Wind Farm Model Research Articles

Ultra-Short-Term Wind Power Prediction Based on Bidirectional Gated Recurrent Unit and Transfer Learning

Wind power forecasting (WPF) is imperative to the control and dispatch of the power grid. Firstly, an ultra-short-term prediction method based on multilayer bidirectional gated recurrent unit (Bi-GRU) and fully connected (FC) layer is proposed. The layers of Bi-GRU extract the temporal feature information of wind power and meteorological data, and the FC layer predicts wind power by changing dimensions to match the output vector. Furthermore, a transfer learning (TL) strategy is utilized to establish the prediction model of a target wind farm with fewer data and less training time based on the source wind farm. The proposed method is validated on two wind farms located in China and the results prove its superior prediction performance compared with other approaches.

Comparison of Two Control Strategies for VSC-MTDC with Wind Farm

: Two VSC-MTDC control strategies with different combinations of controllers are proposed to eliminate transient fluctuations in the DC voltage stability, resulting from a power imbalance in a VSC-MTDC connected to wind farms. First, an analysis is performed of a topological model of a VSC converter station and a VSC-MTDC, as well as of a mathematical model of a wind turbine. Then, the principles and characteristics of DC voltage slope control, constant active power control, and inner loop current control used in the VSC-MTDC are introduced. Finally, the PSCAD/EMTDC platform is used to establish an electromagnetic transient model of a wind farm connected to a parallel three-terminal VSC-HVDC. An analysis is performed for three cases of single- phase grounding faults on the rectifier and inverter sides of a converter station and of the withdrawal of the converter station on the rectifier side. Next, the fault response characteristics of VSCMTDC are compared and analyzed. The simulation results verify the effectiveness of the two control strategies, both of which enable the system to maintain DC voltage stability and active power balance in the event of a fault. Background: The use of a VSC-MTDC to connect wind power to the grid has attracted considerable attention in recent years. A suitable VSC-MTDC control method can enable the stable operation of a power grid. Objective: The study aims to eliminate transient fluctuations in the DC voltage stability resulting from a power imbalance in a VSC-MTDC connected to a wind farm. Methods: First, the topological structure and a model of a three-terminal VSC-HVDC system connected to wind farms are studied. Second, an analysis is performed of the outer loop DC voltage slope control, constant active power control and inner loop current control of the converter station of a VSC-MTDC. Two different control strategies are proposed for the parallel three-terminal VSCHVDC system: the first is DC voltage slope control for the rectifier station and constant active power control for the inverter station, and the second is DC voltage slope control for the inverter station and constant active power for the rectifier station. Finally, a parallel three-terminal VSC-HVDC model is built based on the PSCAD/EMTDC platform and used to verify the accuracy and effectiveness of the proposed control strategy. Results: The results of simulation analysis of the faults on the rectifier and inverter sides of the system show that both strategies can restore the system to the stable operation. The effectiveness of the proposed control strategy is thus verified. Conclusion: The control strategy proposed in this paper provides a technical reference for designing a VSC-MTDC system for wind farms.

Stability analysis of sub/super synchronous oscillation in direct‐drive wind farm considering the energy interaction between PMSGs

A method to analyze the stability of sub/super synchronous oscillations in direct-drive wind farm considering the energy interaction between PMSGs is proposed. First, the dynamic energy model of direct-drive wind farm considering the energy interaction between PMSGs is built. Then, the farm-grid interaction energy is divided into three components: the disturbance energy, the self-coupling energy and the interaction energy between PMSGs, which characterize the initial disturbance effect of PMSG, the self-induction effect and the interaction between generators, respectively. On this basis, the variation characteristics of disturbance energy, coupling energy and the interaction energy are analyzed using Lyapunov's second stability theorem. It is revealed that, the interaction energy is the key factor that determines whether the oscillation will diverge or converge. In addition, the influences of PMSG integration distance, wind speed and control parameters on the stability are studied. Finally, the model of direct-drive wind farm is built in RT-LAB for hardware-in-the-loop tests. Simulation results show that, the longer the PMSG integration distance is or the higher the wind speed where the PMSG is located is, the stronger the interaction between PMSGs is, and the better it is for the sub-synchronous oscillation to converge. The larger the PMSG current loop parameters, voltage loop parameters and PLL parameters are, the stronger the dissipation effect of the interaction energy between PMSGs is, and the better it is for the super-synchronous stability control.

