SSO Risk Evaluation for a Grid-connected PMSG-based Wind Farm Based on Sequential Monte Carlo Simulation

In a grid-connected wind farm based on permanent magnet synchronous generators(PMSGs),the wind speed and the number of operating PMSGs are the two most important influencing factors along with the stochastic nature of sub-synchronous oscillation(SSO)from the point view of the farm.This paper proposes a method of unstable SSO risk evaluation for grid-connected PMSG-based wind farms based on the sequential Monte Carlo simulation(SMCS).The determination of critical wind speed(CWS)of SSO and the sequential simulation strategy of wind speed states and PMSG states in a wind farm at the same wind speed(S-WF),as well as in a wind farm at different wind speeds(D-WF),are studied.Five indices evaluating the expectation,duration,frequency and energy loss of SsO risk are proposed.Moreover,a strategy to reduce SsO risk by adjusting the cut-in wind speed is discussed.The effectiveness of the discussed issues in this paper are proved by the case studies of a 750-PMSG wind farm based on the actual wind speed data collected.

Transient Stability Preventive Control of Wind Farm Connected Power System Considering the Uncertainty

To address uncertainty as well as transient stability constraints simultaneously in the preventive control of windfarm systems, a novel three-stage optimization strategy is established in this paper. In the first stage, the probabilisticmulti-objective particle swarm optimization based on the point estimate method is employed to cope with thestochastic factors. The transient security region of the system is accurately ensured by the interior point methodin the second stage. Finally, the verification of the final optimal objectives and satisfied constraints are enforcedin the last stage. Furthermore, the proposed strategy is a general framework that can combine other optimizationalgorithms. The proposed methodology is tested on the modified WSCC 9-bus system and the New England 39-bussystem. The results verify the feasibility of the method.

GNN Representation Learning and Multi-Objective Variable Neighborhood Search Algorithm for Wind Farm Layout Optimization

With the increasing demand for electrical services,wind farm layout optimization has been one of the biggest challenges that we have to deal with.Despite the promising performance of the heuristic algorithm on the route network design problem,the expressive capability and search performance of the algorithm on multi-objective problems remain unexplored.In this paper,the wind farm layout optimization problem is defined.Then,a multi-objective algorithm based on Graph Neural Network(GNN)and Variable Neighborhood Search(VNS)algorithm is proposed.GNN provides the basis representations for the following search algorithm so that the expressiveness and search accuracy of the algorithm can be improved.The multi-objective VNS algorithm is put forward by combining it with the multi-objective optimization algorithm to solve the problem with multiple objectives.The proposed algorithm is applied to the 18-node simulation example to evaluate the feasibility and practicality of the developed optimization strategy.The experiment on the simulation example shows that the proposed algorithm yields a reduction of 6.1% in Point of Common Coupling(PCC)over the current state-of-the-art algorithm,which means that the proposed algorithm designs a layout that improves the quality of the power supply by 6.1%at the same cost.The ablation experiments show that the proposed algorithm improves the power quality by more than 8.6% and 7.8% compared to both the original VNS algorithm and the multi-objective VNS algorithm.

Dynamics and Small Signal Stability Analysis of PMSG-based Wind Farm with an MMC-HVDC System

The permanent magnet synchronous generator (PMSG)-based wind farm with a modular multilevel converter (MMC) based HVDC system exhibits various oscillations and can experience dynamic instability due to the interactions between different controllers of the wind farm and MMC stations, which have not been properly examined in the existing literatures. This paper presents a dynamic modeling approach for small signal stability analysis of PMSG-based wind farms with a MMC- HVDC system. The small signal model of the study system is validated by the comprehensive electromagnetic transient (EMT) simulations in PSCAD/EMTDC. Then the eigenvalue approach and participation factors analysis are utilized to comprehensively evaluate the impact of different controllers, system’s parameters and the circulating current suppressing controller (CCSC) on the small signal stability of the entire system. From eigenvalue analysis, it is revealed that as the output active power of the wind farm increases within the rated range, the overall system will exhibit a sub-synchronous oscillation (SSO) instability mode, an extremely weak damping mode, and a low frequency oscillation instability mode. From participation factors analysis, it is observed that the SSO mode and weak damping mode are primarily related to the internal dynamics of the MMC, which can be suppressed or improved by CCSC. It is determined that the low frequency oscillation mode is primarily caused by the interactions between the phase locked loop (PLL) control of the wind farm and the voltage and frequency (V-F) control of the MMC station. The analysis also depicts that the larger proportional gain value of the V-F control of the MMC station and smaller PLL bandwidth of the wind farm can enhance the small signal stability of the entire system.

