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Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the large-scale integration of wind power into the grid, the inertia and disturbance resistance of the power system are gradually declining, leading to an increasingly prominent frequency stability issue. An adaptive frequency support control strategy for wind farms based on rotor kinetic energy is proposed. Firstly, a participation factor is proposed, which determines the degree of wind farm participation in frequency support based on the average rotor speed of the wind farm and adaptively adjusts the inertia response parameters and frequency droop control parameters of wind farms. Based on the real-time available rotor kinetic energy of wind turbines, the frequency modulation power of wind farms is dynamically allocated. The frequency support ability of wind turbines with more rotor kinetic energy is fully utilized and excessive participation of low-speed wind turbines is avoided in frequency modulation. After the frequency support is completed, a staged rotor speed recovery method is used to restore the rotor speed of the wind turbines. Simulation results show that the proposed strategy can adaptively adjust the active output power based on the operating status of the wind farms and the available rotor kinetic energy of the wind turbines while achieving effective support for the system frequency and avoiding secondary frequency drop.

Wind farm wakes have a significant impact on momentum and turbulence fluxes within the atmospheric boundary layer， thereby influencing the local climate and environment.Mesoscale numerical models incorporating wind farm parameterizations are powerful tools for studying the climate and environmental impacts of wind farms.In this study， the wind speed and turbulence kinetic energy profiles of the Fitch wind farm parameterization scheme in the WRF mesoscale model are evaluated in the turbine and wake regions using high-resolution Large Eddy Simulations （LES） as “true values” and a method based on the relation derived from classical momentum theory is proposed to correct the grid-inflow wind speed.The method takes into account the blocking effect caused by the grid equivalent thrust and the corrected wind speed is closer to the free-stream wind speed.Results show that the difference between the grid-inflow wind speed from the original Fitch parameterization scheme and the LES is significant and sensitive to the model horizontal resolution.The Fitch-new parameterization with corrected grid-inflow wind speed reduces the relative error in absolute value between grid-inflow and free-stream wind speed to less than 1% across different horizontal resolutions （1000 m， 500 m， and 250 m）.The spatially averaged thrust and output power are consistent with LES results.The Fitch-new parameterization improves the simulated wind speed deficit in the wake zone of wind turbine， especially in the grid with turbine under high resolution.Although the simulated increase in turbulent kinetic energy and its vertical distribution in the wake zone is improved compared to the original Fitch scheme， there are certain issues that require further investigation.

ObjectivesConsidering the difference of primary frequency regulation ability of wind turbines under different wind conditions in wind farms, the evaluation method of frequency regulation capability of wind turbines was optimized on the basis of sag control and inertia control strategy.MethodsAn improved multi-unit frequency modulation reference power cooperative control strategy was proposed.ResultsThe introduction of multi-unit cooperative control method can effectively improve the distribution of frequency modulation reference power among units, thus effectively adjust the degree of each unit participating in the primary frequency regulation of the system. An improved cooperative control strategy was introduced on the basis of inertia control and sag control. According to the operation state of the unit under the actual wind condition, the reference power of the unit which can effectively participate in the primary frequency modulation was evaluated. The introduction of frequency modulation capability coefficient can realize the energy distribution of the reference value of frequency modulation power among units.ConclusionsThe cooperative control strategy can effectively protect the speed of wind turbine and improve the frequency response of power grid.

