Typical Wind Farm Research Articles

A novel improved actuator line method for tandem-arranged wind turbines wake analysis and its application

For a typical wind farm layout, the disturbance caused by wind turbine wakes typically exerts a significant negative impact on power generation. However, due to the insufficient reliability of existing multiple turbines wake simulations, the improved effects of wind farm layout modifications cannot be fully verified. In this paper, we employ a high-fidelity model, the Improved Actuator Line Model (I-ALM), combined with Large Eddy Simulation (LES) based on the OpenFOAM solver, to investigate the active yaw strategy of wind farms. The improvements in the ALM primarily include the Gaussian anisotropic force projection method, the integral velocity sampling method, and the enhanced nacelle-tower projection method. Firstly, we conduct numerical simulations of the NTNU "Blind Test" experimental cases using the combination of the I-ALM method and LES, mainly focusing on aerodynamic forces and wake development characteristics. Then, the proposed method is applied to the wake prediction of three NREL 5 MW tandem-arranged yaw wind turbines after benchmarking. The power generation and wake characteristics under different yaw angles is comprehensively predicted, leading to the development of a preliminary active yaw strategy (including rated state and maximum TSR state). This strategy holds significant guiding value for the accurate and efficient simulation of wind farms.

Geospatial analysis of scour development in offshore wind farms

Scour is one of the most fatal hazards to the foundations of offshore wind turbines. Most of the current research focuses on the formation process and mechanism of scour hole, while there is a lacking of real data monitoring of scour development and real data analysis using data mining techniques and analytical tools for making suitable strategic decisions. In this research, scour phenomenon on a typical offshore wind farm with 72 turbines built on a monopile foundation was inspected consecutively for four times over the course of a year. The geospatial properties of scour, including maximum scour depth, maximum scour extension, scouring and siltation volume were comprehensively analyzed, and the possible autocorrelation between scour data and turbine location was determined using a data-driven decision-making process. The results indicate that the characteristics of the studied scour generally exhibit an upward trend over time, and reflecting strong spatial dependence. Based on local geospatial dependency analysis of maximum scour depth, a High-High gathering area was identified which provides useful clues for making efficient maintenance, protection and planning strategies on specific offshore wind foundations. Formula between average maximum scour depth and inspection time is formulated, which can be used to predict scour development of this offshore wind farm.

Mid-to-long Term Wind Power Forecasting Based on ARIMA-BP Combined Model

Background: Increasing the accuracy of the output power forecasting for wind power is helpful to the improvement of the reliability of power dispatching. Objective: This study aimed to improve the forecasting accuracy of mid-to-long term wind power. Methods: A mid-to-long term wind power forecasting based on ARIMA-BP combined model was proposed. The Empirical Mode Decomposition (EMD) was used to decompose the historical wind power series and obtain the Intrinsic Mode Function (IMF) and residual components, thereby obtaining more regular components. Then, the optimum feature set was obtained based on the minimum Redundancy Maximum Relevance (mRAR) to improve the prediction accuracy for feature extraction. After that, the high-frequency components were predicted using the Back Propagation (BP) neural network model, while the low-frequency components were predicted using the Autoregressive Integrated Moving Average model (ARIMA). Finally, the predicted components obtained were superimposed to deduce the final mid- and long-term wind power prediction results. Results: An analysis was conducted according to the actual data from a typical wind farm. After comparison, it was found that, after empirical mode decomposition and feature extraction analysis, the error of the intelligent combination algorithm based on the ARIMA-BP combined model was smaller than that using only the BP neural network or only the ARIMA. Conclusion: By means of actual data analysis, the effectiveness of the method proposed by the study for mid- and long-term wind power prediction was verified.

Sensibility analysis in the study of economic viability of a wind farm in the Canary Island. A case of study: 10 MW wind farm in Roque Prieto, Gáldar, Gran Canaria.

In this article, the economic viability of a typical wind farm of the Canary Islands is analyzed. The sensibility of the investment is analyzed as opposed to a series of factors which can change. Likewise, it is proved that exist technical and economic determinants that restrict the full development of the wind potential of a certain area, especially in the islands or places of complicated or slightly communicated geography.

