training data set, which has a number of advantages. To assure a wide applicability of the method – also including existing assets – learning here is purely driven by standard operational (SCADA) data. The proposed method is demonstrated first using a cluster of three scaled wind turbines operated in a boundary layer wind tunnel. Given that inflow, wakes, and operational conditions can be precisely measured in the repeatable and controllable environment of the wind tunnel, this first application serves the purpose of showing that the correct error terms can indeed be identified. Next, the method is applied to a real wind farm situated in a complex terrain environment. Here again learning from operational data is shown to improve the prediction capabilities of the baseline model.

Reliability aware multi-objective predictive control for wind farm based on machine learning and heuristic optimizations

In this paper, a reliability aware multi-objective predictive control strategy for wind farm based on machine learning and heuristic optimizations is proposed. A wind farm model with wake interactions and the actuator health informed wind farm reliability model are constructed. The wind farm model is then represented by training a relevance vector machine (RVM), with lower computational cost and higher efficiency. Then, based on the RVM model, a reliability aware multi-objective predictive control approach for the wind farm is readily designed and implemented by using five typical state of the art meta-heuristic evolutionary algorithms including the third evolution step of generalized differential evolution (GDE3), the multi-objective evolutionary algorithm based on decomposition (MOEA/D), the multi-objective particle swarm optimization (MOPSO), the multi-objective grasshopper optimization algorithm (MOGOA), and the non-dominated sorting genetic algorithm III (NSGA-III). The computational experimental results using the FLOw Redirection and Induction in Steady-state (FLORIS) and under different inflow wind speeds and directions demonstrate that the relative accuracy of the RVM model is more than 97%, and that the proposed control algorithm can largely reduce thrust loads (by around 20% on average) and improve the wind farm reliability while maintaining similar level of power production in comparison with a conventional predictive control approach. In addition, the proposed control method allows a trade-off between these objectives and its computational load can be properly reduced.

Two-scale momentum theory for time-dependent modelling of large wind farms

This paper presents a theory based on the law of momentum conservation to define and help analyse the problem of large wind farm aerodynamics. The theory splits the problem into two sub-problems; namely an 'external' (or farm-scale) problem, which is a time-dependent problem considering large-scale motions of the atmospheric boundary layer (ABL) to assess the amount of momentum available to the ABL's bottom resistance at a certain time; and an 'internal' (or turbine-scale) problem, which is a quasi-steady (in terms of large-scale motions of the ABL) problem describing the breakdown of the ABL's bottom resistance into wind turbine drag and land/sea surface friction. The two sub-problems are coupled to each other through a non-dimensional parameter called 'farm wind-speed reduction factor' or 'farm induction factor,' for which a simple analytic equation is derived that can be solved iteratively using information obtained from both sub-problems. This general form of coupling allows us to use the present theory with various types of flow models for each scale, such as a numerical weather prediction (NWP) model for the external problem and a computational fluid dynamics (CFD) model for the internal problem. The theory is presented for a simplified wind farm situation first, followed by a discussion on how the theory can be applied (in an approximate manner) to real-world problems; for example, how to estimate the power loss due to the so-called 'wind farm blockage effect' for a given large wind farm under given environmental conditions.

Generating conditions of strong social support for wind power: insights from community-owned wind energy projects

ABSTRACT In community-owned windfarms, community members are involved in initiating, developing, owning and benefiting from the windfarm. As such, these projects facilitate multiple opportunities for local people to participate in development processes and outcomes. However, like any form of development, community-owned windfarms vary significantly from project to project. Specifically, projects employ different business models – including different economic arrangements, governance structures and community engagement practices – which have significant implications for the degree of community participation enabled and the types of social outcomes experienced. Through qualitative analysis of Australia's two (and only) operating community-owned windfarms, this article presents unique insights into the relationships between windfarm business models and social outcomes. The article reveals the importance of participation to realising a range of positive social outcomes, including experiences of empowerment and active support for wind energy. The findings indicate that community participation in windfarm processes and outcomes, over time and in ways that are sustained and multifaceted, contributes to generating conditions of strong social support. The insights are applicable to improving social outcomes across all models of wind development.

동복풍력단지 제어관리시스템 실증을 위한 실시간 시뮬레이션 모델 검증 연구

As the expansion of renewable energy, the related impacts to the electrical system has become a major issue in industrial sectors. In particular, the proportion of wind power system is gradually increased which might affect power system stability. In this regard, there are continuing demands for a research that to perform verification with realistic state of target farm models before integrating it to the conventional power system. In this paper, we studied the wind farm control structure for composing HILS hardware in the loops) to perform a pre-validation study in terms of electro magnetic analysis. In case of management system, excessive power fluctuation by the wind characteristics have to be handled by focused on both voltage and frequency. To do this, the entire output of the wind farm should be controlled by the wind farm management system, and the main control signal would be the active / reactive power. For this reason, we have developed a wind farm model on RSCAD software for utilizing it real time controller. This paper focused on model verification of the wind farm model in terms of the feasibility. With a verified PSCAD software based model, a comparing processes were designed considering voltage level of connection point.

Coordination planning of wind farm, energy storage and transmission network with high-penetration renewable energy

With larger scale renewable energy integrated, high-penetration renewable energy is deemed as one of the most important characteristics in the future power system. However, the inevitable uncertainty of both source and demand has brought significant challenges to planners. Since discoordination planning schemes restrict the optimal operation, a novel and efficient co-planning methodology is urgent to be investigated. Thus, we propose an innovative co-planning model of wind farm, energy storage and transmission network, which successfully takes imbalanced power, unit ramp capacity and incentive mechanism for renewable energy into consideration. To facilitate the renewable consumption, flexible implementations comprising optimal transmission switching (OTS) and unit commitment (UC) are integrated to further optimize the network topology and achieve flexible startup/shutdown of conventional unit. Furthermore, a linear tie-line model designed for multi-regional planning problems has been constructed to capture the presumed limitations. Afterwards, a decentralized decomposition algorithm based on analytical target cascading (ATC) is modified to deal with the proposed model through a completely parallel mode. The modified IEEE 48-bus test system demonstrates the priority of our proposed co-planning model and the corresponding solution algorithm.

Experimental and numerical investigations of actuator disks for wind turbines

AbstractThe expenses of a wind turbine modeling in particular a wind farm modeling either numerically or experimentally cause to take the advantages of actuator disks (AD). Porous disks (PD) are used to simulate actuator disks in experiments especially for wake studies. A rotor of a wind turbine replaced by a PD can be modeled efficiently with less cost and process time. For a proper PD selection, some semi‐empirical equations are suggested for a range of solidity of 0.2‐0.6. These equations came from a number of tests. The PDs with different dimensions and solidity values were considered for the tests and models. The results of 2D and 3D numerical simulations and the experimental results show that at the solidity of 0.5, the coefficient of performance of a PD reaches approximately 16/27 which is equal to the Betz's limit; the relation between the coefficient of power and the solidity is fitted by a parabolic equation. The average error of the proposed equations for the power coefficient is reported 1.59%. Finally, based on the current results, some semi‐empirical equations are suggested to help the initial PD selection, and then, the cost of further studies may decrease.