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Due to the widespread penetration of wind energy, large-capacity wind turbines (WTs) are becoming more conspicuous. In this paper, a linear control-oriented large-capacity wind turbine (WT) model is redesigned based on the 15 MW WT in openFAST. Compared with the original model, the redesigned model is more efficient and conducive to the design of linear controllers. To verify the validity of the redesigned WT model, we design a distributed control scheme with deloading operation of the wind farm (WF) to fairly share the load. Simulations are performed to verify the effectiveness of the application in the WF. [ABSTRACT FROM AUTHOR]

Recent advancements in wind farm (WF) construction have prompted designers to focus on optimizing WF performance and minimizing the negative effect of wake interference on power output. One proven technique to reduce wake influence and improve power generation is wake steering, which involves adjusting the yaw angles of wind turbines (WTs). The objective of this study is to evaluate the optimization of WF using machine learning (ML), specifically an artificial neural network (ANN) approach. In the first phase of the study, an ANN power model is selected to standardize wake loss and optimize power. In the next phase, a genetic algorithm (GA) will be employed to determine the best yaw angles for the wind direction based on data collected from the ANN power model. The ANN power model might not account for maintenance tasks around the WF. The results of the optimization demonstrate that energy is maximized between wind directions of 166° to 178°. This research achieved a.97 optimized overall power ratio, compared to non-optimized scenarios, using an optimal yaw angle technique in all directions. The findings of the study demonstrate that ANN-based optimization, combined with standardized wake degradation and suitable yaw angle direction, is an efficient method for WF optimization. [ABSTRACT FROM AUTHOR]

Modeling the atmospheric boundary layer (ABL) in the area of a wind farm using computational fluid dynamics (CFD) methods allows us to study the characteristics of air movement, the shading effect, the influence of relief, etc., and can be actively used in studies of local territories where powerful wind farms are planned to be located. The operating modes of a wind farm largely depend on meteorological phenomena, the intensity and duration of which cause suboptimal operating modes of wind farms, which require the use of modern tools for forecasting and classifying precipitation. The methods and approaches used to predict meteorological phenomena are well known. However, for designed and operated wind farms, the influence of meteorological phenomena on the operating modes, such as freezing rain and hail, remains an urgent problem. This study presents a multi-layered neural network for the classification of precipitation zones, designed to identify adverse meteorological phenomena for wind farms according to weather stations. The neural network receives ten inputs and has direct signal propagation between six hidden layers. During the training of the neural network, an overall accuracy of 81.78%, macro-average memorization of 81.07%, and macro-average memorization of 75.05% were achieved. The neural network is part of an analytical module for making decisions on the application of control actions (control of the boundary layer of the atmosphere by injection of silver iodide, ionization, etc.) and the formation of the initial conditions for CFD modeling. Using the example of the Ulyanovsk wind farm, a study on the movement of air masses in the area of the wind farm was conducted using the initial conditions of the neural network. Digital models of wind turbines and terrain were created in the Simcenter STAR-CCM+ software package, version 2022.1; an approach based on a LES model using an actuating drive disk model (ADM) was implemented for modeling, allowing calculation with an error not exceeding 5%. According to the results of the modeling of the current layout of the wind turbines of the Ulyanovsk wind farm, a significant overlap of the turbulent wake of the wind turbines and an increase in the speed deficit in the area of the wind farm were noted, which significantly reduced its efficiency. A shortage of speed in the near and far tracks was determined for special cases of group placement of wind turbines. [ABSTRACT FROM AUTHOR]

The wake effect reduces the wind speed at downstream wind turbines (WTs), making it necessary for the central controller to collect wind power generation data from each WT. However, wind farms (WFs) face a more complex problem in maintaining the voltage stability at the WT terminal while following the transmission system operator (TSO) instructions due to the information collection as well as the possible data loss during transmission. Therefore, this study proposes a coordinated control method for WTs and multiple power sources based on model predictive control under wake disturbance conditions, aiming to reduce the average voltage deviation in WT terminals and go close to the rated voltage and ensure effective compliance with TSO commands in large-scale WFs. Accordingly, the Jensen wake model was utilized to accurately calculate the available active and reactive power limits for each WT. Energy storage systems and static Var generators were modeled to coordinate and maintain the voltage in all WT terminals within the feasible range, providing peak shaving and valley filling support to reduce wind energy waste and shortfall, thereby enhancing the economic and operational reliability of WF. Further, the effectiveness of the proposed method was validated in MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

