Power Control Of Wind Farm Research Articles

Hardware-in-the-Loop Simulation of Flywheel Energy Storage Systems for Power Control in Wind Farms

Hardware-in-the-Loop Simulation of Flywheel Energy Storage Systems for Power Control in Wind Farms

Hierarchical power control of a large-scale wind farm by using a data-driven optimization method.

With the participation in automatic generation control (AGC), a large-scale wind farm should distribute the real-time AGC signal to numerous wind turbines (WTs). This easily leads to an expensive computation for a high-quality dispatch scheme, especially considering the wake effect among WTs. To address this problem, a hierarchical power control (HPC) is constructed based on the geographical layout and electrical connection of all the WTs. Firstly, the real-time AGC signal of the whole wind farm is distributed to multiple decoupled groups in proportion of their regulation capacities. Secondly, the AGC signal of each group is distributed to multiple WTs via the data-driven surrogate-assisted optimization, which can dramatically reduce the computation time with a small number of time-consuming objective evaluations. Besides, a high-quality dispatch scheme can be acquired by the efficient local search based on the dynamic surrogate. The effectiveness of the proposed technique is thoroughly verified with different AGC signals under different wind speeds and directions.

Power train degradation modelling for multi-objective active power control of wind farms

With the coming installation of hundreds of GW of offshore wind power, penetration of the inherent power fluctuations into the electricity grid will become significant. Therefore, the use of wind farms as power reserve providers to support the regulation of the grid’s voltage and frequency through delivering a desired power is expected to increase. As a result, wind turbines will not be necessarily delivering the maximum available power anymore – known as curtailed or derated operation – and will have to be able to deal with time-variant power demand. For this purpose, power setpoints from the grid are dispatched at the farm-level and then tracked at the turbine-level under the constraint of available power in the wind (known as active power control). The idea of this work is taking advantage of the additional degree of freedom lying in the power dispatch between turbines when operating in curtailed conditions. As failure of power train system components is frequent, costly and predictable, we seek to introduce power train degradation into the farm control objectives. To this end, a data-driven model of drivetrain fatigue damage as function of wind conditions and derating factor adapted to the farm active power control objective function is developed based on the pre-analysis of single-turbine simulations and degradation calculations, where the increased turbulence intensity due to wind farm wake effect is also considered. The proposed analytical power train degradation model is computationally efficient, can reflect the fatigue damage of individual gears and bearings in the overall power train life function and in contrast with high-fidelity models can be easily adjusted for different drivetrain configurations. A case study on the TotalControl reference wind power plant is demonstrated.

Heteroscedastic Bayesian Optimisation for Active Power Control of Wind Farms*

Active power control of wind farms remains an open challenge due to inherent noise in wind power that arises from uncertain wind speed measurements and plant/model mismatch. To leverage the heteroscedastic nature of the wind power noise, heteroscedastic Bayesian optimisation (BO) is used for active power control of wind farms. BO utilises closed-loop performance data to tune the parameters of a stochastic model predictive controller (SMPC) in a systematic and data-efficient manner. This, in turn, allows for enhancing the closed-loop performance of the controller intended to decrease the power tracking error. A case study with 9 turbines in a 3 × 3 wind farm shows that the heteroscedastic BO approach achieves a reduced closed-loop power tracking error in terms of root-mean-square by 8.89% compared to one that relies on nominal BO and a decrease by 64.99% compared to a nominal model predictive controller (MPC) whose performance is not tuned using closed-loop data and BO.

Two-Stage Optimal Re/Active Power Control of Large-Scale Wind Farm to Mitigate Voltage Dip Induced Frequency Excursion

A two-stage optimal active and reactive power coordinated control strategy of large-scale wind farm (WF) to mitigate voltage dip induced frequency excursion (VDIFE) is proposed in this letter. Before the voltage fault occurs, the power reference look-up table of each wind turbine (WT) are calculated. When voltage fault occurs, the power reference values of each WT are optimized based on model predictive control. Distinguished from existing works, the proposed optimal power control can better mitigate VDIFE and balance the variation of rotor speed of each WT. Case studies on a doubly-fed-induction-generator based WF built in MATLAB/Simulink are carried out to demonstrate the effectiveness of the proposed control scheme.

