Equivalent Model Of Wind Farm Research Articles

Genetic Clustering-Based Equivalent Model of Wind Farm with Doubly Fed Induction Generator

Genetic Clustering-Based Equivalent Model of Wind Farm with Doubly Fed Induction Generator

Power quality assessment in different wind power plant models considering wind turbine wake effects

Abstract The intense increase in the installed capacity of wind farms has required a computationally efficient dynamic equivalent model of wind farms. Various types of wind-farm modelling aim to identify the accuracy and simulation time in the presence of the power system. In this study, dynamic simulation of equivalent models of a sample wind farm, including single-turbine representation, multiple-turbine representation, quasi-multiple-turbine representation and full-turbine representation models, are performed using a doubly-fed induction generator wind turbine model developed in DIgSILENT software. The developed doubly-fed induction generator model in DIgSILENT is intended to simulate inflow wind turbulence for more accurate performance. The wake effects between wind turbines for the full-turbine representation and multiple-turbine representation models have been considered using the Jensen method. The developed model improves the extraction power of the turbine according to the layout of the wind farm. The accuracy of the mentioned methods is evaluated by calculating the output parameters of the wind farm, including active and reactive powers, voltage and instantaneous flicker intensity. The study was carried out on a sample wind farm, which included 39 wind turbines. The simulation results confirm that the computational loads of the single-turbine representation (STR), the multiple-turbine representation and the quasi-multiple-turbine representation are 1/39, 1/8 and 1/8 times the full-turbine representation model, respectively. On the other hand, the error of active power (voltage) with respect to the full-turbine representation model is 74.59% (1.31%), 43.29% (0.31%) and 7.19% (0.11%) for the STR, the multiple-turbine representation and the quasi-multiple representation, respectively.

Quantitative evaluation method for error of wind farm equivalent model based on multiscale entropy-greedy Gaussian segmentation

Quantitative evaluation method for error of wind farm equivalent model based on multiscale entropy-greedy Gaussian segmentation

Research on simplified modeling of large-scale wind farms based on equivalent transfer function and aggregate equivalent

Introduction: In grid-connected research of large-scale wind farms, the problem of long simulation time exists in the research based on detailed model. In order to realize the fast simulation and grid-connected transient research of wind farm, this paper presents a simplified equivalent model of wind farm based on equivalent transfer function and improved capacity weighting method.Methods: This paper analyzes the mathematical model of wind turbine, corresponding to the strategy of design simplification, and the simplified model is built according to the equivalent transfer function. In addition, this paper considers the equivalent line loss model of collector line, classifies and aggregates the simplified wind turbines, and calculates the equivalent value of the converged wind turbines by using the improved capacity weighting method, the grid-connected simplified modeling of large-scale wind farm is realized.Results: A model of wind farm-MMC-HVDC flexible grid-connected wind power system is built. The simulation results show that the simplified equivalent model improves the simulation speed greatly while guaranteeing the dynamic characteristics.Discussion: The simplified equivalent strategy of wind farm can keep the same dynamic performance with the detailed model, and improve the simulation speed, and realize the fast simulation and grid-connected transient research of wind farm.

An Equivalent Model of Wind Farm Based on Multivariate Multi-Scale Entropy and Multi-View Clustering

Wind farm (WF) equivalence is an effective method to achieve accurate and efficient simulation of large-scale WF. Existing equivalent models are generally suitable for one certain or very few scenarios, and have difficulty reflecting the multiple aspects of dynamic processes of WF. Aiming at these problems, this paper proposes an equivalent model of WF based on multivariate multi-scale entropy (MMSE) and multi-view clustering. Firstly, the influence of the factors on the dynamic process of the wind turbine (WT) is discussed, including control mode, wind speed and its wake effect, resistance of crowbar resistor and so on. The relationship between these factors and the dynamic equivalence of WF is analyzed. Secondly, an overview of MMSE is given, and the applicability of MMSE on WF equivalence is analyzed. On this basis, this paper proposes the extraction process of a WT clustering indicator using MMSE. Then, the multi-view fuzzy C means (MV-FCM) algorithm is used for the clustering of WTs, and the equivalent model of WF is obtained after calculating the equivalent parameters. Finally, the IEEE14 power system including WF is simulated. The results show that the equivalent model could be applied to dynamic process simulation in various fault scenarios of power systems, and the error is small when the cluster number is 4. Compared with the detailed model, the simulation time of the WF equivalent model proposed in this paper is shortened by 86%, and the simulation accuracy is improved by about 44% compared with the comparative model.

