Wind Farm Model Research Articles

A dynamic equivalent aggregation method of wind turbine systems with a full‐scale power converter for electromagnetic transient simulations

With the increasing exploitation of wind energy, the dynamic performances of large-scale wind farms (WFs) are vital for the system design and stability analysis. However, a detailed WF model considering every wind turbine system (WTS) makes the electromagnetic transient simulation time-consuming and computational-demanding. Therefore, a dynamic equivalent aggregation method is proposed for WTS with a full-scale power converter (WTS-FSPC) in this paper, in order to reduce the computational complexity meanwhile maintaining the same accuracy as the detailed model. To improve the accuracy of the equivalent collector system, the collecting strings are decoupled based on the power flow distribution. Then the WTSs are clustered according to the output AC voltage distributions using a Louvain K-means (LKM) algorithm, which innovatively determines the optimal solution automatically. Additionally, a centralized capacitor is set to represent the distributed capacitance of the collector system. Furthermore, to enhance the model adaptability during asymmetric short-circuit faults, the equivalent method for the zero-sequence collector system is introduced. Time-domain simulation results demonstrate the effectiveness and robustness of the proposed method in reflecting the WF dynamics. The case study in a 2-area system further verifies the superiority of the proposed method for the WF integrated power system stability analysis.

A novel transfer learning approach for wind power prediction based on a serio-parallel deep learning architecture

Although machine learning methods have been widely applied in the wind power prediction field, they are not suitable for building the prediction model of a new-built wind farm because of no sufficient historical data. In this study, a novel deep transfer learning approach is proposed for addressing the few-shot learning problem in multi-step ahead wind power prediction. In the pre-training stage, several convolutional neural networks (CNNs) in parallel are separately connected to the long short-term memory network (LSTM), thus forming a unique serio-parallel CNNs-LSTM (CL) feature extractor. The CL utilizes the CNNs and LSTM to extract both the meteorological and temporal feature information of the neighboring wind farms for facilitating the prediction modeling of the source wind farm. In the transfer-training stage, a transfer strategy is designed to transfer partial network parameters of a well-trained CL feature extractor to construct the prediction model of the target wind farm. In addition, a personalized-training strategy is implemented by using crisscross optimization (CSO) to retrain the parameters of fully-connected layer. The proposed method is validated on a swarm of wind farms located in China and the experimental results show its obvious superiority over the non-transfer models involved in this study.

A low-fidelity dynamic wind farm model for simulating time-varying wind conditions and floating platform motion

This paper introduces a dynamic parametric wind farm model that is capable of simulating floating wind turbine platform motion coupled with wake transport under time-varying wind conditions. The simulator is named FOWFSim-Dyn as it is a dynamic extension of the previously developed steady-state Floating Offshore Wind Farm Simulator (FOWFSim). One-dimensional momentum conservation is used to model dynamic propagation of wake centerline locations and average velocities, while momentum recovery is approximated with the assumption of a constant temporal wake expansion rate. Platform dynamics are captured by treating a floating offshore wind farm as a distribution of particles that are subject to aerodynamic, hydrodynamic, and mooring line forces. The finite difference method is used to discretize the momentum conservation equations to yield a nonlinear state-space model. FOWFSim-Dyn is validated against steady-state experimental wind tunnel data obtained from the literature along with simulation results acquired from FAST. The model is shown to possess a satisfactory level of fidelity for engineering applications. Finally, dynamic simulations are conducted to ensure that time-varying predictions match physical expectations and intuition.