Contribution to the Modeling, Control and Dynamic Management of an Autonomous Wind Farm using a Fuel cell and a Super-capacitor

Contribution to the Modeling, Control and Dynamic Management of an Autonomous Wind Farm using a Fuel cell and a Super-capacitor

Validation of copula-based weather generator for maintenance model of offshore wind farm

This article discusses the aspect of modeling weather conditions in marine environment for implementation in the offshore wind farm domain. It is clear that harsh sea weather conditions influence many characteristics of any offshore installation. The accessibility to the infrastructure, maintenance procedures, failure ratios of components, energy provision levels, or utilization of vessels—are the examples of weather-related issues connected to the offshore wind industry. Regarding the growing popularity of digital twin methodology, authors present a novel view to generate weather data with copula-based method. The results obtained are compared to selected historical data and implemented into the maintenance model. The selected indicators of maintenance service are used for usability assessment of proposed copula-based method.

A scenario-based optimal dispatch for joint operation of wind farms and combined heat and power plants considering energy flexibilities in heating networks

A scenario-based optimal dispatch is proposed for joint operation of a wind farm and combined heat and power (CHP) plant considering energy flexibilities in heating networks. First, the heating network is modelled, and energy flexibility is used to improve the flexible adjustment ability of the CHP plant. Then, considering the uncertainty of wind power, a scenario-based optimal dispatch model for wind farms and CHP plants with heating networks is proposed to maximize the revenue expectation of the joint operation. The simulation results show that the joint operation revenue is improved compared with that of independent operation. Considering the energy flexibilities in heating networks, the CHP plant in the joint system can compensate for the fluctuations in wind power output, promote the integration and consumption of wind power, and reduce the penalty cost caused by the uncertainty of wind power.

Reactive-voltage coordinated control of offshore wind farm considering multiple optimization objectives

Reactive power-voltage (Q-V) control of wind turbine (WT) is an effective method to realize voltage control of a grid-connected wind farm system. However, the variation in the output reactive power of WTs would aggravate the system power loss and converter electro-thermal stress. That is, it would reduce the system economy and accelerate the aging failure process of wind power converters. Considering this, a multi-objective Q–V coordinated control strategy for wind farms considering voltage deviation, converter junction temperature, and power loss is proposed in this paper. In this method, the reactive power instruction matrix of the WTs is used as the decision variable, and the system voltage deviation, power loss, and converter junction temperature are used as the state variables. A linear reactive power flow optimization model for wind farms based on the sensitivity analysis method is proposed to map the coupling relationship between decision variables and state variables. It is aimed at enhancing the voltage control of the system while considering the junction temperature of the converter and active power loss of the system. In addition, two control modes are designed to achieve a trade-off among optimization objectives. A wind farm power-flow model and RT-Lab semi-physical simulation are used for verification. The results show that the proposed method can improve the overall performance of the system compared with other methods.

Transmission adaptability planning considering cluster correlation of wind farms

Wind farm cluster penetration and its long-distance delivery have become the important form of wind power accommodation of power network in China. For the adaptability of power network planning to the output volatility of clustering wind farms, three steps are studied as follows. Initially, the output uncertainty model of clustering wind farms based on Auto Regression Integrated Moving Average (ARIMA)-Generalized Autoregression Conditional Heteroscedasticity (GARCH)-Pair Copula is put forward to analyze the intermittency, fluctuation and clustering wind power correlation. Secondly, Full Cost (FC) theory is introduced to account social expense brought by grid-connected clustering wind farms from four aspects of static cost, dynamic cost, environmental cost and inter-generational cost. Meanwhile, available wind power transmission capacity is defined to reflect wind power accommodation based on transmission margin. Finally, the adaptive efficiency index is proposed by the ratio of available wind power transmission capacity to FC. The transmission adaptability planning model considering cluster correlation of wind farms is built with the objective function of the adaptive efficiency index. Taking the actual regional power grid as an example, the results show that the planning scheme considering the optimal adaptive efficiency index of clustering wind farms can be better adapted to the future power network environment.