Development of wind-energy modeling technology and standards

To ensure the stable operation of power systems with large proportions of wind power, China has published a series of national, industry, and enterprise standards for wind power. The increase in the number of standards and the expansion of their application scope have given rise to a situation where multiple standards overlap and conflict with regard to the establishment of models and their applicability, resulting in unclear standard application scenarios. Therefore, it is imperative to analyze the development of wind-turbine and wind-farm modeling, along with the relevant standards. This paper presents the methods for wind-turbine modeling, the equivalent model of wind farms based on the general model of wind turbines, and the technical provisions and application scenarios involved in the relevant domestic and international standards. The adaptability of the relevant standards is examined. The results of this study are helpful for advancing wind power generation in China and ensuring the safe and stable operation of large-scale wind power systems.

Equivalent Modeling of a Direct-Drive Wind Farm Based on the Measured Data

In view of the difference between the wind speed and the actual wind speed in the example, the wind farm with fifteen direct-drive permanent magnet synchronous motors draws the recorded wind speed data on the coordinates of time and wind speed by linear regression to group the change trend. Secondly, the detailed model of wind farm is built in PSCAD, and the multi-machine equivalent model of wind farm is built by the control current source replacing the permanent magnet synchronous motor and the control part. Finally, the simulation analysis shows that the equivalent model reduces the simulation time and accurately reflects the dynamic and transient properties of the wind farm.

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 novel wind farm equivalent model for high voltage ride through analysis based on multi-view incremental transfer clustering

The voltage swell (VS) in power system may require high voltage ride through (HVRT) of wind farms (WFs), and the detailed WF simulation model would need huge computational time, and thus is not be suitable for the analysis of HVRT dynamic behaviour of large-scale WFs. In this paper, a novel WF equivalent model with the required accuracy level for HVRT analysis is proposed. Firstly, multiscale entropies (MSEs) of the operational parameters in wind turbines (WTs) are calculated to represent their distinguishability in different HVRT processes, and the time series of several parameters which have obvious distinguishability are selected as the multi-view clustering indicators (CIs). To handle the multi-view CIs, a new clustering algorithm namely multi-view incremental transfer fuzzy C means (MVIT-FCM) is proposed. This algorithm integrates the transfer learning technique to increase the stability and accuracy of the WTs clustering. Also, the high-dimensionality of the time series based CIs and the consequent computational burden in clustering are considered, and an incremental technique is applied in MVIT-FCM to handle the large-scale WF modelling. A real WF system is used for case study. The results indicate that the multi-view CIs are very effective for increasing the equivalent accuracies. In addition, with the aid of incremental and transfer learning techniques, MVIT-FCM can acquire stable clustering results and handle large-scale WF accurately and efficiently.

Equivalent Modeling of Dfig-Based Wind Farms for Sub-Synchronous Resonance Analysis

The order of the detailed model of doubly-fed induction generator (DFIG) wind farms are too high and the simulation is difficult. Most of the existing research has used a single-machine equivalent model and clustering aggregation model for equivalence and few papers have explored the principles and equivalent conditions of the single-machine equivalent model under sub-synchronous resonance (SSR). Due to this reason, this paper equates DFIG wind farms connected with series compensated transmission network to two separate units based on the principle of matrix similarity transformation and the mathematical model and physical model of each unit has been studied. Then, the DFIG wind farm equivalent model’s validity is analyzed in number difference and collecting line difference based on linearization analysis. Finally, the system model is built in EMTDC/PSCAD, the damping analysis method is used to test the equivalent model’s validity and further reveal the mechanism of the system’s unstable operation. The results show that: the equivalent model can effectively reduce the system dimension and accurately reflect the dominant oscillation characteristics of DFIG wind farm under SSR; when SSR occurs, the damping coefficient of DFIG wind farm is negative under the oscillation frequency.

Development of an Equivalent Wind Farm Model for Frequency Regulation

The operational security of power systems for wind farm (WF) integration becomes more important as the penetration of wind generation increases. A WF may contain hundreds of wind turbines (WT) and thus the power system simulations based on the complex model of a WT consume much time. This article proposes an equivalent WF model with the frequency-regulation function. The main research works include the establishment of the generic model of a WT, the development of single- and multimachine equivalent models for a large WF, the clustering of WTs based on the developed nonlinear wake model and the fuzzy C-means (FCM) clustering algorithm, the integration of the frequency-regulation function into the equivalent WF model, and the validation of the developed equivalent WF model by various operating scenarios. The results demonstrate that the proposed multimachine equivalent method based on the FCM clustering provides a more accurate model than a single-WT equivalent model. Consequently, the research results provide a solution for simulating a large-scale wind power integration, providing efficient, accurate, and fast steady-state and dynamic analyses.