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]

A calculation method for optimizing the LCOE and the cost of subsequent PTX production from wind power is presented, based on Weibull parameters and cost functions. The PTX products methanol, ammonia and synthetic diesel are considered, as well as hydrogen, which is converted into an easily transportable form by hydrogenation of a liquid organic hydrogen carrier (LOHC). Haru‐Oni in Chile and Tarfaya in Morocco were chosen as examples of production sites with excellent wind conditions. These are compared with three domestic sites with very good to very moderate wind conditions in the order Dornum (North German coast), Senftenberg (Lausitz) and Ludwigshafen (Rhine Graben). [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.)

When offshore wind farms are connected to a hydrogen plant with dedicated transmission lines, for example, high-voltage direct current, the fluctuation of wind speed will influence the efficiency of the alkaline electrolyzer and deteriorate the techno-economic performance. To overcome this issue, firstly, an additional heating process is adopted to achieve insulation for the alkaline solution when power generated by wind farms is below the alkaline electrolyzer minimum power threshold, while the alkaline electrolyzer overload feature is used to generate hydrogen when wind power is at its peak. Then, a simplified piecewise model-based alkaline electrolyzer techno-economic analysis model is proposed. The improved economic performance of the islanded green hydrogen system with the proposed operation strategy is verified based on the wind speed data set simulation generated by the Weibull distribution. Lastly, the sensitivity of the total return on investment to wind speed parameters was investigated, and an islanded green hydrogen system capacity allocation based on the proposed analysis model was conducted. The simulation result shows the total energy utilization increased from 62.0768% to 72.5419%, and the return on investment increased from 5.1303%/month to 5.9581%/month when the proposed control strategy is adopted. [ABSTRACT FROM AUTHOR]

South America is a region that stands out worldwide for its biodiversity of ecosystems, cultural heritage, and potential considering natural resources linked to renewable energies. In the global crisis due to climate change, South American countries have implemented actions to carry out a progressive energy transition from fossil energies to renewable energies and contribute to the planet's sustainability. In this context, South American countries are implementing green strategies and investment projects linked to wind farms to move towards achieving the sustainable development goals for the year 2030 of the UN agenda and achieving low-carbon economies for the year 2050. This article studies the advances in wind energy implementation in South America, highlighting progress and experiences in these issues through a review of the scientific literature considering the year 2023. The methodology applied in this article was carried out through the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the generation of scientific maps. As a result, this article presents the main developments, lessons learned/gaps, and future sustainable prospects on the road to 2050. According to the results, renewable wind energy infrastructure was applied in South America during the global climate change crisis era. Different levels of development in on-shore wind farms have been reached in each country. Also, a promising future exists for off-shore wind energy considering the highest potential. Finally, this article concludes that implementing emerging technologies like the production of green hydrogen and synthetic e-fuels looks like a synergetic clean energy solution combined with wind energy, which may transform the region into a world-class sustainable territory. [ABSTRACT FROM AUTHOR]

The research reported in this article focuses on a comparison of two different approaches to setting error bounds, or prediction intervals, on short-term forecasting of solar irradiation as well as solar and wind farm output. Short term in this instance relates to the time scales applicable in the Australian National Electricity Market (NEM), which operates on a five-minute basis throughout the year. The Australian Energy Market Operator (AEMO) has decided in recent years that, as well as point forecasts of energy, it is advantageous for planning purposes to have error bounds on those forecasts. We use quantile regression as one of the techniques to construct the bounds. This procedure is compared to a method of forecasting the conditional variance by use of either ARCH/GARCH or exponential smoothing, whichever is more appropriate for the specific application. The noise terms for these techniques must undergo a normalising transformation before their application. It seems that, for certain applications, quantile regression performs better, and the other technique for some other applications. [ABSTRACT FROM AUTHOR]