As the major demand for the development and utilization of new energy, the large-scale development of wind power is a key support in achieving the strategic goal of “cabron peaking and carbon neutrality” for China. The problem of safe and stable operation of wind farms caused by external grid faults has become one of the key bottlenecks restricting the large-scale, clustered, and intelligent development of wind power. This paper mainly focuses on the voltage surge condition of the power grid. First, it analyzes the transient characteristics of high voltage ride-through (HVRT) of the doubly-fed induction generator-wind turbine, permanent magnet synchronous generator-wind turbine, and wind farms. Then, it summarizes the corresponding HVRT and post-fault voltage recovery coordinated optimal control strategies based on the different control areas, and it classifies and compares the working principles and advantages and disadvantages of various control strategies. Afterwards, it recapitulates the principle, advantages and disadvantages, and effects of the existing HVRT control method for large-scale wind farms, and analyzes the differences between the single wind turbine and the large-scale wind farms from the perspective of control structure. Finally, it discusses the development trend and potential research hotspots of wind farm voltage intelligent safety control in the future, aiming to provide reference for improving the large-scale application of wind power and the safe operation of power grids in China.

Fast and accurate analysis of the short-circuit characteristics of large wind farms has important engineering application value, and the short-circuit characteristics of wind farms under the influence of the wake effect vary greatly. Therefore, it is necessary to establish a wind farm short-circuit fault time equivalence model. A wind farm short-circuit fault dynamic equivalence method considering the effect of wake effect is proposed. First, the wake effect factor is defined to reflect the degree of the unit affected by the wake effect. Then, the wake effect factor is used as the grouping basis to reduce the variability of operating state of the units within the group under the influence of the wake effect. A positive- negative- zero-sequence network equivalence method is analyzed to improve the effectiveness of the equivalence model in asymmetric short-circuit faults. An equivalence method suitable for zero-sequence network is proposed and a platform is built for verification. The simulation results show that the dynamic short-circuit fault equivalence model proposed can accurately reflect the active and reactive short-circuit output characteristics of wind farms under the influence of the wake effect.

In order to address the issue of premature attenuation of the lifespan of some wind turbines due to uneven fatigue among wind turbines in wind farms, an active power optimal scheduling strategy based on fatigue distribution was proposed. This method took active power distribution as the research object, comprehensively considered the influence of fatigue load and wake effect, combined weight redistribution was carried out according to the power prediction information of wind turbines and the power reference value of fatigue coefficient, and adaptive genetic algorithm was used to find the optimal weight coefficient. The simulation results demonstrate that the proposed method can track superior dispatching instructions, while achieving uniform fatigue levels among wind turbines in the field, reducing fatigue load, and decreasing the operation and maintenance costs of the wind farm. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Drainage & Irrigation Machinery Engineering / Paiguan Jixie Gongcheng Xuebao is the property of Editorial Department of Drainage & Irrigation Machinery Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Plateau Meteorology is the property of Plateau Meteorology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Power Generation Technology is the property of Power Generation Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the increasingly prominent issues of climate change and energy shortages, large-scale development of renewable and clean energy has become a common choice for countries around the world. Among them, onshore wind power plays an important role in the global energy transformation process due to its mature technology, safety, and efficiency advantages. However, because wind energy resources have the characteristics of low energy density, instability, and spatial dispersion, large-scale development of onshore wind power requires a large amount of land resources to support. To ensure the land resource demand for wind power development under the goal of 'carbon neutrality,' this paper conducted a quantitative study on the land occupation of onshore wind power in China. Firstly, based on the criterion of exclusivity, this paper reclassified the types of land occupation for wind farms from a spatio-temporal perspective. Secondly, based on the sample parameter method and the alpha shape algorithm, this paper calculated the land area of existing onshore wind farms in China categorically. Finally, considering the development trend of wind power technology, this paper systematically simulated and predicted the wind power land demand in different development scenarios under the 'carbon neutrality' goal. The results indicated that: (1) Based on the principle of exclusivity, the total land use area of wind farms could be divided into two parts in the spatial dimension: the direct impact area and the indirect impact area. The direct impact area could be divided into two parts in the temporal dimension: permanent land use and temporary land use. (2) In 2022, the land occupied by the wind turbine foundation, permanent land use, temporary land use, and direct impact area of China's onshore wind farms reached 48.28 km², 352.08 km², 1 234.86 km², and 1 638.81 km², respectively. (3) In 2022, the indirect impact area and total land use area of China's onshore wind farms reached 9.81x104 km ² and 9.99x104 km², respectively. The total land use area was almost equivalent to the total urban land use area of China. (4) Compared with the scenario of current technical level, under the scenario of technological progress and wind turbine upgradation, the various types of land use demand for wind power in China will have significantly decreased by 2060, and land use demand will have reached its peak by around 2050. The large-scale upgradation of wind turbines has great potential for saving land resources. Therefore, perfecting the land use standards of wind power construction, improving the land use efficiency of wind power development, and promoting the construction of offshore wind power are important paths for coordinating the sustainable use of wind energy resources and land resources. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[Introduction] To test the reliability of the CMA-WSP wind speed product in short-term wind resource forecasting, the CMA-WSP 3 d wind speed forecasting product with a wind speed of 100 m is tested and analyzed. [Method] This research was based on the measured data of 100 m wind speed in three wind farms in Zaoyang Zhoulou, Macheng Caijiazhai and Central Dajin. [Result] The results are as follows: (1) CMA-WSP has a good overall forecasting performance on the wind speed in Zaoyang wind farm within three days. The forecasted results are consistent with the measured wind speed change trend. The correlation between the forecasted and the measured wind speed on the first day are 0.728, 0.74 and 0.86 for 15 min intervals, hourly averages, and daily averages, respectively. (2) The relative error between the CMA-WSP forecast and the measured wind speed shows a strong regularity. The relative errors for the forecasted wind speed on the first day are 68 %, 70 % and 92 % for 15 min intervals, hourly averages, and daily averages, respectively. The forecasted wind speed is higher than the measured wind speed. The hourly average wind speed and relative error are characterized by low wind speed during the day and high wind speed at night. The change of monthly average wind speed is opposite to the change of MRE value, and it is the lowest from January to June and from October to December, and the highest from July to September. (3) When considering regional differences, CMA-WSP has the best forecasting effect on the wind speed in Zaoyang wind farm. The correlation between the forecasted and the measured wind speed on the first day can reach 0.728, and the correlation on the second to third days is also more than 0.6. The correlation between the forecasted wind speed and the measured wind speed in Caijiazhai and Central Dajin by CMA-WSP is less than 0.6. [Conclusion] CMA-WSP forecasting performance is favorable as a whole, and the relative error has strong regularity. It is beneficial to revise the product and reduce the error level in the next step.