CFD-based surrogate modelling of urban wind farms using artificial neural networks: double rotor arrangements

Extensive characterization studies are required to identify optimal wind farm layouts and achieve high power density, i.e., power per land area. Performing such studies using experimental or high-fidelity numerical methods can be timely and computationally expensive. To alleviate this obstacle, surrogate models can be developed to mimic the behavior of the simulation/experiment. In this paper, a shallow feed-forward artificial neural network (ANN) surrogate model is developed. The Levenberg-Marquardt algorithm is used to train a model with 3 layers and 10 hidden nodes. The model correlates the arrangement of a double-rotor vertical axis wind turbine array, as the fundamental generating cell of the wind farm, with its overall power performance. The inputs are the relative distance (R) and angle (®) between the rotors, and the output is the overall power coefficient of the array. In total, 96 CFD-simulated arrangements are used as data points to train, validate and test the model. The trained model has a mean square error of 2.10 × 10-5 and R-squared of 0.99, indicating its accuracy and generalizability. The average and maximum errors are 3% and 10%, respectively. The employed method can be expanded to accommodate more rotors towards optimal urban wind farm layout design.

Dynamic equivalence of doubly-fed wind turbines based on parameter identification and optimization

The dynamic equivalence and modeling of large-scale wind farms is the basis for studying the problem of multi-temporal and spatial scale wind farms grid connection, and the establishment of a high-precision equivalence model is a key issue among them. This paper takes doubly-fed wind turbines as the research object. Aiming at the problem of large dynamic errors in direct aggregation of equivalents into a single unit, an equivalent model combined with particle swarm optimization algorithm is established to improve the accuracy of dynamic equivalents. The electrical parameters, reactive power reference values, active power controller parameters, and active DC voltage controller parameters are optimized using particle swarm optimization. The research results show that the equivalent model after optimizing the parameters of the active power and DC bus voltage controller is the closest to the detailed model in describing the dynamic characteristics of multiple machines.

Investigation on FRT Capability of PMSG-Based Offshore Wind Farm Using the SFCL

A rapiddevelopment of the permanent magnetic synchronous generator (PMSG)-based offshore wind farm has been witnessed in the past decade. To prevent the offshore wind farms from being disconnected during a fault, many countries have proposed specification requirements for the fault ride-through (FRT). Traditional FRT scheme Chopper circuit for PMSG needs to consume all the mismatching energy and the impulse current is too high during the fault, which will even affect the safe of the PMSG. In this paper, the superconducting fault current limiter (SFCL) is applied to enhance the ability of the FRT in the PMSG-based offshore wind farm. In the proposed scheme, the resistive SFCL can reduce the energy consumption and improve the working performance of the Chopper circuit by absorbing the power of the grid-side converter (GCS) during the fault. An offshore wind farm model is built through PSCAD/EMTDC software. The comparison simulation results validate the feasibility of the proposed scheme.

PMSM Wind Farm Aggregation Algorithm Based on Power Equivalence Principle

With the continuing increase of offshore wind farm power scale, it is urgent to propose a simplified wind farm model, which aggregates the entire wind farm into single or several aggregated wind turbine generators (WTGs), aiming to save computing resources and improve simulation speed. A novel aggregation algorithm that considers the power loss of offshore submarine cable is proposed, which is different from the traditional wind farm modeling method that adopts amplifying transformer as aggregation medium. Moreover, multi machine aggregation (MMA) algorithm is furtherly proposed to improve the aggregation accuracy. Simulation results verify that the proposed aggregation method can present the dynamic characteristics of wind farm with high accuracy, and can be popularized for other types of wind farm.