Model Predictive Control for Wind Farm Power Tracking With Deep Learning-Based Reduced Order Modeling

Dynamic power control of wind farms (WFs) is necessary to provide automatic generation control (AGC) services for the power system. However, cooperative WF control for AGC remains a great challenge because of the nonlinear and high-dimensional nature of the wake flow dynamics. To address this challenge, this article proposes a model predictive control (MPC) framework with deep learning-based reduced-order modeling (ROM). Two novel neural network architectures are designed, which successfully formulate a WF ROM capturing the dominant wake steering dynamics in a computationally efficient manner. Compared to physical models, the data-driven ROM reduces the number of model states by orders of magnitude. Then, a novel WF AGC framework embedding the derived WF ROM is proposed. Thrust coefficient and yaw steering are both employed to optimize WF power tracking performance. Compared to prior WF AGC controllers, the dynamic yaw actuation is first optimized for AGC considering the wake steering dynamics. Case studies validate the effectiveness of the deep learning-based WF ROM at capturing the wake traveling dynamics. The WF controllers were stress-tested under time-varying inflow directions. The proposed MPC can react to different wind directions and generates higher-quality control performance than existing alternatives with extended trackable AGC range and better dynamic power tracking performance.

Adaptive Response Method for Communication Failures of Hierarchical Reactive Power Control in Wind Farms

Advanced controllers have been widely introduced in power system-related research to alleviate the uncertainties of renewable sources. The performance of the controllers should be verified in a realistic environment before the controllers are used in practical applications. This study focuses on voltage regulation of wind farms in voltage drop cases where communication failures occur and discusses modifications done on the online wind farm management system. The modified management system is designed to respond to a voltage drop when the outer reference signal from the upper-level controller is absent due to technical problems, especially communication errors. Such a situation has not been considered in most of the management systems in the literature. The predictable situations in real fields have been further verified under the hardware-in-the-loop simulation environment RTDS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TM</sup> . During communication failure, the proposed method can respond automatically to voltage recovery, along with a designed objective function. A comparative analysis of actual control modes was also conducted to prove the relevance and effectiveness of the proposed method.

Construction of offline predictive controller for wind farm based on CNN–GRNN

In order to reduce Levelized Cost of Energy(LCOE), the wind farm development trend includes mainly two points: (1) rotor diameter of wind turbines will be larger, (2) density of offshore facilities will be higher. Consequently, the wake interference effect will be more severe, and predictive control has become one of the main studies for reducing wake disturbance. However, most of these strategies are usually computationally expensive and have limitations for large-scale wind farms since the optimization cost tends to increase exponentially with prediction horizons and number of wind turbines, which is unfavorable for real-time control. Considering control effectiveness and online computational efficiency, an offline predictive controller using convolutional neural network-general regression neural network (CNN–GRNN) is proposed for wind farm power control considering fatigue load. A CNN–GRNN hybrid network is trained to carry out the mapping between system states and optimal control settings calculated by particle swarm optimization (PSO) algorithm under various wind directions and wind speeds, and CNN–GRNN acts as an offline optimizer to provide ideal control laws with negligible online computation workload. The power at Horns Rev 1 can be improved up to 5.6% in comparative experiments with lookup table (LUT) strategy while keeping the fatigue load within the preset limitation, and the extensive simulation results under various wind conditions indicate that this proposed strategy can provide a more desirable performance for wind farm power control with fatigue constraint while retaining the advantage of negligible online computational burden.

An ultra‐short‐term wind speed prediction model using LSTM based on modified tuna swarm optimization and successive variational mode decomposition

AbstractAccurate ultra‐short‐term wind speed prediction is extremely important for the power control of wind farms, the safe dispatch of power systems, and the stable operation of power grids. At present, most wind farms mainly rely on supervisory control and data acquisition systems to obtain operation and maintenance data which includes operating characteristics of wind turbines. In the ultra‐short‐term wind speed prediction, a long short‐term memory network is one of the commonly used deep learning methods. To address the problem that improper selection of long short‐term memory network's hyperparameters may affect the prediction results, In the present study, a hybrid prediction model based on the long short‐term memory and the modified tuna swarm optimization algorithm was established, and was used to predict after the wind speed sample data had been decomposed by successive variational mode decomposition method. The experimental results reveal that the proposed model effectively improved the accuracy of wind speed prediction for wind farms compared with the support vector regression, deep belief networks, and long short‐term memory models optimized by particle swarm optimization algorithm.

Active power control of wind farms: an instantaneous approach on waked conditions

This paper presents a closed-loop controller for wind farms to provide active power control services using a high-fidelity computational fluid dynamics based wind plant simulator. The proposed design enhances power tracking stability and allows for simple understanding, where each turbine is considered as a pure time-delay system. The paper investigates the control performance with different nominal power distributions in a fully waked condition and limited power availability. Results demonstrate the improvement in power production obtained by closing the control loop, compared to greedy operation. Additionally, power tracking capabilities are enhanced with a nominal power distribution favored by axial-induction, as well as the occurrence of turbine saturation and the distribution of loads.