Copyright of Southern Energy Construction is the property of Southern Energy Construction Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to explore the wake interference effect of the wind farm and quantitatively predict the overall output efficiency under different wind farm layouts, the mathematical model and prediction program of the wake interference effect was constructed. The newly established model originates from the Jensen model, which was the most commonly used to predict the wake effect of a single wind turbine, and was based on the shadowing effect and shadowing mode of the upstream wind turbine on the downstream one in the wind farm layout. The variation of wind farm overall output efficiency with wind farm scale, row/ column spacing and wind direction angle in homogeneous arrangement mode, and with dislocation spacing and wind direction angle in staggered arrangement mode were studied. The results show that the overall output efficiency increases with the increase of row/ column spacing under different wind farm scales, and decreases with the increase of wind farm scales under different row/ column spacing. There are obvious differences in the overall output efficiency of the wind farm under different dislocation spacing and wind direction angle. It is necessary to reasonably arrange the row/ column spacing of wind turbines according to the wind direction where the wind farm is located, to maximize the overall output efficiency. Research can provide guidance for the prediction of wake interference effect under any wind farm layout and rational design of wind farm layout. [ABSTRACT FROM AUTHOR]

The distributed parameter model of the transmission line is a necessary premise for accurately analysing the harmonic instability of the wind farm. However, when the model is used for harmonic instability analysis, it causes difficulties in solving the zero and pole distributions of the transcendental equation of the system. Given this, the impedance model of the wind farm considering the line distribution parameters is established and verified. Next, a method based on Pade approximation to solve the zero and pole distributions of the transcendental equation is proposed. The exponential function is approximated to the rational fraction only by selecting the order of the rational fraction, without fitting coefficients. Further, the influence of different transmission line models on the harmonic instability under different line lengths and grid strengths is analyzed. Finally, the effectiveness of the proposed method is verified by the simulation case. The results show that the effect of distribution parameters cannot be ignored, otherwise, it may lead to the inaccurate analysis of stability and deviation or even omission of harmonic amplification points in the high-frequency band. The proposed method can not only accurately evaluate the stability of the system, but also analyze the harmonic amplification points.