An overall performance index for wind farms: a case study in Norway Arctic region

Wind farms (WFs) experience various challenges that affect their performance. Mostly, designers focus on the technical side of WFs performance, mainly increasing the power production of WFs, through improving their manufacturing and design quality, wind turbines capacity, their availability, reliability, maintainability, and supportability. On the other hand, WFs induce impacts on their surroundings, these impacts can be classified as environmental, social, and economic, and can be described as the sustainability performance of WFs. A comprehensive tool that combines both sides of performance, i.e. the technical and the sustainability performance, is useful to indicate the overall performance of WFs. An overall performance index (OPI) can help operators and stakeholders rate the performance of WFs, more comprehensively and locate the weaknesses in their performance. The performance model for WFs, proposed in this study, arranges a set of technical and sustainability performance indicators in a hierarchical structure. Due to lack of historical data in certain regions where WFs are located, such as the Arctic, expert judgement technique is used to determine the relative weight of each performance indicator. In addition, scoring criteria are predefined qualitatively for each performance indicator. The weighted sum method makes use of the relative weights and the predefined scoring criteria to calculate the OPI of a specific WF. The application of the tool is illustrated by a case study of a WF located in the Norwegian Arctic. Moreover, the Arctic WF is compared to another WF located outside the Arctic to illustrate the effects of Arctic operating conditions on the OPI.

Modelling and Load Test Analysis of a Wind farm for 14 Bus System

This paper describes about the reliable and secure operation of a power system network is an indispensable concern for the system operator. The modelling of wind farm and load test analysis is used to identify the real and reactive power by using DIgSILENT power factory simulation software.By developing the 5MW Wind farm modelling by using Permanent Magnet Synchronous Generator (PMSG).The main aim is to analyze the performance of wind farm modelling with load Test System and also to detect the fault occurrence by using the condition of short circuit studies in the system. In IEEE 14 bus system it is used to identify the wind generator is in overload condition and the wind generator is in lower load condition.

Type-2 neutrosophic number based multi-attributive border approximation area comparison (MABAC) approach for offshore wind farm site selection in USA

The technical, logistical, and ecological challenges associated with offshore wind development necessitate an extensive site selection analysis. Technical parameters such as wind resource, logistical concerns such as distance to shore, and ecological considerations such as fisheries all must be evaluated and weighted, in many cases with incomplete or uncertain data. Making such a critical decision with severe potential economic and ecologic consequences requires a strong decision-making approach to ultimately guide the site selection process. This paper proposes a type-2 neutrosophic number (T2NN) fuzzy based multi-criteria decision-making (MCDM) model for offshore wind farm (OWF) site selection. This approach combines the advantages of neutrosophic numbers sets, which can utilize uncertain and incomplete information, with a multi-attributive border approximation area comparison that provides formulation flexibility and easy calculation. Further, this study develops and integrates a techno-economic model for OWFs in the decision-making. A case study is performed to evaluate and rank five proposed OWF sites off the coast of New Jersey. To validate the proposed model, a comparison against three alternative T2NN fuzzy based models is performed. It is demonstrated that the implemented model yields the same ranking order as the alternative approaches. Sensitivity analysis reveals that changing criteria weightings does not affect the ranking order.

몬테카를로 기반 풍력발전단지 손실 저감을 위한 실시간 시뮬레이션 연구

The expansion of renewable energy in electrical system has become a major issue regarding the impact of energy consumption. In the case of wind power plants, a frequent output variation has been caused due to its environmental dependent characteristics which can lead the power curtailment process in order to match power balance of main power grid. The curtailment process of a large-scale wind farm causes loss not only electrical extraction but also mechanical loads. By considering the curtailment issue, the study for minimizing system loss of power plant is required in terms of operation efficiency. This paper proposes an algorithm to achieve flexible control during real power curtailment process in a wind farm considering wake effect. An algorithm based on the Monte Carlo was derived for finding appropriate weight factors based on electrical loss in wind farm arrays. The weight factors has been composed through realistic wind speed analysis from wake effect. A real wind farm model was established by RSCAD and implemented for case studies. A curtailment signal was imposed to verify the effects with composed real-time simulation.