A Wind Farm Power Control Strategy for Dust Content in Wind Turbine Cabin

To balance the benefit of dust content and the negative influence of fatigue load due to the wake flow effect in wind farms, a wind farm power control strategy for dust content in the wind turbine cabin is proposed and the fatigue load is also taken into account. A wind farm active power optimization control model is established by deriving the linear relationship between the active power of wind turbines, the wake flow fluctuation, the dust content in the cabin, and the fatigue load of wind turbines. Then, by coordinating the active power between wind turbines, the active power output requirement of wind farms is demanded, and the dust content and fatigue load can be effectively controlled. To verify the effectiveness of the proposed control strategy, an IEEE-RTS 79 test system is established in this paper, and wind farms will be controlled to participate in the system frequency regulation.

Power Control and Management of DFIGs Wind Farm Equipped with Aggregation Methods by Using Local Supervision Unit Based on S-Function Builder

Power Control and Management of DFIGs Wind Farm Equipped with Aggregation Methods by Using Local Supervision Unit Based on S-Function Builder

A Review on the Application of the MPC Technology in Wind Power Control of Wind Farms

Due to the uncertainty and volatility of wind speed, its natural output power also exhibits strong volatility. In order to avoid the negative effects of the excessive fluctuation of the output power of wind turbines in the wind farm and the power grid, it is very important to accurately predict and reasonably plan and control the output power of the wind farm. Model predictive control (MPC) is a type of advanced control method that can deal with a multi-input multi-output nonlinear system. Compared to the traditional proportional integral derivative control, MPC is more suitable for the complex wind farm model and exhibits good control performance, and has been gradually applied to control the wind power in wind farms. In this article, we have summarized the application of the MPC technology in the prediction and control of wind power in a wind farm, analyze the application of the MPC technology, including MPC, multi-objective MPC, nonlinear MPC, and distributed MPC, in the wind farm power control with different optimization objectives. In addition, the optimization of the active and reactive power of wind farms has also been discussed in detail. Furthermore, some hot topics in the current research, such as the multi-objective optimization problem of coordinating the maximum power and reducing the fatigue load and power smoothing control problem, have been summarized. Finally, the existing problems of MPC applied to wind power systems have been discussed.

Distributed optimal active and reactive power control for wind farms based on ADMM

In this paper, a distributed optimal active and reactive power control (DARPC) strategy based on the alternating direction method of multipliers (ADMM) is proposed for wind farms (WFs). The WFs operate in a distributed manner to minimize the network power loss, voltage deviations of buses from the rated voltage, and active power output deviations of WTs from their proportional distribution (PD)-based power reference. An optimization problem is formulated as a quadratic programming (QP) problem by using the linearized DistFlow model. The ADMM-based solution is used to decompose the centralized optimization problem to several subproblems which are solved in individual local controllers with exchanged information from their practical neighbor controllers. Compared with existing distributed/hierarchical optimal control, the impacts of the active power injection from the WTs are taken into consideration and optimized while meeting the dispatch command from the transmission system operator (TSO). The ADMM-based distributed solution eliminates the requirement of a central unit. Compared with conventional centralized optimal control, the scalability of the WFs is improved. A WF consisting of 20 WTs is simulated in MATLAB/Simulink to test the control effectiveness of the proposed DARPC strategy.

Rejecting wake-rotor overlapping load disturbances: An extension to active power control of wind farms

This paper studies an extension to the active power control of wind farms for further structural load alleviation of waked wind turbines. We demonstrate that the structural fatigue loading of a downstream turbine, which its rotor overlaps with wakes of its upwind turbine, can be significantly alleviated, while the wind farm power production follows a power reference signal. A load variations model is proposed, based on the multi-rotor concept, to sense the variations of the wind velocity and the associated loading across the rotor area of a single wind turbine. Then, an individual rotor control is proposed to reject the rotor-wake overlapping load disturbances about an operating condition, commanded with the closed-loop APC at the wind farm level. The applicability and key features of the controller are discussed with a wind farm example consisting of 2×2 turbines. A large-eddy simulation model is employed for resolving the turbulent flow, the wake structures and its interaction with an atmospheric boundary layer. The effectiveness of the proposed load disturbance rejection is evaluated using the damage equivalent load of the tower base fore-aft bending and torsional moments of the individual wind turbines.