Turbine layout optimisation for large‐scale offshore wind farms–A grid‐based method

This study presents a new turbine layout optimisation approach using a grid-based problem formulation for improved design performance and computational efficiency for industrial-scale applications. A particle swarm optimisation algorithm is employed in the wind turbine layout optimisation, in which a micro-siting function is proposed to allow solutions 50 m of deviation while maximising energy capture without compromising maritime navigation or search and rescue operations. Solutions are assessed by a wind farm model, comprising the Larsen wake model, a multiple wake effect summation method, and a rotor-effective wind speed calculation. A novel look-up function is populated by on-the-fly algorithm and is used to reduce the number of model evaluations by approximately 95%. A gigawatt scale hypothetical site is presented to test the model on a realistically complex scenario. A set of design solutions generated by the algorithm are compared to empirical designs, with the algorithm outperforming the empirical solutions by 7.55% on average, in terms of net-present-value of energy capture minus the capital cost of turbines. The numerical efficiency and design effectiveness are examined and further improvements discussed.

Stability Analysis of Power Grid Connected With Direct-Drive Wind Farm Containing Virtual Inertia Based on Integrated Dissipation Energy Model

Concerning the difficulty in unified modeling of direct-drive wind farm with virtual inertia and the unknown cause of low frequency oscillation, a detailed model of integrated dissipation energy of direct-drive wind farm is built. First, the dissipation energy model of wind generator and the integrated dissipation energy model of direct-drive wind farm containing virtual inertia and PLL is derived and built. On this basis, according to different sources of integrated dissipation energy, it is decomposed into free dissipation energy, resonant dissipation energy and forced dissipation energy. These three components respectively characterize the contribution of inherent damping, generator-grid coupled resonance and generator-grid interaction to the stability of system. And then, how the variation of wind speed and generator location affects the stability of system is analyzed quantitatively using the integrated dissipation energy. Finally, the model of IEEE 10-machine 39-bus system connected with direct-drive wind farm is built in RTDS for hardware-in-the-loop tests. Simulation results verify that, the detailed model exhibits three different oscillation states, and each generator in the wind farm contributes differently to system oscillation. The traditional equivalent model of wind farm only exhibits one oscillation state and cannot reflect the difference in contributions of generators, thus it may wrongly identify the stability of system, even aggravate oscillation.

A new method for simulating multiple wind turbine wakes under yawed conditions

Counter-rotating vortices generated in wake steering not only deform the turbine wake, but also can make the wake trajectory of a non-yawed downwind turbine deviate from its rotor centerline, referred to as “secondary wake steering” phenomenon. Recent studies have also shown that the vortex interactions become clearer when the wind farm includes multiple turbines. However, in the common analytical models for active yaw control, the effects of these vortices are not considered. Evidently, this omission can lead to a decrease in model prediction accuracy. To compensate for it, a new analytical wind farm model is proposed. It adopts a physical-based momentum conserving wake superposition method to deal with the interaction of multiple wakes, in which, not only combining the streamwise velocity deficit of each individual yawed wind turbine, but also the transverse velocity from different wakes. Additionally, an “added yaw angle” is defined for a downwind turbine operating in upstream yawed turbine wakes, to reflect the change in local wind direction it perceives. For validation purposes, the LES wind field obtained from the SOWFA tool is used as a reference, and the newly proposed model is found to agree well with LES results and outperforms the representative conventional analytical model in almost all test cases. The new model can successfully reproduce the “secondary wake steering” phenomenon in the overlapped wake, and provides significant improvements in predicting power production of wind turbines.

Deep learning-aided model predictive control of wind farms for AGC considering the dynamic wake effect

To provide automatic generation control (AGC) service, wind farms (WFs) are required to control their operation dynamically to track the time-varying power reference. Wake effects impose significant aerodynamic interactions among turbines, which remarkably influence the WF dynamic power production. The nonlinear and high-dimensional nature of dynamic wake model, however, brings extremely high computation complexity and obscure the design of WF controllers. This paper overcomes the control difficulty brought by the dynamic wake model by proposing a novel control-oriented reduced order WF model and a deep-learning-aided model predictive control (MPC) method. Leveraging recent advances in computational fluid dynamics (CFD) to provide high-fidelity data that simulates WF dynamic wake flows, two novel deep neural network (DNN) architectures are specially designed to learn a dynamic WF reduced-order model (ROM) that can capture the dominant flow dynamics. Then, a novel MPC framework is constructed that explicitly incorporates the obtained WF ROM to coordinate different turbines while considering dynamic wake interactions. The proposed WF ROM and the control method are evaluated in a widely-accepted high-dimensional dynamic WF simulator whose accuracy has been validated by realistic measurement data. A 9-turbine WF case and a larger 25-turbine WF case are studied. By reducing WF model states by many orders of magnitude, the computational burden of the control method is reduced greatly. Besides, through the proposed method, the range of AGC signals that can be tracked by the WF in the dynamic operation is extended compared with the existing greedy controller.