This paper explores an interesting approach to wind energy technology, focusing on a novel type of drag-driven vertical-axis wind turbines (VAWTs). Studied geometries employ rotor-shaped cross-sections, presenting a distinctive approach to harnessing wind energy efficiently. The rotor-shaped cross-section geometries are examined for their aerodynamic efficiency, showcasing the meticulous engineering behind this innovation. The drag-driven turbine shapes are analyzed for their ability to maximize energy extraction in a variety of wind conditions. A significant aspect of these turbines is their adaptability for diverse applications. This article discusses the feasibility and advantages of utilizing these VAWTs in fence configurations, offering an innovative integration of renewable energy generation with physical infrastructure. The scalability of the turbines is highlighted, enabling their deployment as a fence around residential properties or as separators between highway lanes and as energy-generating structures atop buildings. The scientific findings presented in this article contribute valuable insights into the technological advancements of rotor-shaped VAWTs and their potential impact on decentralized wind energy generation. The scalable and versatile nature of these turbines opens up new possibilities for sustainable energy solutions in both urban and residential settings, marking a significant step forward in the field of renewable energy research and technology. In particular, it was shown that among the proposed rotor geometries, the five-blade rotor was characterized by the highest efficiency and, working in a palisade configuration with a spacing of 10 mm to 20 mm, produced higher average values of the torque coefficient than the corresponding Savonius turbine. [ABSTRACT FROM AUTHOR]

With the increasing penetration of wind power into the grid, its intermittent and fluctuating characteristics pose a challenge to the frequency stability of grids. Energy storage systems (ESSs) are beginning to be used to assist wind farms (WFs) in providing frequency support due to their reliability and fast response performance. However, the current schemes mostly focused on the sole operation of either WT or ESSs for frequency support, lacking in economy and reliability. To address these issues, this work proposes a sliding mode consensus control (SMCC) method for ESSs to assist the frequency response of WTs. First, a coordination strategy for the WTs and ESS is proposed to reinforce the frequency support capability of a WT–ESS by determining the optimal capacity of ESSs. Meanwhile, a novel SMCC is proposed to achieve proportional power sharing among ESSs and increase the total power output of the entire WFs during wind energy shortages. Moreover, an event triggered communication mechanism is utilized to reduce the information exchanges among ESSs. Subsequently, a Lyapunov stability analysis is conducted to prove the stability and finite time consensus of the proposed method. Simulation case studies are conducted in IEEE 39-bus system to validate the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

Wind power has been developing rapidly as a key measure to mitigate human-driven global warming. The understanding of the development and impacts of wind farms on local climate and vegetation is of great importance for their rational use but is still limited. In this study, we combined remote sensing and on-site investigations to identify wind farm locations in Inner Mongolia and performed landscape pattern analyses using Fragstats. We explored the impacts of wind farms on land surface temperature (LST) and vegetation net primary productivity (NPP) between 1990 and 2020 by contrasting these metrics in wind farms with those in non-wind farm areas. The results showed that the area of wind farms increased rapidly from 1.2 km2 in 1990 to 10,755 km2 in 2020. Spatially, wind farms are mainly clustered in three aggregation areas in the center. Further, wind farms increased nighttime LST, with a mean value of 0.23 °C, but had minor impacts on the daytime LST. Moreover, wind farms caused a decline in NPP, especially over forest areas, with an average reduction of 12.37 GC/m². Given the impact of wind farms on LST and NPP, we suggest that the development of wind farms should fully consider their direct and potential impacts. This study provides scientific guidance on the spatial pattern of future wind farms.

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.

As the grid-connected capacity of a large number of wind farms continues to increase, their short-circuit current will have a great impact on protection. Therefore, accurate short-circuit current calculation is beneficial for correct protection action evaluation. Considering the issues related to the diversity of wind turbine types in wind farms and the inability to accurately simulate the failure transient process in previous studies, this research focuses on investigating the impact of control strategies during faults on the transient process of wind turbines. In this paper, the single-machine equivalent model of doubly-fed induction generators (DFIG) and permanent magnet synchronous machine (PMSM) is established by considering the types of wind turbines, and the calculation method of short-circuit current of hybrid wind farms is further proposed by using the classification aggregation equivalent effect method. The accuracy of short-circuit current calculation proposed in this article was verified on RTDS, and a power grid fault analysis method suitable for wind farm connection was proposed based on the characteristics of equivalent circuits of DFIG and PMSM during fault steady-state.