Evaluating wind farm wakes in large eddy simulations and engineering models

We study wind farm wakes with large eddy simulations (LES) and use these results for the evaluation of engineering models such as the Jensen model, the coupled wake boundary layer model (CWBL), the Turbulence Optimized Park model (TurbOPark), and the wind farm model developed by Niayifar and Porté-Agel (Energies 9, 741 (2016)). We study how well these models capture the wake effects between two aligned wind farms with 72 turbines separated by 10 kilometers in a neutral boundary layer. We find that all considered models over-predict the wind farm wake recovery compared to what is observed in LES. The TurbOPark model predictions on the wind farm wake effect are closest to the LES results for the scenario considered here.

Modeling of Ethiopian Wind Power Production Using ERA5 Reanalysis Data

Ethiopia has huge wind energy potential. In order to be able to simulate the power system operation, hourly time series of wind power is needed. These can be obtained from ERA5 data but first a realistic model is needed. Therefore, in this paper ERA5 reanalysis data were used to model wind power production at two topographically different and distant regions of Ethiopian wind farms—Adama II and Ashegoda. Wind speed was extracted from the ERA5 nearest grid point, bi-linearly interpolated to farms location and statistically down-scaled to increase its resolution at the site. Finally, the speed is extrapolated to hub-height of turbine and converted to power through farm specific power curve to compare with actual data for validation. The results from the model and historical data of wind farms are compared using performance error metrics like hourly mean absolute error (MAE) and hourly root mean square error (RMSE). When comparing with data from Ethiopian Electric Power (EEP), we found hourly MAE and RMSE of 2.5% and 4.54% for Adama II and 2.32% and 5.29% for Ashegoda wind farms respectively, demonstrating a good correlation between the measured and our simulation model result. Thus, this model can be extended to other parts of the country to forecast future wind power production, as well as to indicate simulation of wind power production potential for planning and policy applications using ERA5 reanalysis data. To the best of our knowledge, such modeling of wind power production using reanalysis data has not yet been tried and no researcher has validated generation output against measurement in the country.

A Methodology for Estimating the Position of the Engineering Bedrock for Offshore Wind Farm Seismic Demand in Taiwan

Taiwan lies in the circum-Pacific earthquake zone. The seabed soil of offshore wind farms in Taiwan is mainly composed of loose silty sand and soft, low-plasticity clay. The seismic demand for offshore wind turbines has been given by the local code. Ground-motion analysis is required to consider the site effects of the soil liquefaction potential evaluation and the foundation design of offshore wind turbines. However, the depth of the engineering bedrock for ground motion analysis is not presented in the local code. In this study, we develop a three-dimensional ground model of an offshore wind farm in the Changhua area, through use of collected in situ borehole and PS (P wave (compression) and S (shear) wave velocities) logging test data. The engineering bedrock is the sediment at the depth where the average shear wave velocity of soil within 30 m, Vsd30, is larger than 360 m/s. In this ground model, the shear wave velocity of each type of soil is quantified using the seismic empirical formulation developed in this study. The results indicate that the engineering bedrock lies at least 49.5–83 m beneath the seabed at the Changhua offshore wind farm. Based on these findings, it is recommended that drilling more than 100 m below the seabed be done to obtain shear wave velocity data for a ground response analysis of the seismic force assessment of offshore wind farm foundation designs.

A hybrid risk analysis model for wind farms using Coloured Petri Nets and interpretive structural modelling