A huge amount of historical data has been generated during the operation of wind farms, and the improvement of data quality is the prerequisite work for achieving high-efficient and intelligent maintenance of wind farms. Therefore, the distribution characteristics and formation mechanism of wind power data in wind farms are analyzed, and a variance change rate criterion and quartile combined method to identify abnormal wind power data is proposed. Firstly, the original wind power curve is preprocessed by physical rules, and the obviously abnormal data is eliminated. Then, the abnormal power data points of the accumulation type of the wind power curve are identified and cleaned by the wind power variance change rate criterion method, and the threshold value of the criterion is automatically obtained through the box plot. After that, the quartile method is used to identify and clean the discrete abnormal data points. Finally, the feasibility of the proposed algorithm is verified by an example. The results show that the proposed algorithm has the advantages of easy implementation, high efficiency, and strong universality. The anomaly recognition performance of the proposed method is superior to the local outlier factor (LOF) or Thompson tau-quartile algorithms, and the value of its time consumption is 9.6 s or 0.49 s lower than that of the LOF or Thompson tau-quartile algorithm, respectively. The universality of the proposed algorithm has been verified at 5 wind farms in different locations.

The informatization and intelligence of wind farm lock and key management is the key to further improve the level of wind farm operation and maintenance management. This paper designs a smart key security management and control architecture for wind farms. Combined with the proposed short-range wireless communication anti-jamming technology, key encryption, and dynamic attack detection technology, a set of wind farm smart key security management and control system has been developed. The system can realize the functions of opening and closing information traceability and risk prevention, etc., which improves the network, digital and intelligent level of wind farm operation and maintenance.

Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to reduce the impact of the wake effect on the wind farm power generation, to improve the utilization rate of wind energy, an optimization algorithm-adaptive weight Genetic-Particle Swarm Optimization Algorithm (GA-PSO) was proposed. Firstly, the cost per unit of produced energy and coordinates of wind turbines were regarded as the target function and variable respectively, inertial weight was added to the speed update of optimization variable to change the search speed. Secondly, the layout of wind turbines was optimized based on micro-site selection of WASP. Eventually, the calculation results were also compared with the genetic algorithm (GA), firefly algorithm(FA) and particle swarm algorithm(PSO) optimization algorithms. The results show that the cost per unit of produced energy in the wind farm optimized by PGOA algorithm is 2 016 yuan/ GWh, reducing 232 yuan/ GWh and the annual power generation is 82. 633 GWh, increased by 8. 538 GWh compared with before optimizing, meanwhile the wake loss is decreased by 1. 12%. The research results are guiding significance to the future micro-site selection of wind farms. [ABSTRACT FROM AUTHOR]

The number of turbines in the actual wind farm is large and at different operating wind speeds. It is very complicated to build a sub-synchronous oscillation study model that includes a detailed model of each turbine. Furthermore, it is time-consuming and computationally intensive to apply the model to the sub-synchronous oscillation study. Therefore, it is necessary to simplify the complexity of the sub-synchronous oscillation study by establishing a wind farm equivalent model that retains the sub-synchronous oscillation characteristics of the interaction between the wind farm and the interconnected system. In this paper, the effect of different control strategies on the sub-synchronous oscillation model in different operating wind speed intervals of wind turbines was taken into account, and the wind turbines were clustered using the K-means algorithm based on wind speed indexes. Considering the effects of different current amplitude and phase at the terminal of each wind turbine, a simplified modeling method of the collector system based on power equivalence was proposed. And the relevant wind farm simulation model was built in PSCAD/EMTDC, and the sub-synchronous oscillation modes before and after the equivalence were used to verify the rationality of this equivalence method.