Reinforcement Learning-Based Wind Farm Control: Toward Large Farm Applications via Automatic Grouping and Transfer Learning

The high system complexity and strong wake effects bring significant challenges to wind farm operations. Conventional wind farm control methods may lead to degraded power generation efficiency. A reinforcement learning (RL)-based approach is proposed in this paper to handle these issues, which can increase the long-term farm-level power generation subject to strong wake effects while without requiring analytical wind farm models. The proposed method is significantly distinct from existing RL-based wind farm control approaches, whose computational complexities usually increase heavily with the increase of total turbine numbers. In contrast, our method can greatly reduce training loads and enhance learning efficiency via two novel designs: (1) automatic grouping and (2) multi-agent-based transfer learning (MATL). Automatic Grouping can divide a large wind farm into small turbine groups by analyzing the aerodynamic interactions between turbines and utilizing some key principles from the graph theory. It enables the separated conduction of RL algorithms on small turbine groups, avoiding the complex training process and high computational costs of applying RL on the entire farm. Based on Automatic Grouping, MATL can further reduce the computational complexity by allowing agents (i.e. wind turbines) to inherit control policies under potential group changes. Case studies with a dynamical simulator show that the proposed method achieves clear power generation increases than the benchmark. It also dramatically reduces computational costs compared with typical RL-based wind farm control methods, paving the way for the application of RL in general wind farms.

Two-stage AGC Control Method for Wind Farm Considering Power Forecast and Theoretical Power Error

With the large-scale wind farm connected to the power system, the uncertainty and fluctuation of its output bring challenges to the power grid dispatching and operation, especially in the automatic generation control. The problem of inaccurate theoretical power upper limit and power forecast has always been prominent. Therefore, this paper proposes a two-stage AGC control method for wind farms. In the first stage, the power control target value of the wind turbine group is determined by comprehensively considering the grid connection point of the station, the power consumption of the station, the loss in the station, and the theoretical power error. In the second stage, the power control of the wind turbine group is divided according to the proportion of the maximum power generation capacity and sent to the wind turbine, to realize the closed-loop control. Based on the measured data of a typical wind farm in Jiangsu province, the calculation results show that the control strategy proposed in this paper is effective and can greatly improve the response speed and accuracy of wind farm AGC.

Life cycle carbon footprint accounting of an offshore wind farm in Southeast China—Simplified models and carbon benchmarks for typhoons

Offshore wind farms are fast-spreading off the southeast coast of China to achieve carbon neutrality by 2060. However, unfavorable weather conditions in Southeast China (e.g. frequent typhoons and severe wind intermittency) may increase the carbon footprint of offshore wind energy. But typhoons have never been considered in carbon accounting before, which may affect the design decisions of stakeholders and research priority setting for academicians. (i) To determine the effect of typhoons on carbon footprint, a life cycle assessment was performed on a typical offshore wind farm using real data. The results demonstrate that typhoons can be the largest contributor to carbon footprint, and the maximum wind speed of typhoons is the most critical parameter to carbon footprint. (ii) To quantify the carbon emissions caused by typhoons, simplified models estimating carbon emission intensity under different maximum wind speeds of typhoons are established, using Monte Carlo method based on 51 historical typhoons. The models imply that wind turbines with Permanent Magnet Synchronous Generator (PMSG) are highly recommended in typhoon-prone area, because their carbon footprint is smaller than those with Doubly-Fed Induction Generator (DFIG). (iii) To set carbon benchmarks for typhoons, statistic analysis was conducted based on the simulated samples generated by Monte Carlo method. The carbon benchmarks of typhoons show that gearbox has the highest carbon emission intensity (mean=4.35g/kW, 95th percentile=7.00g/kW) among all components, resulting DFIG with gearbox not suitable in typhoon-areas. The site selection of offshore wind farms in Southeast China should avoid areas where the maximum wind speed of typhoons exceeds 44.5 m/s. This study emphasizes the importance of typhoons in carbon footprint accounting for offshore wind farms in Southeast China. More significantly, the proposed simplified models and carbon benchmarks of typhoons can be applied to feasibility studies in early design stage for engineers and stakeholders.

Mitigating the Negative Impact of Wind Power on Soaring Birds through Government Restrictions

Wind power is widely used and creates value worldwide. However, it also poses a threat to the survival of soaring birds. This study focuses on the role of government restrictions in mitigating the adverse effects of wind power on soaring birds. We used an overall research method to identify research questions, selected typical wind farms from different provinces and topographic landscapes in China for descriptive analysis, and supported by data from environmental impact reports, government gazettes, and walk-through surveys, analyzed and concluded that government restrictions can be used as a means of weakening the impacts of wind power generation on soaring birds. And our findings suggest that site control for wind farms that have not yet been established, restrictions on the timing of power generation for wind farms in operation, and ecological restoration of wind farms that have caused environmental damage are effective implementation options for government restrictions. Additionally, the policy strengths of the above government restrictions are guided, recommended, and mandatory, respectively. Government restrictions can serve as an effective means of mitigating the negative impact of wind power on soaring birds, generating economic value while maximizing the protection of soaring birds’ subsistence. Based on our findings, we call on governments to pay attention to the negative impacts of wind farms on soaring birds and put forward three concrete and feasible recommendations, expecting countries to enact governmental constraints to find a balance between economic, social, and ecological benefits.