Wind farm active power dispatching algorithm based on Grey Incidence

This study proposes a wind farm active power dispatching(WFAPD) algorithm based on the grey incidence method, which does not rely on an accurate mathematical model of wind turbines. Based on the wind turbine start-stop data at different wind speeds, the weighting coefficients, which are the participation degrees of a variable speed system and a variable pitch system in power regulation, are obtained using the grey incidence method. The incidence coefficient curve is fitted by the B-spline function at a full range of wind speeds, and the power regulation capacity of all wind turbines is obtained. Finally, the WFAPD algorithm, which is based on the regulating capacity of each wind turbine, is compared with the wind speed weighting power dispatching(WSWPD) algorithm in MATLAB. The simulation results show that the active power fluctuation of the wind farm is smaller, the rotating speed of wind turbines is smoother, and the fatigue load of highspeed turbines is effectively reduced.

Wake Effects on A Hybrid Semi-Submersible Floating Wind Farm with Multiple Hub Heights

Wind farms generally consist of a single turbine installed with the same hub height. As the scale of turbines increases,wake interference between turbines becomes increasingly significant, especially for floating wind turbines(FWT).Some researchers find that wind farms with multiple hub heights could increase the annual energy production(AEP),while previous studies also indicate that wake meandering could increase fatigue loading. This study investigates the wake interaction within a hybrid floating wind farm with multiple hub heights. In this study, FAST.Farm is employed to simulate a hybrid wind farm which consists of four semi-submersible FWTs(5MW and 15MW) with two different hub heights. Three typical wind speeds(below-rated, rated, and over-rated) are considered in this paper to investigate the wake meandering effects on the dynamics of two FWTs. Damage equivalent loads(DEL) of the turbine critical components are computed and analyzed for several arrangements determined by the different spacing of the four turbines. The result shows that the dynamic wake meandering significantly affects downstream turbines’ global loadings and load effects. Differences in DEL show that blade-root flapwise bending moments and mooring fairlead tensions are sensitive to the spacing of the turbines.

A Chaotic Local Search-Based Particle Swarm Optimizer for Large-Scale Complex Wind Farm Layout Optimization

Wind energy has been widely applied in power generation to alleviate climate problems.The wind turbine layout of a wind farm is a primary factor of impacting power conversion efficiency due to the wake effect that reduces the power outputs of wind turbines located in downstream.Wind farm layout optimization(WFLO)aims to reduce the wake effect for maximizing the power outputs of the wind farm.Nevertheless,the wake effect among wind turbines increases significantly as the number of wind turbines increases in the wind farm,which severely affect power conversion efficiency.Conventional heuristic algorithms suffer from issues of low solution quality and local optimum for large-scale WFLO under complex wind scenarios.Thus,a chaotic local search-based genetic learning particle swarm optimizer(CGPSO)is proposed to optimize large-scale WFLO problems.CGPSO is tested on four larger-scale wind farms under four complex wind scenarios and compares with eight state-of-the-art algorithms.The experiment results indicate that CGPSO significantly outperforms its competitors in terms of performance,stability,and robustness.To be specific,a success and failure memories-based selection is proposed to choose a chaotic map for chaotic search local.It improves the solution quality.The parameter and search pattern of chaotic local search are also analyzed for WFLO problems.