Abstract To achieve the transient overvoltage suppression of doubly fed induction generator (DFIG) terminals and the point of common coupling (PCC) in the wind farm (WF), an adaptive overvoltage control strategy based on data‐driven approach is proposed. Firstly, the PCC reactive power demand and DFIG overvoltage threshold are derived, respectively. Secondly, three regression models are developed through surface fitting to determine three variables: the maximum overvoltage of each DFIG, the PCC maximum overvoltage, and the reactive power demand of each DFIG. Subsequently, the K‐means clustering method is adopted to group all DFIGs, and then the reactive power requirement of the PCC is proportionally distributed to each group of DFIGs. Lastly, a coordinated control strategy of DFIGs and static var generator (SVG) is designed based on the values of three regression models, which can adaptively adjust the reactive power absorbed by each DFIG and SVG. In particular, the de‐load control is adopted to enlarge the maximum reactive power capacity of DFIG. The simulation results show that the proposed control strategy can timely and effectively restore the transient overvoltage of the wind farm to the normal range during the gird failure, ensuring the stable operation of the wind farm.

The paper describes a new method for determining the energy efficiency of the life cycles of wind farms by the aggregated data of energy costs. The rationale for the use of aggregated data to determine energy costs during the life cycle of the wind farms is given. The classification of wind turbines and wind farms elements by the parameters and technical characteristics of the elements with subsequent division into groups for which aggregated data of energy costs are determined is given. Within the framework of the method have been developed an algorithm for determining energy costs on the production of elements of wind turbines and wind farms and formulas for their calculation during the life cycle of wind farms. In order to test the method, energy cost was calculated during the life cycle for two wind farms with wind turbines that differ parameters and technical characteristics of the elements and their energy efficiency was determined. It has been shown that the use of a hydrogen storage unit as part of a wind turbine makes it possible to efficiently use energy during down periods and increase the efficiency of the installation by 25–30%. It is noteworthy that a wind-hydrogen farm allows not only to accumulate excess energy during lean periods, but also to save the resource of wind turbines. When the energy reserve in the hydrogen battery reaches close to full, part of the wind turbines of the wind-hydrogen farm can be automatically temporarily stopped in a given order. This technology can provide an extension of the warranty service life for each wind turbine. [ABSTRACT FROM AUTHOR]

A wind farm composed of doubly-fed wind turbines (DFWTs) based on the virtual synchronous generator (DFWTs-VSG) control strategy exacerbates the fatigue load on the main shaft of the DFWT-VSGs in the wind farm when responding to the frequency variation of the power system. The central controller of the wind farm can reduce the main shaft fatigue load of each DFWT-VSG by reasonably allocating the required damping coefficient of each DFWT-VSG while engaging in power system frequency response. In this study, a damping coefficient allocation method considering the main shaft fatigue load is proposed. First, a discretization equation that quantifies the relationship between the damping coefficient and its main shaft torque in DFWT-VSG is constructed. Then, based on this discretization equation, the minimization of the sum of main shaft torque fluctuation from all DFWT-VSGs is taken as the objective function in the central controller, and the constraints of the damping coefficient are set based on the support capacity of the wind farm and the operating state of each DFWT-VSG. Finally, the required damping coefficient of each DFWT-VSG is allocated in real-time based on the fmincon algorithm in the central controller. Simulation results verify the superiority of the proposed damping coefficient allocation method. [ABSTRACT FROM AUTHOR]

With global shifts towards sustainable energy models, the urgency to address rising fossil fuel prices, military conflicts, and climate change concerns has become evident. The article aims to identify the development of wind energy in Poland. This study introduces an integrated methodology for enhancing renewable energy capacities by selecting new construction sites for onshore wind farms across Poland. The proposed methodology utilises a hybrid model incorporating multiple criteria decision-making methods, such as the Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), alongside the semiautomated spatial analysis method using QGiS software (v. 3.32 Lima). The model considers economic, social, and environmental criteria and limitations, offering a comprehensive approach to the decision-making process. It was found that wind farms occupy 460.7 km2 in Poland, with a 250 m buffer around each turbine and a total power capacity of 5818 MW. The results show that an additional 7555.91 km2 of selected areas, 2.34% of the country's area, theoretically offer significant opportunities for wind energy development. The spatial analysis identifies potential sites with promising opportunities for domestic and international renewable energy investors. The study's findings contribute towards achieving national and EU renewable energy targets while offering a replicable framework for informed spatial planning decisions in other regions. [ABSTRACT FROM AUTHOR]