Dynamic Equivalent Modeling for Wind Farms With DFIGs Using the Artificial Bee Colony With K-Means Algorithm

With the increasing penetration of wind power, it is recognized that wind power will have a greater and greater impact on the planning and operation of the original power system. And the detailed modeling of wind farm with doubly-fed induction wind generator (DFIG) will require large storage and computation resources, which poses technical challenges for equivalent modeling of wind farm. In this paper, a multi-machine dynamic equivalent modeling method for wind farms with DFIGs is proposed. First, the artificial bee colony with k-means (ABC-KM) algorithm is proposed to improve the effectiveness of wind farm clustering. Second, the operating data composed of wind speed, pitch angle, rotor angular velocity, rotor current, real-time active and reactive power are selected as clustering indicators. A wind farm with DFIGs is divided into several groups and DFIGs in the same group are clustered as one DFIG through equivalent parameter aggregation. The proposed wind farm modeling method consisting of clustering method and clustering indicators is verified by comparing the simulation results of equivalent and detailed models at steady-state and dynamic-state cases.

A Wind Farm Equivalent Method Based on Multi-View Transfer Clustering and Stack Sparse Auto Encoder

Large-scale wind farm (WF) constitutes dozens or even hundreds of wind turbines (WTs), making it complex and even impractical to develop each individual WT in detail when building WF model. Thus, the equivalent model of WF, with a reasonable reduction of the detailed model, is essential to be developed. In this paper, we propose a multi-view transfer clustering and stack sparse auto encoder (SSAE) based WF equivalent method, which can be used in the low voltage ride through (LVRT) analysis of WF. First, to obtain distinguishable deep-level and multi-view representation of wind turbine (WT), stack sparse auto encoder (SSAE) is used to extract features from the time series of several WT physical quantities, and these features are used as the clustering indicator (CI). Then, a multi-view transfer FCM (MVT-FCM) clustering algorithm, which combines transfer learning with multi-view FCM (MV-FCM), is put forward for WTs clustering. Two transfer rules are designed in this algorithm, and the clustering center and membership degree in the source domain are transferred to guide the clustering process of target domain samples. Finally, the calculation method of equivalent parameters is presented. To verify the effectiveness of the proposed method, a modified actual system in East Inner Mongolia of China is utilized for case study, and the performance of the proposed model is compared with several state-of-the-art models. Simulation results show that the equivalent errors of the proposed model decrease at least 3% when comparing with other models. Also, the error fluctuations are within 6% under different simulation conditions, which illustrates the well-performed robustness of the proposed model.

Key Issues on the Design of an Offshore Wind Farm Layout and Its Equivalent Model

Offshore wind farms will have larger capacities in the future than they do today. Thus, the costs that are associated with the installation of wind turbines and the connection of power grids will be much higher, thus the location of wind turbines and the design of internal cable connections will be even more important. A large wind farm comprises of hundreds of wind turbines. Modeling each using a complex model leads to long simulation times—especially in transient response analyses. Therefore, in the future, simulations of power systems with a high wind power penetration must apply the equivalent wind-farm model to reduce the burden of calculation. This investigation examines significant issues around the optimal design of a modern offshore wind farm layout and its equivalent model. According to a review of the literature, the wake effect and its modeling, layout optimization technologies, cable connection design, and wind farm reliability, are significant issues in offshore wind farm design. This investigation will summarize these important issues and present a list of factors that strongly influence the design of an offshore wind farm.

A Dynamic Equivalent Model for DFIG-Based Wind Farms

With the increasing scale of wind farms, the fault characteristics tend to be complex, which poses a technical challenge to establish the dynamic equivalent model of wind farms. In this paper, a dynamic equivalent method of DFIG-based wind farm based on the density peak clustering algorithm (DPCA) is presented. First, under an analysis of short-circuit current (SCC) in single doubly fed induction generator (DFIG), the clustering indexes are selected. Second, with the selected clustering indexes and DPCA, a more refined two-stage clustering of DFIGs in wind farm is carried out. Third, the units in the same cluster are equivalent to one unit, and then the dynamic equivalent model of the DFIG-based wind farm is established. Finally, the proposed method is validated through the MATLAB/SIMULINK-based simulation results, and the comparison results also show that the dynamic equivalent model proposed in this paper has a better performance than two other equivalent models. Moreover, another comparison between DPCA and K-means clustering algorithm is analyzed, and the result shows that DPCA has a better performance which provides a better choice for dynamic equivalence of wind farms.