Economic power control for offshore wind farms with loop connection cables

Abstract The Canadian developer and owner of green power facilities Northland Power Inc. owns two offshore wind farms (OWFs) in the German Bight, Deutsche Bucht and Nordsee One, operated by the subsidiary Northland Power Europe GmbH. The company supports and conducts research in the field of power flow optimization in wind farm networks. The work at hand represents the results of this effort to maximize the efficiency of the assets. The project was accomplished within a cooperation between the Nordsee One GmbH and the Institute of Electrical Power and Energy Technology at the Hamburg University of Technology. From an external point of view, an OWF represents an “en bloc” power plant connected to the onshore transmission grid via power export cables. Nevertheless, such a power plant comprises a complex, large-scale internal medium voltage network. In case of failure or cable outage, the network topology of an OWF may be modified and unintended overloading of inter-array cables (IACs) is possible. In order to address this issue, a new algorithm and software tool for economic power control in OWFs are introduced in the following which can be employed in wind farms with integrated loop connection cables (LCCs). This configuration particularly entails the risk of overloading cable segments depending on the present wind speed. The new algorithm provides the operator with adapted active power setpoints for each wind turbine generator (WTG) in a given network topology. The aim is to maximize OWF power generation and minimize internal power losses while secure network operation is guaranteed. Using load flow analysis based on WTG power output measurements, the load on each cable section is monitored and the cables can be utilized to their individual full capacity while overload is avoided. The practicability of the approach is demonstrated by means of simulation results.

Wind-DFIG wireless controlled using EGPRS standard applied to the ancillary services in a smart grid environment

This paper presents an alternative topology analysis for reactive power control in wind farms with wireless communication link integration. The co-simulation environment with telecommunication, machine control, and power system demonstrate in this analysis, that it can be an useful solution for planning the operation of modern power systems and their ancillary services in a smart grid scenario. A low cost and easy implementation EGPRS standard wireless link was adopted to perform the communication from the smart grid operator and the wind farm with the objective of maintain the voltage levels under established limits. The wireless communication was used to send the value of measured voltage of one critical bar to the operator and to return the reactive power reference to the wind farm. The studied scenario is represented by a seven bar power system model connected to a DFIG wind farm. The results obtained by this co-simulation endorse this original proposal and show its operability.

Research on Reactive Power and Voltage Control for Wind Farm Based on coordinate control of DFIGs and SVG

Abstract Wind farm generally uses long-distance transmission mode to transmit electricity, because the grid can not fully support the grid voltage of wind farm. When the wind speed changes, there is a problem of voltage instability at the grid-connection point. However, the reactive power compensation device of the power grid can not meet the requirements of fast reactive power response and compensation. Although doubly-fed induction generators (DFIGs) have fast power decoupling control, its reactive power is limited by active power output. In order to meet the reactive power demand of the power grid, the instability of the voltage at the grid-connection point is solved by coordinating the reactive power output capacity of DFIGs and the Static Var Generator (SVG). This paper proposes a coordinated control method of reactive voltage in wind power plant based on Starsim, which uses the upper computer to send voltage control instructions to control the reactive output of the simulation model in Starsim. The simulation model is built in MATLAB, and the simulation model is put into Starsim. Through the simulation, it is concluded that the control method could stabilize the grid voltage of wind farm.

Wind Turbine Clustering Algorithm of Large Offshore Wind Farms considering Wake Effects

This paper proposed the SVD (singular value decomposition) clustering algorithm to cluster wind turbines into some group for a large offshore wind farm, in order to reduce the high-dimensional problem in wind farm power control and numerical simulation. Firstly, wind farm wake relationship matrixes are established considering the wake effect in an offshore wind farm, and the SVD of wake relationship matrixes is used to cluster wind turbines into some groups by the fuzzy clustering algorithm. At last, the Horns Rev offshore wind farm is analyzed to test the clustering algorithm, and the clustering result and the power simulation show the effectiveness and feasibility of the proposed clustering strategy.

Hierarchical Active Power Control of DFIG-Based Wind Farm With Distributed Energy Storage Systems Based on ADMM

A hierarchical active power control (HAPC) scheme based on the alternating direction method of multipliers (ADMM) is proposed for doubly-fed induction generator (DFIG)-based wind farms with distributed energy storage systems (ESSs). The wind farm controller optimizes the active power references for DFIG-based wind turbines (WTs) and ESSs inside the wind farm, and the aim is to minimize fatigue loads by minimizing variations of thrust force and shaft torque of WTs while tracking the dispatch command from the transmission system operator (TSO). Moreover, ESSs management is considered in the central controller and WT controllers. The solution method based on the ADMM with fast convergence is used to solve the model predictive control (MPC) optimization problem in a hierarchical manner without loss of optimality. With the ADMM, the control task is distributed to WT controllers, and the computation burden of the central wind farm controller can be efficiently reduced. A wind farm with 30 DFIG WTs was used to validate the control performance of the proposed HAPC scheme.