Aiming at the policy background of the comprehensive parity of wind power grid and the industry requirements for reducing the cost of electricity per kilowatt-hour, this paper implements EAM system based on Maximo platform. Through information system integration, hierarchical processing of operation and maintenance business, and wind turbine design optimization, fast response and self-learning fault handling, preventive maintenance reminder based on equipment status are implemented, and the mean time to repair(MTTR) of wind farms is reduced by 17%. Through the refined control of wind turbine data, material traceability based on serial number, standard operation and process control, dynamic adjustment of inventory based on material turnover rate has been realized, and the cost of spare parts in the wind farm inventory and the annual operation and maintenance costs(CO&M) of wind farm are reduced. This effectively reduces the levelized cost of energy in wind farms.

A new scheme of lithium ion battery and super capacitor hybrid energy storage system in wind farm was proposed. The original wind power was decomposed by the wavelet packet frequency division technology to obtain the compensation power of the hybrid energy storage system. Taking the hybrid energy storage configuration scheme as the optimization variable, the improved life cycle cost was introduced, and a multi-objective energy storage system configuration model of net income volatility waste air volume was established. The model was solved by the improved probabilistic mutation particle swarm optimization (PMPSO) to obtain the Pareto solution set. The solution set was normalized to obtain the optimal allocation scheme of entropy weight. Compared with the single objective optimal scheme, it shows that the optimal allocation scheme of entropy weight takes into account both economy and function. Compared with the adaptive quantum particle swarm optimization (AQPSO), it is verified that the optimal allocation scheme of entropy weight obtained by improved PMPSO algorithm not only ensures the small waste air volume and volatility, but also configures the energy storage with smaller capacity to obtain greater benefits and meet the multi-objective allocation requirements.

Distributed energy storage (DES) wind turbine is an effective means to solve the problem of system frequency stability caused by large-scale wind power connection. In this paper, an inertial control method for DES wind farms based on model predictive control (MPC) is proposed.First, the linearized prediction model of the DES wind farm is established. Then, on this basis, in combination with the control framework of MPC, an optimization model and strategy of MPC inertial control are proposed considering the cost of energy storage loss and the balanced change of wind turbine rotor speed,in order to achieve the balanced change of wind turbine rotor speed during inertia control. The simulation results show that the proposed control strategy can effectively coordinate the active power output of the wind power generation unit and the energy storage system unit in the DES wind turbine, reduce the cost of charging and discharging loss of the energy storage system, and ensure that the rotational speed of all wind turbines in the wind farm tends to be average during the inertial control period, avoiding the problem of wind turbines exiting frequency regulation due to excessive reduction of the rotational speed of wind turbines. The inertial control strategy of the DES wind farm is beneficial to improve the frequency stability of the power grid, which is of great significance to ensure the safe operation of the power grid.

Collector lines in wind farms mostly contain multiple branches,and generally only one measuring device is set at the head of the line. If single-end fault locating is carried out based on the device after a fault,only multiple suspected fault points can be obtained on different branches,and the actual fault branch cannot be determined. Although the current multi-terminal algorithm can locate the fault branch,the number of measuring devices required is too much,which is expensive. In this paper,the topological structure of a collector line is analyzed under the most common single-phase grounding fault. It is pointed out that when faults occur on different branches,the same data may be collected by the single-end measuring device,making the fault branch cannot be identified only by the single-end electrical volume,so it is necessary to add measuring devices to identify the fault branch. In order to reduce the investment,the minimum number and location of additional measuring devices required to identify the fault branch on lines with different topologies are determined. Combined with the new measurement information and the existing single-terminal method,an algorithm is proposed to locate the fault branch and achieve fault location based on multi-terminal fault recording information. The effectiveness and practicability of the proposed method are verified by PSCAD/EMTDC simulation. The proposed algorithm is improved from the single-terminal impedance method to solve the problem of fault branch location,and it can be realized by adding a few measuring devices on the basis of the current configuration. The cost is low,which means a good engineering application prospect.