Performance of the CMA-GD Model in Predicting Wind Speed at Wind Farms in Hubei, China

This study assesses the predictive capabilities of the CMA-GD model for wind speed prediction in two wind farms located in Hubei Province,China.The observed wind speeds at the height of 70m in wind turbines of two wind farms in Suizhou serve as the actual observation data for comparison and testing.At the same time,the wind speed predicted by the EC model is also included for comparative analysis.The results indicate that the CMA-GD model performs better than the EC model in Wind Farm A.The CMA-GD model exhibits a monthly average correlation coefficient of 0.56,root mean square error of 2.72 m s^(-1),and average absolute error of 2.11 m s^(-1).In contrast,the EC model shows a monthly average correlation coefficient of 0.51,root mean square error of 2.83 m s^(-1),and average absolute error of 2.21 m s^(-1).Conversely,in Wind Farm B,the EC model outperforms the CMA-GD model.The CMA-GD model achieves a monthly average correlation coefficient of 0.55,root mean square error of 2.61 m s^(-1),and average absolute error of 2.13 m s^(-1).By contrast,the EC model displays a monthly average correlation coefficient of 0.63,root mean square error of 2.04 m s^(-1),and average absolute error of 1.67 m s^(-1).

Characteristics of sub-synchronous oscillation in grid-connected wind farm system

With the rapid development of wind power, wind turbines are accompanied by a large quantity of power electronic converters connected to the grid, causing changes in the characteristics of the power system and leading to increasingly serious sub-synchronous oscillation (SSO) problems, which urgently require the generalized classification and characterization of the emerging oscillation problems. This paper classifies and characterizes the emerging types of SSO caused by grid-connected wind turbines to address these issues. Finally, the impact of the typical system parameters changes on the oscillation pattern is analyzed in depth to provide effective support for the subsequent suppression and prevention of SSO.

Research on Asymmetric Fault Location of Wind Farm Collection System Based on Compressed Sensing

Aiming at the problem that most of the cables in the power collection systemof offshore wind farms are buried deep in the seabed,whichmakes it difficult to detect faults,this paper proposes a two-step fault location method based on compressed sensing and ranging equation.The first step is to determine the fault zone through compressed sensing,and improve the datameasurement,dictionary design and algorithmreconstruction:Firstly,the phase-locked loop trigonometric functionmethod is used to suppress the spike phenomenon when extracting the fault voltage,so that the extracted voltage valuewillnot have a large error due to the voltage fluctuation.Secondly,theλ-NIM dictionary is designed by using the node impedancematrix and the fault location coefficient to further reduce the influence of pseudo-fault points.Finally,the CoSaMP algorithmis improved with the generalized Jaccard coefficient to improve the reconstruction accuracy.The second step is to use the ranging equation to accurately locate the asymmetric fault of the wind farm collection system on the basis of determining the fault interval.The simulation results show that the proposedmethod ismore accurate than the compressedsensingmethod andimpedancemethod in fault section location and fault location accuracy,the relative error is reduced from 0.75%to 0.4%,and has a certain anti-noise ability.

Research on Reactive Power Optimization of Offshore Wind Farms Based on Improved Particle Swarm Optimization

The lack of reactive power in offshore wind farms will affect the voltage stability and power transmission quality of wind farms.To improve the voltage stability and reactive power economy of wind farms,the improved particle swarmoptimization is used to optimize the reactive power planning in wind farms.First,the power flow of offshore wind farms is modeled,analyzed and calculated.To improve the global search ability and local optimization ability of particle swarm optimization,the improved particle swarm optimization adopts the adaptive inertia weight and asynchronous learning factor.Taking the minimum active power loss of the offshore wind farms as the objective function,the installation location of the reactive power compensation device is compared according to the node voltage amplitude and the actual engineering needs.Finally,a reactive power optimizationmodel based on Static Var Compensator is established inMATLAB to consider the optimal compensation capacity,network loss,convergence speed and voltage amplitude enhancement effect of SVC.Comparing the compensation methods in several different locations,the compensation scheme with the best reactive power optimization effect is determined.Meanwhile,the optimization results of the standard particle swarm optimization and the improved particle swarm optimization are compared to verify the superiority of the proposed improved algorithm.