This study proposes an enhanced Kepler Optimization (EKO) algorithm, incorporating fractional-order components to develop a Proportional-Integral-First-Order Double Derivative (PI–(1+DD)) controller for frequency stability control in multi-area power systems with wind power integration. The fractional-order element facilitates efficient information and past experience sharing among participants, hence increasing the search efficiency of the EKO algorithm. Furthermore, a local escaping approach is included to improve the search process for avoiding local optimization. Applications were performed through comparisons with the 2020 IEEE Congress on Evolutionary Computation (CEC 2020) benchmark tests and applications in a two-area system, including thermal and wind power. In this regard, comparisons were implemented considering three different controllers of PI, PID, and PI–(1+DD) designs. The simulations show that the EKO algorithm demonstrates superior performance in optimizing load frequency control (LFC), significantly improving the stability of power systems with renewable energy systems (RES) integration. [ABSTRACT FROM AUTHOR]

For large-scale constructed wind farms, reducing wake loss and improving the overall output power are the main objectives for their optimal operation. Therefore, a wind farm power maximization method based on a multi-strategy improved sparrow search algorithm (MS-ISSA) is proposed in this paper. Integrating the wake propagation mechanism of wind turbines and the characteristics of the classic Jensen wake model, the Jensen-Gaussian wake model and wake superposition model were constructed to accurately calculate the wind farm wake distribution. The constructed Jensen-Gaussian wake model and wake superposition model can accurately describe the non-uniform distribution characteristics of wake velocity. The Sin chaotic model, Cauchy distribution, and hyperparameter adaptive adjustment strategy were used to improve the sparrow search algorithm (SSA), and the optimization ability, convergence speed, and stability of the SSA were improved. Accordingly, considering the maximum output power of the wind farm as the optimization target and axial induction factor as the optimization variable, a coordinated optimization model for wind turbines based on MS-ISSA was proposed to realize the coordinated optimal operation of wind turbines with reduced wake loss. Considering the Danish Horns Rev wind farm as the research object, the results of optimization using particle swarm optimization algorithm, whale optimization algorithm, basic sparrow search algorithm, and MS-ISSA were calculated and analyzed. The calculation results revealed that under different incoming wind conditions, the MS-ISSA exhibited better optimization results than the other optimization algorithms. [ABSTRACT FROM AUTHOR]

This article provides a detailed evaluation of cutting-edge artificial intelligence (AI) approaches and metaheuristic algorithms for optimizing wind turbine location inside wind farms. The growing need for renewable energy sources has fueled an increase in research towards efficient and sustainable wind farm designs. To address this challenge, various AI techniques, including genetic algorithms (GA), particle swarm optimization (PSO), simulated annealing, artificial neural networks (ANNs), convolutional neural networks (CNNs), and reinforcement learning, have been explored in combination with metaheuristic algorithms. The goal is to discover optimal sites for turbine placement based on a variety of parameters such as energy output, cost-effectiveness, environmental impact, and geographical restrictions. The paper examines the advantages and disadvantages of each strategy and highlights current breakthroughs in the area. This assessment adds to continuing efforts to optimize wind farm design and promote the use of clean and sustainable energy sources by offering significant insights into current advances. [ABSTRACT FROM AUTHOR]

In a wind farm, the grounding circuit is a complex electrical system made up of several interconnected groundings. Its role is very important for the protection of the electrical equipment during a direct lightning strike. To contribute to the optimization of the conception of this complex grounding system in his real configuration (wind turbine towers, grounding grids, and several hundred meters of buried conductor wires ...), we propose in this paper a realistic modeling. Realistic because, to model in all rigors using an electromagnetic field formalism a problem of propagation of electromagnetic transients in electrical device of large geometrical dimensions where the main environment (soil) is not homogeneous does not bring an appreciable advantage and consumes a very high calculation time. Also, the ideal for the engineer is to have a model that is easy to understand, accessible in its theory and obviously preserving the accuracy of the results and with very low computation time. In this work, to study the lightning surges in a wind farm, mainly to know the Ground Potential Rise and the distribution of the currents in the conductors, we use a modeling based on the transmission line equations. Our modeling is carried out by considering the effect of the frequency on the electrical characteristics (resistivity and dielectric permittivity) of the soil; the results we obtain are without loss of precision and with a very short calculation time. [ABSTRACT FROM AUTHOR]