This research presents an integrated approach combining Coloured Petri Nets and Interpretive Structural Modelling, called hybrid ISM-CPN model, to risk assessment of wind farms development. The dynamic nature of the component elements of wind farms and considering the risks interdependencies motivate this combination. A questionnaire survey targeting experts is conducted for calculating the modified Risk Priority Numbers (RPNs). Thirty-four factors are ranked, and the nine critical risks identified as “Political instability”, “Sanctions”, “Economic insecurity”, “Interest rate fluctuations”, “Exchange rate fluctuations”, “Inflation rate fluctuations”, “Feasibility risk”, “Shortage of capital risk”, and “Supplier risk”. The values of RPNT = 260.26, RPNC = 251.31, and RPNQ = 238.77 indicate that “Exchange rate fluctuations” is the most important critical risk. The second one, is “Political instability” with RPNT = 255.35, RPNC = 247.28, and RPNQ = 230.56. The simulation results of a 50 MW wind farm reveal, with a 90% confidence level, “Sanctions” would cause 43.9% increase in project execution time and 28% decrease in project quality, and “Shortage of capital risk” has the greatest impact on project cost, with a 25.79% increase. This work further proposes several strategies to respond to the CRs and concludes that investments in REs can support post-COVID-19 economic recovery.

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.

Switching transients caused by vacuum circuit breakers in collection grids of offshore wind farms

AbstractIn this study, transients that can occur in the collection grids of offshore wind farms were investigated. On the basis of the multiple prestrike and reignition model that can reflect the actual operation of a vacuum circuit breaker (VCB), a switching transient simulation model of a typical offshore wind farm was built. The developed model included a VCB, a transformer, a cable, and an arrester. Issues concerning the closing time, length of the feeder, and topology of the collection grid were discussed. Results showed that the relationship between overvoltage amplitude and closing time was approximately sinusoidal and the maximum value was obtained when the closing time was near the peak of the power source. In addition, the overvoltage at the end of a feeder terminal was the largest among all of the transformer overvoltages of the same feeder. The overvoltage of the main transformer and that of the transformer located at the end of a feeder decreased slowly with the increase in feeder length during the closing of VCB. However, these overvoltages were positively related to feeder length during the opening of VCB. The star topology was superior to the other topologies in terms of coping with the overvoltage caused by switching transients. The effectiveness of a suppression measure, namely, installation of a resistance‐capacitor filter, was also verified.

Wind farm control ‐ Part I: A review on control system concepts and structures

Wind farm control design is a recently new area of research that has rapidly become a key enabler for the development of large wind farm projects and their safe and efficient connection to the power grid. A comprehensive review of the intense research conducted in this area over the last 10 years is presented. Part I reviews control system concepts and structures and classifies them depending on their main objective (i.e. to maximise power production or to provide grid services. The work and key findings in each paper are discussed in detail with particular emphasis on the turbine side. Additionally, the review contributes to the existing reviews on the area by providing an elegant classification between model testing and control approaches. Areas where significant work is still needed are also discussed. In Part II, a thorough review on aerodynamic wind farm models for control design purposes is provided.

Accurate long-term power generation model for offshore wind farms in Europe using ERA5 reanalysis

Accurate long-term wind speed data is important for understanding the role of offshore wind farms in future energy systems. Meteorological reanalyses, such as ERA5, are relied upon by the wind energy industry and researchers. Being unaffected by onshore topography and surface roughness, the historic generation of offshore wind farms can be accurately predicted using such weather reanalysis.In this work we present a new method for using ERA5 weather data to model long term (>40 year) hourly wind generation for individual offshore wind farms. The model is validated against 57 offshore wind farms in Europe, and reduces the root mean squared error in hourly and daily capacity factor predictions by 10% and 18% respectively when compared to the Renewables Ninja. Further, 40 years (from 1980 to 2019) of ERA5 hourly wind speeds within 200 km of the coast is made easily available for energy system research on our accompanying website (windtlas.xyz).