Cyber-physical Modeling Technique based Dynamic Aggregation of Wind Farm Considering LVRT Characteristic

With the increasing penetration of wind power,large-scale integrated wind turbine brings stability and security risks to the power grid.For the aggregated modeling of large wind farms,it is crucial to consider low voltage ride-through(LVRT)characteristics.However,in aggregation methods,the approximate neglect behavior is essential,which leads to inevitable errors in the aggregation process.Moreover,the lack of parameters in practice brings new challenges to the modeling of a wind farm.To address these issues,a novel cyber-physical modeling method is proposed.This method not only overcomes the aggregation problem under the black-box wind farm but also accurately realizes the aggregation error fitting according to the operation data.The simulation results reveal that the proposed method can accurately simulate the dynamic behaviors of the wind farm in various scenarios,whether in LVRT mode or normal mode.

A nonlinear wake model of a wind turbine considering the yaw wake steering

Duo to fluctuations in atmospheric turbulence and yaw control strategies,wind turbines are often in a yaw state.To predict the far wake velocity field of wind turbines quickly and accurately,a wake velocity model was derived based on the method of momentum conservation considering the wake steering of the wind turbine under yaw conditions.To consider the shear effect of the vertical incoming wind direction,a two-dimensional Gaussian distribution function was introduced to model the velocity loss at different axial positions in the far wake region based on the assumption of nonlinear wake expansion.This work also developed a“prediction-correction”method to solve the wake velocity field,and the accuracy of the model results was verified in wake experiments on the Garrad Hassan wind turbine.Moreover,a 33-kW two-blade horizontal axis wind turbine was simulated using this method,and the results were compared with the classical wake model under the same parameters and the computational fluid dynamics(CFD)simulation results.The results show that the nonlinear wake model well reflected the influence of incoming flow shear and yaw wake steering in the wake velocity field.Finally,computation of the wake flow for the Horns Rev offshore wind farm with 80 wind turbines showed an error within 8%compared to the experimental values.The established wake model is less computationally intensive than other methods,has a faster calculation speed,and can be used for engineering calculations of the wake velocity in the far wakefield of wind turbines.

A call for enhanced data-driven insights into wind energy flow physics

With the increased availability of experimental measurements aiming at probing wind resources and wind turbine operations,machine learning(ML)models are poised to advance our understanding of the physics underpinning the interaction between the atmospheric boundary layer and wind turbine arrays,the generated wakes and their interactions,and wind energy harvesting.However,the majority of the existing ML models for predicting wind turbine wakes merely recreate Computational fluid dynamics(CFD)simulated data with analogous accuracy but reduced computational costs,thus providing surrogate models rather than enhanced data-enabled physics insights.Although ML-based surrogate models are useful to overcome current limitations associated with the high computational costs of CFD models,using ML to unveil processes from experimental data or enhance modeling capabilities is deemed a potential research direction to pursue.In this letter,we discuss recent achievements in the realm of ML modeling of wind turbine wakes and operations,along with new promising research strategies.

Performance of the Wind Farm Parameterization Scheme Coupled with the Weather Research and Forecasting Model under Multiple Resolution Regimes for Simulating an Onshore Wind Farm

We use the Wind Farm Parameterization(WFP) scheme coupled with the Weather Research and Forecasting model under multiple resolution regimes to simulate turbulent wake dynamics generated by a real onshore wind farm and their influence at the local meteorological scale. The model outputs are compared with earlier modeling and observation studies. It is found that higher vertical and horizontal resolutions have great impacts on the simulated wake flow dynamics. The corresponding wind speed deficit and turbulent kinetic energy results match well with previous studies. In addition, the effect of horizontal resolution on near-surface meteorology is significantly higher than that of vertical resolution. The wake flow field extends from the start of the wind farm to downstream within 10 km, where the wind speed deficit may exceed 4%. For a height of 150 m or at a distance of about 25 km downstream, the wind speed deficit is around 2%. This indicates that, at a distance of more than 25 km downstream, the impact of the wind turbines can be ignored. Analysis of near-surface meteorology indicates a night and early morning warming near the surface, and increase in near-surface water vapor mixing ratio with decreasing surface sensible and latent heat fluxes. During daytime, a slight cooling near the surface and decrease in the near-surface water vapor mixing ratio with increasing surface sensible and latent heat fluxes is noticed over the wind farm area.