Due to the intermittence of renewable sources, reliable customer service is not guaranteed unless hybrid-energy systems emerged with energy storage is employed together with an appropriate energy management system (EMS). Usually integrating different renewable energy sources can able to supply a reliable power to consumers. In this paper, an efficient EMS for a four-wire, 1000 kW Microgrid system is presented. The studied system consists of three wind farms and three solar plants with a central link to battery banks, an electrolyzer, and a fuel-cell system. A hybrid optimization algorithm is adapted to track the maximum power of PV arrays under variable weather and partial shading conditions. The tracking algorithm merges the benefits of the Whale Optimization and Perturb-and-Observe (P&O) technique. To track the maximum available power of wind plants, a P&O algorithm is established by calculating the proper duty cycle of a boost dc/dc converter that regulates the load current. A TS-Fuzzy-based controller for the inverter is applied to provide an acceptable power quality level associated with the battery bank, electrolyzer, and fuel cell. The suggested inverter controllers provide multi tasks such as reactive power compensator, voltage regulator under unbalanced loads, and active filter. The validity of the proposed system is verified via Extensive Hardware-in-Loop (HIL). [ABSTRACT FROM AUTHOR]

Over the years, researchers have proposed models for analyzing and predicting the low-frequency behavior of wind farm grounding systems. The lumped parameter modeling based on circuit theory presents an acceptable compromise between ease of implementation, consumption of computational resources, and accuracy of the results. This paper addresses the impact of mutual couplings between grounding elements in measuring the wind turbines grounding impedance using a clamp-on-ground meter. Furthermore, it proposes a new lumped parameter model for wind farm grounding to include the representation of such effects. In this study, computer simulation uses lumped parameter modeling and electromagnetic field modeling to evaluate turbine grounding impedance readings based on actual wind farm project data. The simulation results, assuming the electromagnetic model as a reference, have demonstrated that the proposed solution can estimate the low-frequency impedance of the measuring loop with an average error of 85.8% lower than existing state-of-the-art models. [ABSTRACT FROM AUTHOR]

Wind turbines have gained popularity as one of the efficient micro grid energy generation sources over the years. However, wind energy yield has been greatly impacted by the wind farm wake effect, especially when the size of the wind farm becomes very large. One way to address this challenge is through wind turbine arrangements and their scalability. Developing effective means of optimizing wind farm configurations is a major concern for energy communities. This has continuously led to increased power generation and a corresponding cost reduction. As such, this research article developed an optimal large-scale offshore wind turbine placement based on wind farm layout design. The system developed focuses on wind turbine placement in wind farm layouts that have a negative influence on the capital investment due to the increasing wake effect thereby reducing energy production. A multi-objective function consisting of wake effect and component costs within the wind farm is formulated. After-which a cuckoo search algorithm technique is employed in the wind farm model to optimize the optimization problem (minimizing the wake effect while improving power output and cost). Four test case scenarios were considered when implementing the wind model. The results obtained from the developed scheme were compared with those obtained when other optimization techniques were used, using power output and cost as performance metrics. The obtained power output and cost for the test case scenario in the ideal wind turbine position on the wind farm are 18288.3KW, 19680.1KW, 18879.9KW, 21105.8KW and 26.9, 28.7, 26.9, and 29.8KW, respectively. This shows that the result obtained from the developed scheme outperformed that obtained from PSO and that of WOA. [ABSTRACT FROM AUTHOR]

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.)

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

Despite the unique advantages a doubly fed induction generator (DFIG) offers to the grid‐integrated renewable energy systems, they have a limitation of being susceptible to grid fault as their stator windings are directly connected to the grid. The fault current limiters (FCLs) provide a sustainable solution by enhancing the fault ride‐through capability and thus it improve the transient performance of a DFIG. In this work, a multi‐inductor‐based H‐bridge fault current limiter (HBFCL) is proposed to augment the transient performance of a DFIG. The operational efficacy of the HBFCL is evaluated through the administration of both symmetrical and asymmetrical fault scenarios. The effectiveness of the HBFCL is further investigated by comparing the performance of the HBFCL with that of the bridge‐type series dynamic braking resistor (BSDBR). Both the graphical and numerical interpretations of the simulation result assert that the HBFCL improves the transient performance of a DFIG‐based wind farm and outweighs the performance of the BSDBR in all aspects. [ABSTRACT FROM AUTHOR]