Copyright of Electric Power is the property of Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Extensive use of wind energy resources in China not only brings huge economic benefits， but also reduces environmental pollution caused by various fossil fuels.However， some studies have shown that the rotation of the rotor blades will increase surface roughness and then enhance turbulence in the rotor wakes by changing the stability of the atmospheric boundary layer （ABL）.If these changes are spatially large enough， they may affect the local and even regional weather and climate.With increasing installed capacity of wind farms， a correct understanding of the possible impacts of wind power on climate is needed， especially its impact on temperature.It is of great significance for the scientific use of wind energy resources and ensuring the sustainable development of energy in China.Satellite remote sensing technology can conduct large-scale， long-term， and regular observations of the land surface， and data acquisition is relatively easy.The MODIS satellite data has been used in studies of the impact of wind farms on the land surface temperature.Based on MODIS satellite data from 2003 to 2019， this paper explores the impact of two large wind farms in Shangyi and Zhangbei in northern Zhangjiakou on land surface temperature.Combined with wind direction， vegetation index， surface albedo， and land use data， the results show that： （1）During spring， autumn， and winter， the land surface temperature downwind of the wind farm is higher than that upwind during nighttime， the opposite is true during the day.The land surface temperature in the downwind area of the wind farm is significantly affected by the wind turbine wake.（2）Compared with the surrounding areas not affected by wind farms， the land surface temperature within wind farms increased significantly in spring and summer， especially during spring night with an increaing trend of 0.379 ℃·（10a）-1， followed by summer night and autumn night with increaing trends of 0.206 ℃·（10a）-1 and 0.211 ℃·（10a）-1， respectively.The warming trends` spatial pattern couples well with the geographic distribution of wind turbines.（3）Although wind farms in Shangyi and Zhangbei have impacts on LST， compared with other studies the increasing trends caused by wind farms in this study area is weak and much smaller than the interannual LST increasing trend in the Bashang area with increaing trends of 0.817 ℃·（10a）-1， the land surface temperature increases caused by the operation of wind turbines is negligible compared with which caused by fossil fuels.Changes in land surface temperature within wind farms are not only affected by the operation of wind farms but also a combination of various influencing factors， such as climate change， vegetation， and land-use changes.

The influence of wind farms on atmospheric boundary layer structures， especially turbulence characteristics， is an important topic in the field of wind energy meteorology.Taking Qilinshan Wind Farm in Shangyi County， Hebei Province as the research object， this paper explores the effect of wind farm operation on turbulence intensity using long-term （2006 -2019） wind data from 70 m and 50 m of a 70 m-wind tower in the wind farm.May of each year was selected for analysis considering the consistency and complement of the data.A comparative analysis of turbulence intensities before （2006 -2009） and after （2010 -2019） the wind farm construction is conducted.The results show that the turbulence intensity shifted to higher values， i.e.， the turbulence increased， after the construction of the wind farm， with the turbulence intensity increased more during night than that during the day.Turbulence intensity decreased with the increase of wind speed.The operation of wind farm increased the turbulence intensity significantly when wind speed was in 4~15 m·s-1 for 70 m， and 3~12 m·s-1 for 50 m.Turbulence intensity had obvious diurnal variation.The operation of wind farm increased the turbulence intensity both during the day and night.The increase of turbulence intensity at 50 m was slightly greater than that at 70 m during the day， but was lower than that at 70 m during night， which made the turbulence intensity difference between the two layers and between day and night decreased after the wind farm construction.