An Observational Study on the Local Climate Effect of the Shangyi Wind Farm in Hebei Province

Zhangjiakou is an important wind power base in Hebei Province,China.The impact of its wind farms on the local climate is controversial.Based on long-term meteorological data from 1981 to 2018,we investigated the effects of the Shangyi Wind Farm(SWF)in Zhangjiakou on air temperature,wind speed,relative humidity,and precipitation using the anomaly or ratio method between the impacted weather station and the non-impacted background weather station.The influence of the SWF on land surface temperature(LST)and evapotranspiration(ET)using MODIS satellite data from 2003 to 2018 was also explored.The results showed that the SWF had an atmospheric warming effect at night especially in summer and autumn(up to 0.95℃).The daytime air temperature changes were marginal,and their signs were varying depending on the season.The annual mean wind speed decreased by 6%,mainly noted in spring and winter(up to 14%).The precipitation and relative humidity were not affected by the SWF.There was no increase in LST in the SWF perhaps due to the increased vegetation coverage unrelated to the wind farms,which canceled out the wind farm-induced land surface warming and also resulted in an increase in ET.The results showed that the impact of wind farms on the local climate was significant,while their impact on the regional climate was slight.

Sub-synchronous frequency domain-equivalent modeling for wind farms based on rotor equivalent resistance characteristics

The equivalent simplification of large wind farms is essential for evaluating the safety of power systems.However,sub-synchronous oscillations can significantly affect the stability of power systems.Although detailed mathematical models of wind farms can help accurately analyze the oscillation mechanism,the solution process is complicated and may lead to problems such as the“dimensional disaster.”Therefore,this paper proposes a sub-synchronous frequency domain-equivalent modeling method for wind farms based on the nature of the equivalent resistance of the rotor,in order to analyze sub-synchronous oscillations accurately.To this end,Matlab/Simulink is used to simulate a detailed model,a single-unit model,and an equivalent model,considering a wind farm as an example.A simulation analysis is then performed under the sub-synchronous frequency to prove that the model is effective and that the wind farm equivalence model method is valid.

Impact of Wind Farms of DFIG Type on Power System Transient Stability

The impact of large-scale grid-connected wind farms of Doubly-fed Induction Generator (DFIG) type on power system transient stability is elaborately discussed in this paper. In accordance with an equivalent generator/converter model, the comprehensive numerical simulations with multiple wind farms of DFIG type involved are carried out to reveal the impact of wind farm on dynamic behavior of existing interconnected power system. Different load models involving nonlinear load model and induction motor model are considered during simulations. Finally, some preliminary conclusions are summarized and discussed.

Multi-ACO Application in Routing and Scheduling Optimization of Maintenance Fleet (RSOMF) Based on Conditions for Offshore Wind Farms

Reducing the operation and maintenance (O & M) cost is one of the potential actions that could reduce the cost of energy produced by offshore wind farms. This article attempts to reduce O & M cost by improving the utilization of the maintenance resources, specifically the efficient scheduling and routing of the maintenance fleet. Scheduling and routing of maintenance fleet is a non-linear optimization problem with high complexity and a number of constraints. A heuristic algorithm, Ant Colony Optimization (ACO), was modified as Multi-ACO to be used to find the optimal scheduling and routing of maintenance fleet. The numerical studies showed that the proposed methodology was effective and robust enough to find the optimal solution even if the number of offshore wind turbine increases. The suggested approaches are helpful to avoid a time-consuming process of manually planning the scheduling and routing with a presumably suboptimal outcome.

基于WindFarmer软件的风电场优化设计

本文介绍了WindFarmer软件的基本功能,提出了利用WindFarmer软件进行风电机组选型和风电场优化设计的方法,介绍某风电场的风电机组选型过程,分析了某风电场的风机布置和优化、经济性评价和比较的全过程。