Electromagnetic disturbance characteristic of typical high voltage switchgear interruption process in offshore wind farm based on integrated conduction model

AbstractIn an offshore wind farm, a high‐voltage switchgear interruption in an offshore substation creates a high‐frequency, high‐amplitude overvoltage that can cause severe electromagnetic interference problems in the intelligent electronic device. To address this issue, mathematical models, vector fitting and finite element method were used to build an integrated electromagnetic disturbance conduction model, whose accuracy was verified by large current interruption tests. Through the simulation of a typical offshore wind farm's single‐phase grounding fault interruption process, the electromagnetic disturbance characteristics and the impact of the ground position are obtained. The results show that during the fault interruption process of phase B grounded, the dominant frequency of disturbance voltage can reach 4.34 MHz, and the amplitude can reach 1.12 kV. The ground grid has a large impact on the interference voltage, which can reach a maximum value of 2.28 kV under specific conditions. The disturbance voltage in the fault process mainly comes through the current transformer, while during the interruption process, it mainly comes through the ground grid. The authors provide a method to establish the integrated electromagnetic disturbance simulation model, which can improve the accuracy of the electromagnetic disturbance conduction process simulation of intelligent components.

Revealing the ecological impact of low-speed mountain wind power on vegetation and soil erosion in South China: A case study of a typical wind farm in Yunnan

Due to the limited local electricity consumption in northeastern, northwestern, and northern China, developers are committed to unlocking wind resource-rich areas in the mountainous regions of southern China, leading to the southward movement of wind power. Construction processes destroy vegetation (especially forests) and may cause subsequent ecological impacts. However, due to the scarcity of cloudless remote sensing images in southern China, the ecosystem impact of low-speed wind farm construction in southern China remains unclear, which is not conducive to decision-making. This study used the flexible spatiotemporal data fusion (FSDAF) model to fuse the normalized difference vegetation index (NDVI) value obtained by Landsat 5 satellite Thematic Mapper (TM)/Landsat 8 satellite Operational Land Imager (OLI) and the NDVI value obtained by Moderate-resolution Imaging Spectroradiometer (MODIS) images. Cloudless and high-resolution (30 m) fusion results were used to establish a system framework for evaluating and quantifying the impact of low-speed mountain wind farms on vegetation and soil erosion. The results showed that wind farm construction led to a significant decline in mountain vegetation growth. The impact ranges of wind farm roads were mainly within the 0–30 m buffer zone (the reduction range of NDVI was 0.04–0.22), and the impact ranges of wind turbine foundations were mainly within the 0–60 m buffer zone (the reduction range of NDVI was 0.00–0.26). In addition, the NDVI in regions damaged by mountain wind farm construction was more sensitive to climate change. After vegetation damage, the synergistic effect of factors such as rain and terrain may lead to the doubling of soil erosion (compared to 2010, the increase in soil erosion could reach 1195.96%). In the study area, roads should be scientifically planned to reduce their length, and vegetation restoration and environmental supervision should be strengthened.

Research on Wind Power Prediction Based on A Gated Transformer

Wind power, as a type of renewable energy, has received widespread attention from domestic and foreign experts. Although it has the advantages of cleanliness and low pollution, its strong randomness and volatility can bring disadvantages to the stable operation of the power grid. Accurate power prediction can avoid the adverse effects of wind power, and is of great significance for power grid frequency regulation, peak shaving, and energy improvement. However, traditional wind power prediction methods can only achieve accurate predictions in the short term and perform poorly in medium- to long-term prediction tasks. To address this issue, a power prediction model based on a Gated Transformer is proposed in this paper. Firstly, it can extract features from different types of data sources, effectively capture their correlations, and achieve data fusion. Secondly, gating unit, dilated convolution unit, and multi-head attention mechanism are added to improve the Receptive field and generalization ability of the model. In addition, adding a decoder to guide data prediction further improves the accuracy of prediction. Finally, experiments are carried out with the data collected from typical wind farms. The results show that the proposed Gated Transformer achieves consistent state-of-the-art results in prediction tasks on different time scales.