Wind farms in AGC: Modelling, simulation and validation

Wind farms are increasingly interested in participating in the secondary frequency control, especially in power systems where AGC is organized by regulation zones comprising generation units of different technologies. In order to estimate the wind farms behaviour and overall impact on automatic generation control (AGC), a reasonably simple wind farm model is needed. First-order and second-order linear models of wind farms are proposed in the literature in AGC-related studies. However, these simple models neglect key dynamic features for AGC integration of wind farms, such as artificial ramp limitations, dead bands, communication delays and start-up delays caused by turbine orientation. This paper presents a still simple wind farm model, however representing all relevant dynamics and availability constraints at power system control level. Applying an illustrative case study, model parameters are tuned and validated by means of field measurements, recorded during a response trail run of a real wind farm of 30 MW installed power operating within the Spanish power system.

Simplified wake modelling for wind farm load prediction

This paper presents a simple numerical wind farm model, where pragmatic choices are made in the modelling of underlying physical processes, with the aim of making useful power production and wind turbine load estimates. The numerical model decomposes the wind farm, inspired by the approach of the dynamic wake meandering model (DWM), into simple sub-models for a single wake deficit (1D Gaussian), wake meandering (statistical), and wake added turbulence (eddy viscosity based). Particular attention is given to selecting a momentum conserving wake summation method, because of its critical role in coupling the influence of individual wakes. Results are presented to illustrate the influence that wake summation methods have on equilibrium velocity and power production in a row of turbines, for different inter-turbine spacing and inflow velocities. Comparisons against published data from the Lillgrund wind farm illustrate that the suggested modelling approach reproduces important trends observed in the field data.

Wind farm wake modeling based on deep convolutional conditional generative adversarial network

Modeling of wind farm wakes is of great importance for the optimal design and operation of wind farms. In this work a surrogate modeling method for parametrized fluid flows is proposed for wind farm wake modeling, based on the state-of-the-art deep learning framework i.e. deep convolutional conditional generative adversarial network. Based on the proposed method and the data generated by high-fidelity large eddy simulations, a novel wind farm wake model is developed. The developed model is first validated against high-fidelity data and the results show that it achieves accurate, efficient, and robust prediction of wind turbine wake flow, at all the streamwise locations including both near wake and far wake, for both streamwise and spanwise velocity components, and at the cases with different inflow wind profiles. Then an extensive parametric study is carried out and the results show that the model generalizes well to unknown flow scenarios. Furthermore, a case study for a wind farm is investigated by the developed model. The prediction results are then compared with high-fidelity simulations, showing that the model can predict the wind farm wake flow (including both the streamwise and spanwise velocity fields) very well.

Simplified Floating Wind Turbine for Real-Time Simulation of Large-Scale Floating Offshore Wind Farms

Floating offshore wind has received more attention due to its advantage of access to incredible wind resources over deep waters. Modeling of floating offshore wind farms is essential to evaluate their impacts on the electric power system, in which the floating offshore wind turbine should be adequately modeled for real-time simulation studies. This study proposes a simplified floating offshore wind turbine model, which is applicable for the real-time simulation of large-scale floating offshore wind farms. Two types of floating wind turbines are evaluated in this paper: the semi-submersible and spar-buoy floating wind turbines. The effectiveness of the simplified turbine models is shown by a comparison study with the detailed FAST (Fatigue, Aerodynamics, Structures, and Turbulence) floating turbine model. A large-scale floating offshore wind farm including eighty units of simplified turbines is tested in parallel simulation and real-time software (OPAL-RT). The wake effects among turbines and the effect of wind speeds on ocean waves are also taken into account in the modeling of offshore wind farms. Validation results show sufficient accuracy of the simplified models compared to detailed FAST models. The real-time results of offshore wind farms show the feasibility of the proposed turbine models for the real-time model of large-scale offshore wind farms.