With the rapid development of wind power generation， the impact of the construction and operation of utility-scale wind farms on the local climate has attracted extensive attention.In order to study the characteristics of the local climatic response to the deployment of wind farms under different surface conditions， the area from Wulan Chaka to Gonghe Shazhu Yuxiang， which is located in the southwest of Qinghai Lake， was chosen as center， and the mesoscale meteorological numerical model WRF （Weather Research and Forecasting Model） and the Fitch wind turbine model were carried out under the conditions of flat canyons， high-altitude mountains and water body， respectively.The results showed that： Under the ideal situation of the same installed capacity， the averaged wind speed inside the wind farms showed the significant decrease trends under the three sensitivity experiments， and the amplitude and scope of wind speed attenuation was stronger during nighttime than that during the daytime， and the wind speed attenuation in the vertical direction can reached up to 600 m.The 2 m temperature inside the wind farms located in flat canyons or water body mainly decreased， which was possibly related to the negative temperature gradient and the water evaporation enhancement， and the temperature cooling was more significant during nighttime while no significant changes in 2 m temperature were detected over the mountain wind farm.Meanwhile， the changes of 2 m humidity inside the wind farms were not obvious， and the overall decreased trends were mainly weak during nighttime.Due to rotation of the wind turbine， the planetary boundary layer height increased significantly.The characteristics of the local climatic response to the deployment of wind farms under different surface conditions were different， which was mainly related to the different climatic features under different surface conditions， and was also affected by the spatial layout of wind turbines.These results can provide some support for the design of climate-friendly wind farms.

In recent years, sub-synchronous oscillation incidents have been reported to happen globally, which seriously threatens the safe and stable operation of the power system. As the capacity of a single wind turbine generator is small, a wind farm with tens even hundreds of wind turbine generators makes the order of the model of the wind farm high with the wind turbine generators complex coupled with each other. Reasonable and effective equivalent modeling of the wind farm is the basis for the study on the reasons, characteristics and suppression of the oscillations in the wind power integrated power system. In this paper, the advantages and disadvantages of the existing relatively thorough methods for wind farm equivalent modeling is analyzed based on the recognition of the reasons for the sub-synchronous oscillations in the wind power integrated power system. Then the issues in the field of equivalent modeling of the wind farm that need to be further studied are summarized, hoping to provide references for the future study of small-signal dynamic equivalence of a wind power collecting system and sub-synchronous oscillations of the wind power integrated power system.

Copyright of Power Generation Technology is the property of Power Generation Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the continuous increase of wind power penetration, the terminal voltage stability of grid-connected wind farms faces significant challenges. The characteristics between reactive power and voltage of each node inside the wind farm is studied, and a hierarchical and partitioned reactive power control strategy based on clustering of wind turbine active power output is proposed. Firstly, the voltage command together with the wind farm real-time data are obtained through the automatic voltage control system, which are then used for calculating its reactive power demand. Secondly, the wind turbines are partitioned based on the active power output by using the fuzzy C clustering method. Furthermore, an improved particle swarm optimization algorithm is proposed to optimize the reactive power distribution, and then guide for the reactive power and voltage control in each cluster. Finally, simulations are performed to prove the effectiveness of the proposed strategy by comparisons with the traditional schemes. The results show that the proposed control strategy can effectively improve the control performance for the connection point voltage and reduce the fluctuations of the terminal voltage in a wind farm. [ABSTRACT FROM AUTHOR]

Copyright of Advances in New & Renewable Energy is the property of Editorial Office of Advances in New & Renewable Energy and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Offshore wind power is an important development trend in the field of renewable energy in China in the future. Firstly, this paper summarized the current development of domestic offshore wind power industry, analyzed the development trend of domestic offshore wind power industry chain, and then introduced the current cutting-edge technology of offshore wind power industry from the subdivided technical field. It mainly covers unit technology, including blade design and pitch control, transmission chain, motor, converter, main control system, etc; networking transmission technology, including AC, DC and low-frequency transmission technology, as well as compact and lightweight offshore platform design technology; engineering construction technology, mainly including offshore engineering, wind farm construction and submarine cable technology; offshore wind power operation and maintenance technologies, mainly including offshore wind power prediction, wake prediction, optimal dispatching, monitoring and maintenance, etc. The research results can provide the reference for the development of offshore wind power in China.