Carbon Fiber Composites for Large-Scale Wind Turbine Blades: Applicability Study and Comprehensive Evaluation in China

Wind energy is a type of clean energy that can address global energy shortages and environmental issues. Wind turbine blades are a critical component in capturing wind energy. Carbon fiber composites have been widely recognized for their excellent overall performance in large-scale wind turbine blades. However, in China, the wide application of carbon fiber composites in wind turbine blades still faces many problems and challenges. This paper examines the current state of carbon fiber composites for wind turbine blades and the geographical distribution characteristics of wind resources in China. The economic revenues from increasing the length of wind turbine blades in four typical wind farms, including offshore wind farms, are compared. Using a mathematical model, the energy efficiency of carbon fiber composites in the application of large wind turbine blades is evaluated from the aspects of cost, embedded energy, and carbon footprint. Further, the current relationship between supply and demand for the industrial structure of carbon fiber in China is revealed. The manufacturing technologies for carbon fiber composite wind turbine blades are analyzed, and corresponding countermeasures are proposed. Finally, the incentive policy for applying carbon fiber composites to wind turbine blades is explained, and the development prospects are explored. In this paper, the economics and energy efficiency of the application of carbon fiber composite materials in large wind turbine blades are analyzed and comprehensively evaluated by using mathematical models, which will provide a valuable reference for China’s wind turbine blade industry.

Research on Representative Engineering Applications of Anemometer Towers Location in Complex Topography Wind Resource Assessment

The typical location and number of anemometer towers in the assessed area are the key to the accuracy of wind resource assessment in complex topography. As calculation examples, this paper used two typical complex topography wind farms in Guangxi, Yunnan province in China. Firstly, we simulated the wind resource status of the anemometer tower in the Meteodyn WT software. Secondly, we compared the simulated wind resource with the actual measured data by the anemometer tower in the same situation. Thirdly, we analyzed the influence of anemometer tower location and quantity in the accuracy of wind resource assessment through the comparison results. The results showed that the range which the anemometer tower can represent is limited (

Control Strategy for Low Voltage Ride-Through Based on Fast Protection Circuit Breaker

Automatic reclosing, which is the installed feeder line of the wind farm, will make the voltage drop twice when permanent faults occur in a system. It is easy to cause the wind farm to disconnect from the power grid. The control strategy for low voltage ride-through based on a fast protection circuit breaker is proposed to solve this problem. The control principle of fast protection circuit breakers applied in typical wind farm traversing scenarios is analyzed. It can improve the success rate of low voltage ride-through while voltage drops twice. Through the coordination of the fast protection circuit breaker body and the control system, the impact time of fault current in the system can be significantly shortened compared with the traditional circuit breaker. Finally, the effectiveness of the control strategy is verified by simulation in PSCAD/EMTDC environment.

Subsynchronous control interaction analysis between PMSG-based offshore wind farm and SVGs

With the rapid development of wind energy, the subsynchronous oscillations (SSO) involved with wind conversion systems, has been the focus of academic concern. To analyse the subsynchronous control interaction (SSCI) issue of multi-converters (wind-power converter and widespread SVGs), the state matrix of a typical PMSGs-based offshore wind farm system including SVGs is constructed . It is proposed that SVGs in wind farm may cause SSCI issue by their uncoordinated control targets. Under certain conditions, the SSCI between SVGs forms the dominant oscillation mode, delivered with variation of the SSO frequency. By calculating the influence factors along with different converters, it can be concluded that unreasonable parameter configuration of converters and variations of operating conditions affect the SSCI characteristics. At last, time domain simulation is used to verify the SSCI mechanism and characteristics of the PMSG-based offshore wind farm and SVGs.

Multi-objective coordinated optimization of power system with wind power accommodation

To decrease wind curtailment rate and increase wind power accommodation, power purchase area should increase wind power purchase. However, the current electricity price of wind power is higher than that of thermal power, which makes wind power accommodation difficult to quantify. It is not practical to optimize these two objectives of the wind power accommodation and power purchase cost. Based on this contradiction, this paper describes this contradiction by constructing a multi-objective optimization model (MOOM) that considers the above two objectives. Firstly, the model is resolved by hybrid particle swarm optimization and gravity search algorithm (HPSO-GSA) to obtain Pareto optimal solution set. Then, according to fuzzy satisfaction function, Pareto optimal solution is selected from Pareto optimal frontier. Finally, the typical daily load and wind farm output value in spring of a certain region are taken as examples for simulation verification. It is verified that the proposed method can indeed provide a scientific power purchase scheme for regional power purchase.