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The DFIG-based wind farm faces sub-synchronous oscillation (SSO) when it is integrated with a series-compensated transmission system. The equivalent SSO damping is influenced by both wind speed and compensation level. However, it is hard for the wind farm to obtain a compensation level in time to predict the SSO risk. In this paper, an SSO risk prediction method for a DFIG wind farm is proposed based on the characteristics identified from noise-like signals. First, SSO-related parameters are analyzed. Then, the potential SSO frequency and damping are identified from signals at normal working points by integration using variational mode decomposition and Prony analysis. Finally, a fuzzy inference system is established to predict the SSO risk of a DFIG wind farm. The effectiveness of the proposed method is verified by simulation. The proposed prediction method can predict SSO risks caused by the variation in wind speed, while the transmission line parameters are undetectable for the wind farm. [ABSTRACT FROM AUTHOR]

This paper studies the stability of a real-world wind farm, Bison Wind Generation System (BWGS) in the state of North Dakota in the United States. BWGS uses an AC collector grid rated at 34.5 kV and a symmetrical bipolar high-voltage DC (HVDC) transmission grid rated at ±250 kV. The HVDC line transfers a total power of 0.5 GW, while both the HVDC rectifier and inverter substations use line-commuted converters (LCCs). The LCC-based rectifier adopts constant DC current control to regulate HVDC current, while the inverter operates in constant extinction angle control mode to maintain a fixed HVDC voltage. This paper proposes a frequency scan-based approach to obtain the d–q impedance model of (i) BWGS AC collector grids with Type 4 wind turbines that use permanent magnet synchronous generators (PMSGs) and two fully rated converters, and (ii) an LCC-HVDC system. The impedance frequency response of the BWGS is acquired by exciting the AC collector grid and LCC-HVDC with multi-sine voltage perturbations during its steady-state operation. The resulting voltage and current signals are subjected to a fast Fourier transform (FFT) to extract frequency components. By analyzing the impedance frequency response measurement of BWGS, a linear time–invariant (LTI) representation of its dynamics is obtained using the vector fitting (VF) technique. Finally, a Bode plot is applied, considering the impedance of the BWGS and grid to perform stability analyses. This study examines the influence of the short circuit ratio (SCR) of the grid and the phase lock loop (PLL) frequency bandwidth on the stability of the overall system. The findings provide valuable insights for the design and verification of an AC collector and LCC-based HVDC transmission systems. The findings suggest that the extraction of the impedance model of a real-world wind farm, achieved through frequency scanning and subsequent representation as an LTI system using VF, is regarded as a robust, suitable, and accurate methodology for investigating the dynamics, unstable operating conditions, and control interaction of the wind farm and LCC-HVDC system with the AC grid. [ABSTRACT FROM AUTHOR]

In large-scale wind farms, the voltage fluctuations caused by the uncertainty of wind speed at the turbine terminals pose a pressing challenge. This article presents a localized voltage control strategy tailored toward rapid adjustments in turbine terminal voltage in wind turbine generators. Based on relative voltage observation, this strategy achieves voltage coordination between the high and low ends of the transformer in wind turbine generators. Firstly, the overall structure of the wind farm and the characteristics of terminal voltage are analyzed. Secondly, the principles and feasibility of the relative voltage control strategy are examined. Finally, the effectiveness of the proposed control strategy is validated through simulation results from a specific wind farm. The results demonstrate its capability to achieve a fast and stable voltage dynamic response within the wind farm based on local information, thus mitigating the risk of voltage out of limit. [ABSTRACT FROM AUTHOR]

Compared to conventional energy sources, wind power is a clean energy source with high intermittence and uncertainty. As a system that converts wind energy into electricity, wind farms inevitably face severe reliability issues. In this paper, based on reliability theory, a new reliability modeling method for wind farms is proposed. Firstly, a belief reliability model for wind farms is constructed. Then, a power generation model based on two-directional rotor equivalent wind speed is established to represent the wind farm performance in the belief reliability model. Finally, several numerical studies are conducted to verify the power generation model under different wind speeds and directions, to demonstrate the belief reliability model with different levels of uncertainty, and to compare the belief reliability considering two-directional rotor equivalent wind speed with other methods. [ABSTRACT FROM AUTHOR]

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]

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