Implementation of realistic wind farm layout optimization using artificial bee colony algorithm

ABSTRACT This paper presents the realistic optimization model for wind farm (WF) design. The investigated WF is located in the southern region of Turkey and consists of 10 WTs. In this modeling process, the real wind measurement data, the experimental power curve of WT, variable thrust coefficient, and the real WT characteristic features are taken into account. Also, the wake effect calculations are realized considering the wind direction, topographic effect, and roughness. Wind Atlas Analysis and Application Program (WAsP) is used in calculating WF annual energy production (AEP) and verifies the results of the WF optimization model. ABC algorithm is used in the solution of the developed model. Optimal WT coordinates are determined. The real AEP value of the existing WF is 51.570 GWh. In the WAsP software, the AEP value of the existing WF is found to be 50.665 GWh. With the solution of the developed model, the AEP value of WF is increased to 53.454 GWh. The WTs are positioned on determined coordinates in the WAsP software. The AEP value is found to be 53.542 GWh. As a result, the AEP of WF is increased by 5.68%. The AEP of WTs has been increased except for one WT.

Small-signal modeling of wind farm with direct-drive PMSG using the component connection method

In order to analysis the small signal stability of the direct-drive permanent magnetic synchronous generator (PMSG) based wind farm, this paper makes a small signal model of the windfarm including a detailed model of PMSG adopting a component connection method (CCM). In the process, the system, consist of wind turbine units with PMSG, collection cable, feeder, transmission cable and grid side, is divided into several subsystems. The interconnection between different subsystems is represented by a linear algebra matrix. Then, through integrating the model of each block and the interconnection matrix, the state space matrix of wind farm can be easier to be built. Based on this model, eigenvalue trajectory is applied to analysis control parameters of the wind turbines to achieve better dynamic performance of the system. A simulation model based on MATLAB/SIMULINK of a two by two wind turbines (WT) wind farm is presented as a test system to validate the effectiveness of CCM.

Benefits of Energy Storage Systems for Small-Scale Wind Farm Development in Latvia

Abstract The paper covers the main aspects and restrictions on siting small-scale wind farms in Latvia and benefits of using energy storage systems with small-scale wind farms. The restrictions of siting have been analysed. Grid connection restrictions are addressed as the main issues for small-scale wind farm development in Latvia. Two small-scale wind farm models with similar properties have been made and analysed within the framework of the research. The paper proposes the idea for maximising the production of small-scale wind farm in a small area site with high wind potential.

A novel protection method for a wind farm collector line based on FCM clustering analysis

To solve the problem of incorrect collector line protection in large-scale wind farms, we propose a protection method based on fuzzy c-means (FCM) clustering analysis. The intrinsic relationship between the fault current and the voltage sag of a doubly-fed induction generator (DFIG) was analysed under fault conditions. According to the component characteristics contained in the DFIG fault current, the DC, AC power frequency, and rotation frequency under different operation conditions were extracted to form the historical data sample set. The historical feature samples were divided into either the non-fault or fault category by the FCM algorithm in the multi-dimensional fault space, thereby obtaining the clustering centres. The distance similarity criterion was used to determine the attributes of the real-time feature sample to evaluate its protection. A centralized large-scale wind farm model was established in DIgSILENT to simulate various fault and operation conditions of the collector line. The simulation results showed that the proposed protection method could trip the fault collector line accurately by considering DFIG feeding current under different fault conditions. The proposed protection method improved the relay adaptability under variable wind farm operation conditions.

Equivalent Modelling of Wind Farms for Probabilistic Harmonic Propagation Studies

This paper investigates the effect of modelling of different aspects of wind farms (WF) as a first step towards development of the equivalent harmonic model initially of WF, and later of other types of converter connected generation, for studies of harmonic propagation and mitigation in power electronics (PE) rich transmission networks. The aim of the study was to establish a set of scaling factors that can be used when modelling the entire WF as a single probabilistic harmonic generator in harmonic propagation studies, considering different WF configurations, weather conditions, harmonic cancellation effects, individual wind generator availability, cable disconnection, and the influence of the non-ideal external grid. The importance of modelling resonance and individual characteristic harmonics on the equivalent modelling of WF has also been established. The results facilitate the assessment of global contribution of PE connected RES to harmonic levels in transmission networks and more generally harmonic propagation studies in large PE rich transmission networks.