Mapping the evolution of Canada’s wind energy fleet

This paper describes the development of a data set mapping all commercial wind turbines in Canada: the 9th largest onshore wind energy fleet in the world. Details contained in the data enable the analysis of the evolution of commercially deployed wind turbines as well as the illustrating the effects of the introduction and cessation of different policies affected historical deployment. In total, over 6700 turbines spanning more than 265 projects and nearly 14.0 GW of generating capacity were indexed including coordinates, model, manufacturer, owner, tower height and commissioning date. Data were compiled from publicly available sources including planning documents, technical reports, environmental impact studies and acoustic emission reports. In 2021, average rotor diameters were 140 m with 108 m tall towers, more than triple the average diameter and double the average tower height from 20 years ago. The analysis of this data found that a typical wind farm in Canada consists of 0.97 turbines/km2, while the typical installed capacity density has recently jumped from around 1 to 2.94 MW/km2 in 2021. Competitive procurement policies have often trended towards larger installed capacities, whereas smaller wind farms emerged more often when policies directed at community ownership were in place. Policies that encouraged local manufacturing developed regional clusters of particular manufacturers, but generally ceased after the local manufacturing policies ended. This work serves as a benchmark for the evolution of wind energy in Canada and a framework for similar data sets in other jurisdictions, and provides a resource for academic researchers, wind farm developers, government bodies and planning agencies as well as the general public.

Configuration of suppression schemes against high-frequency transient reignition overvoltages caused by shunt reactor switching-off in offshore wind farms

It is crucial to protect offshore wind farms from overvoltages because of bad accessibility and high maintenance costs. During the process of switching-off a shunt reactor with a 40.5 kV vacuum circuit breaker, high-steepness reignition overvoltages may occur. In order to reproduce this reignition overvoltage accurately and further determine a suitable mitigation scheme, this paper proposes a new quantitative evaluation framework, which comprehensively considers its magnitude and steepness. Firstly, main component models of a typical offshore wind farm are developed in PSCAD/EMTDC, which consider their high-frequency transient characteristics. Subsequently, the transient characteristics of reignition overvoltages at the 35 kV side of shunt reactors are analyzed. Then, for the sake of investigating the mitigation performance of different overvoltage protective schemes, a quantitative evaluation is conducted. Simulation results show that switching-off shunt reactors in offshore wind farms can cause high-frequency transient reignition overvoltages characterized by a high amplitude and a high steepness at its terminals. It is found that a resistor-capacitor filter or a new scheme combining a surge arrester with a smart choke can be installed on the terminals of shunt reactors to decrease both the magnitude and the steepness of reignition overvoltages.

A Novel Energy Absorption-Based Protection Scheme for Surge Transients in Large-Scale Wind Farm System

Onshore wind farms are usually connected to the power grid by 35 kV transmission lines. Due to the high altitude of the wind turbines and their associated long transmission corridors, lightning activities are more frequent for onshore wind farms compared to the power plant in plain areas. In addition, the insulation levels of 10–35 kV distribution lines are generally low. All these facts make proper lightning strike protection a technical challenge for onshore wind farms. Taking a typical onshore wind farm as the testing object, the structure of the 35 kV distribution line connected to this wind farm is modeled by EMTP software in this paper. The leakage current and absorbed energy of the arrester at the head tower when lightning currents of different amplitudes inject into the overhead line are studied. By calculating the energy accumulated in the arrester within 100 µs, the causes of the lightning trip and damage to the arrester of the distribution line, i.e., the energy accumulation mechanism, are clarified. Eventually, the lightning protection updating scheme for this distribution line is proposed: a combination of refitting the porcelain cross arm at the head and end of the tower and filling the grounding body of the tower with a drag-reduction agent, which shows that the lightning resistance level of the distribution can be improved by 50 times in maximum.

Wind Farm Energy Storage System Based on Cat Swarm Optimization–Backpropagation Neural Network Wind Power Prediction

To solve the instability problem of wind turbine power output, the wind power was predicted, and a wind power prediction algorithm optimized by the backpropagation neural network based on the CSO (cat swarm optimization) algorithm was studied, and a wind farm energy storage system model was built on this basis. By collecting the wind power plant’s historical wind speed, power, and other parameters, the short-term wind farm output power was predicted, and the operation of the wind farm energy storage system was controlled to suppress the output power of the wind farm when the wind farm was connected to the grid so as to improve the stability of the output power of the wind farm. At the same time, typical wind farm data were taken as an example to verify the feasibility of the proposed method.