Numerical weather prediction (NWP) correction is one of the key technologies to improve the accuracy of wind and solar power forecasting. However, there are few researches on the correction of NWP irradiance. At the same time, most of the existing NWP wind speed correction methods only consider a single location, ignoring the spatio-temporal coupling of wind speed, which affects the correction effect. Aiming at this problem, this paper proposed an integrated correction method for multi-point NWP wind speed and irradiance considering the spatial and temporal correlation of regional wind and solar resources. Taking the measured wind speed and irradiance data of multiple wind farms and photovoltaic power plants in the region as the learning objective of the correction model, an integrated NWP correction model based on attentional neural network was established to simultaneously correct multiple station-level NWP data with certain correlation. The proposed method was verified by the NWP data and historical wind speed / irradiance data of 8 wind farms and 7 photovoltaic power plants in a region. The calculation results show that compared with the traditional single-point NWP correction method, the proposed method can effectively improve the accuracy of NWP.

With the substantial increase of the installed capacity of wind power, it has brought great challenges to grid stability and safety. In order to improve the grid stability and the anti-interference ability of grid frequency, wind farms urgently need to have frequency regulation function and increase frequency response speed. According to the latest technical requirements such as starting time and adjustment time of grid, this paper analyzes the current mainstream frequency regulation schemes in the market. A frequency regulation scheme combining energy management platform and frequency regulation equipment is proposed, so that the wind turbine receives instructions from the energy management platform and frequency regulation equipment at the same time, and adopts different control strategies for inertia response and primary frequency regulation to adjust the active power of the wind farm to ensure the response speed and stability during frequency regulation.The scheme has been tested for inertia response and primary frequency modulation in a wind farm in Yunnan province. In the test results, the response time of inertia response is less than 1 sec, the control deviation is within ±1% of rated capacity, the starting time of primary frequency modulation is less than 2 sec, the response time is less than 5 sec, the adjustment time is less than 8 sec, and the control deviation of primary frequency regulation is within ±1% of rated capacity. All the technical indexes meet the requirements of Yunnan Grid.

Copyright of Power Generation Technology is the property of Power Generation Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Engineering for Thermal Energy & Power / Reneng Dongli Gongcheng is the property of Journal of Engineering for Thermal Energy & Power and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Engineering for Thermal Energy & Power / Reneng Dongli Gongcheng is the property of Journal of Engineering for Thermal Energy & Power and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The volatility and randomness of wind speed have caused potential safety hazards to wind power grid integration. Improving wind speed forecasting is crucial to the stability of wind power systems and the development of wind energy. A novel short-term wind speed forecasting model (MI-RNN) was proposed based on mutual information (MI) and recursive neural network (RNN). In this model, the MI theory was introduced to select the optimal length of historical wind speed sequence (τ), and the method of using each τ step to forecast wind speed at the next time point was adopted to input the historical wind speed data into RNN for model training. The final wind speed forecasting result was output by the trained RNN model. Besides, the MI-RNN model was applied to the wind speed dataset collected from a wind farm and the forecasting accuracy was compared with that of the traditional wind forecasting methods. The results show that the MI-RNN model has achieved a higher forecasting accuracy compared with the commonly used wind farm wind speed forecasting methods, and can accurately forecast the future wind direction, which is expected to be applied to wind speed forecasting of wind farms with spatial dimensions.

Copyright of Plateau Meteorology is the property of Plateau Meteorology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Plateau Meteorology is the property of Plateau Meteorology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Plateau Meteorology is the property of Plateau Meteorology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)