An equivalent modeling method for Offshore Wind Farms based on fault characteristics analysis

A large offshore wind farm usually consists of dozens or even hundreds of wind turbines. Due to the limitation of the simulation scale, it is necessary to develop an equivalent model of offshore wind farms for power system studies. At present, the aggregation method is widely adopted for wind farm equivalent modeling. In this paper, the topology, electrical parameters, operating conditions and individual turbine characteristics of the offshore wind farms are taken into consideration. Firstly, the output power distribution of offshore wind farm, the voltage distribution of the collector system and the fault ride-through characteristics of wind turbines are analyzed. Then, a dynamic equivalent modeling method for offshore wind farms is developed based on the fault characteristics analysis. Finally, the proposed method is validated through time-domain simulation.

Research on the Impact on Sub-synchronous Resonance Risk from DFIG Number and Series-compensated Degree

Sub-synchronous resonance (SSR) occurred in the power system with both Doubly Fed Induction Generators (DFIGs) and series-compensated lines. Existing paper discovered the nonlinear relationship of SSR damping and the number of DFIGs. In this paper, the equivalent model of wind farms and series-compensated power system in Guyuan is built in RT-LAB. Based on that model, the SSR characteristics of wind farms in Guyuan area under different DFIG numbers and line parameters are investigated through impedance method. The results show that: 1) The relationship between SSR characteristics and DFIG number is nonlinear in Guyuan area. Under the condition of a given wind speed, there is certain number of DFIGs which will induce worst damping effect. 2) A higher series-compensated degree results in a less stable system.

A Practical Equivalent Method for DFIG Wind Farms

With increasing penetration of wind power, it is recognized that manufacturer-specific models of wind turbines are not appropriate for power system studies, and the equivalent model of wind farms required by dynamic modeling poses a complicated technical challenge. In this paper, a practical equivalent method for wind farms with doubly fed induction generator (DFIG) wind turbines is proposed. Three critical steps are involved in this method. Step 1, modeling of a wind turbine, the model incorporating the complete fault ride through process is constructed and validated for an operational DFIG wind turbine. Step 2, clustering of wind turbines, a practical four-machine equivalent method is proposed. Initially, dynamic responses of active power of a DFIG wind turbine operating at various wind speeds are investigated to obtain three clusters of wind turbines, then four clusters are further determined by improving equivalency error at some typical wind speed scenarios. Step 3, an analytical method is proposed to determine the equivalent collector network parameters for each of the four equivalent machines. The proposed method is validated by comparing with the single-machine representation and the detailed wind farm model. Simulation results show that the proposed method has good performance for different fault durations, voltage dips, and changing wind scenarios.

Review of Recent Development of Dynamic Wind Farm Equivalent Models Based on Big Data Mining

Recently, the big data mining method has been applied in dynamic wind farm equivalent modeling. In this paper, its recent development with present research both domestic and overseas is reviewed. Firstly, the studies of wind speed prediction, equivalence and its distribution in the wind farm are concluded. Secondly, two typical approaches used in the big data mining method is introduced, respectively. For single wind turbine equivalent modeling, it focuses on how to choose and identify equivalent parameters. For multiple wind turbine equivalent modeling, the following three aspects are concentrated, i.e. aggregation of different wind turbine clusters, the parameters in the same cluster, and equivalence of collector system. Thirdly, an outlook on the development of dynamic wind farm equivalent models in the future is discussed.

Study of Time and Meteorological Characteristics of Wind Speed Correlation in Flat Terrains Based on Operation Data

The accurate calculation and characteristic analysis of wind speed correlation (WSC) is the basis of wind farm equivalent modeling, wind power prediction and other advanced applications. It is well known that the accurate calculation of WSC depends on the quality of the raw data, and the WSC of wind turbines is related to spatial, time and meteorological conditions. However, the researches on the statistical analysis of time/meteorological WSC characteristics and the original data quality improvement for WSC calculation are rarely carried out. This paper reviews and redefines the concept and connotation of spatial, time and meteorological WSC. On this basis, a general process is proposed for WSC calculation including data classification, extraction and cleaning. Then the WSC characteristics between wind turbines are analyzed from time and meteorological dimensions based on the actual operation data. In addition, the influence of time WSC and meteorological WSC on wind turbine equivalent modeling and wind power prediction was discussed. The results of case study shows that the proposed general WSC calculation process is feasible and effective; the WSC for different time scales, wind speed ranges and wind directions varies greatly; the spatial WSC cannot characterize the time variability and directionality of the WSC. And the time and meteorological WSC characteristics are of great engineering value to improve the wind turbine equivalent modeling and wind power prediction accuracy, the influence of time scale and meteorological conditions should be considered in the applications of WSC.