Search

Volatile electricity prices have raised concerns about the economic feasibility of wind projects in Finland. This study assesses the economic viability and optimal operational strategies for integrating wind-powered green hydrogen production systems. Utilizing modeling and optimization, this research evaluates various wind farms in Western Finland over electricity market scenarios from 2019 to 2022, with forecasts extending to 2030. Key economic metrics considered include internal rate of return, future value, net present value (NPV), and the levelized cost of hydrogen (LCOH). Results indicate that integration of hydrogen production with wind farms shows economic benefits over standalone wind projects, potentially reducing LCOH to €2.0/kgH 2 by 2030 in regular and low electricity price scenarios, and to as low as €0.6/kgH 2 in high-price scenarios. The wind farm with the highest capacity factor achieves 47% reductions in LCOH and 22% increases in NPV, underscoring the importance of strategic site selection and operational flexibility. • Volatility of electricity prices raises viability concerns for wind producers. • Examining the optimal operation and economic feasibility of wind-hydrogen systems. • Evaluated wind farms in Western Finland from 2019 to 2022, with a 2030 outlook. • Integration with hydrogen production enhances financial resilience and adaptability. • Strategic site selection and adaptive PEMEC operational plans found crucial. [ABSTRACT FROM AUTHOR]

Wind power forecasting plays a pivotal role in ensuring the dependable integration of wind energy into power grids. This research introduces a novel Bagged-CNN architecture tailored for short-term wind power forecasting. This architecture harnesses the combined strengths of the Bagging ensemble technique and Convolutional Neural Networks (CNN) to enhance prediction accuracy and robustness. Our approach demonstrates superior forecasting capabilities by exploiting the spatial and temporal features captured by CNNs. To validate our proposed architecture, we have conducted a comprehensive evaluation across three distinct wind farms, each characterized by unique geographical and climatic conditions. We systematically compared the performance of our Bagged-CNN model against several standalone machine-learning algorithms commonly employed in wind power forecasting. The hyperparameters of our Bagged-CNN model are meticulously optimized using the GRID search algorithm, which systematically explores a predefined parameter space. Notably, our predictions shed light on the substantial impact of training data period length on wind power prediction accuracy. Our research underscores the potential of coupling Bagging and CNN techniques for short-term wind power forecasting. The Bagged-CNN architecture, as proposed, serves as a robust framework for capturing intricate spatial and temporal patterns in wind power data, thereby facilitating more precise and dependable predictions. [ABSTRACT FROM AUTHOR]

Offshore solar is seen as a promising technology for renewable energy generation. It can be particularly valuable when co-located within offshore wind farms, as these forms of energy generation are complementary. However, the environmental impact of offshore solar is not fully understood yet, and obtaining a better understanding of the possible impact is essential before this technology is applied at a large scale. An important aspect which is still unclear is how offshore solar affects the local hydrodynamics in the marine environment. This article describes the hydrodynamic wake generated by an offshore solar array, arising from the interaction between the array and a tidal current. A computational fluid dynamic (CFD) modeling approach was used, which applies numerical large eddy simulations (LES) in OpenFOAM. The simulations are verified using the numerical model TUDFLOW3D. The study quantifies the wake dimensions and puts them in perspective with the array size, orientation, and tidal current magnitude. The investigation reveals that wake width depends on array size and array orientation. When the array is aligned with the current, wake width is relatively confined and does not depend on the array size. When the array is rotated, the wake width experiences exponential growth, becoming approximately 30% wider than the array width. Wake length is influenced by factors such as horizontal array dimensions and current magnitude. The gaps in between the floaters decrease this dependency. Similarly, the wake depth showed similar dependencies, except for the current magnitude, and only affected the upper meters of the water column. Beneath the array, flow shedding effects occur, affecting a larger part of the water column than the wake. Flow shedding depends on floater size, gaps, and orientation. [ABSTRACT FROM AUTHOR]

The impact of the topological formation of wind farms upon the lightning induced overvoltages injected into the grid was not covered earlier in literature. However, this topic is highly important to be investigated to allow the usage of the most reliable topology against lightning strikes. For such reason, the paper investigates this point with consideration of most damaging cases as lightning strikes to multi-blades. The testing used ATP software for four main topologies, radial, single-sided ring SSR, double sided ring and star topology. The features defining the similarities in response and the variance range between these topologies were recorded and analyzed. The multi-blade strikes gave an expected increase of 15% to 100% in the injected overvoltage to the grid for all topologies. The star topology showed the most reliable performance by allowing the least injected overvoltage to the grid. The percentage of reduction in the magnitude of the injected overvoltages reached 50.78%, 66.07% and 89.04% for SSR, DSR and star topology respectively with respect to radial topology. Recommendation was provided for design engineers to consider star topology during design phase in terms of more reliable lightning protection. [ABSTRACT FROM AUTHOR]

Aim: The aim of this study is to understand the development of windsports and the challenges faced by wind-based leisure activities. Methods: The socio-historical dynamics behind the development of wind leisure are analysed on the basis of specialised tourism blogs, as well as a field study carried out in the Northeast Region of Brazil, combining ethnographic observations and semi-structured interviews with 6 wind leisure enthusiasts. All the data is processed using the serious leisure perspective tools developed by Stebbins, and the body ecology tools developed by Andrieu et al. Results: The results show that wind sports provide a dual continuum. First of all, there are the practitioners, from Neophyte to Devotee Worker. Then there is the junction between land, water and air, where wind practices ensure a dual aesthetic. On the one hand, riders are united by a community of flow, emotion and sensation mediated by their relationship with the wind and the creation of play materials (waves, lagoons, trade winds). On the other hand, the tourist development of these practices serves a political narrative based on postcard aesthetics, enabling wind concessions to be traded for the development of wind farms. Conclusion: this original research calls for a more systematic exploration of wind practices and the hybridisation of natural elements, which seems to illustrate the crisis that modern sport and its institutions are going through today. [ABSTRACT FROM AUTHOR]

This study investigates the influence of atmospheric stability and ground effects on wind turbine wake recovery, challenging the conventional linear relationship between turbulence intensity and wake expansion coefficient. Through comprehensive field measurements and numerical simulations, we demonstrate that the linear wake expansion assumption is invalid at far-wake locations under high turbulence conditions, primarily due to ground effects. We propose a novel nonlinear wake expansion model that incorporates both atmospheric stability and ground effects by introducing a logarithmic relationship between the wake expansion coefficient and turbulence intensity. Validation results reveal the superior prediction accuracy of the proposed model compared to typical engineering wake models, with root mean square errors of wake wind speed predictions ranging from 0.04 to 0.063. The proposed model offers significant potential for optimizing wind farm layouts and enhancing overall wind energy production efficiency. [ABSTRACT FROM AUTHOR]

This paper reviews renewable energy integration with the electrical power grid through the use of advanced solutions at the device and system level, using smart operation with better utilisation of design margins and power flow optimisation with machine learning. This paper first highlights the significance of credible temperature measurements for devices with advanced power flow management, particularly the use of advanced fibre optic sensing technology. The potential to expand renewable energy generation capacity, particularly of existing wind farms, by exploiting thermal design margins is then explored. Dynamic and adaptive optimal power flow models are subsequently reviewed for optimisation of resource utilisation and minimisation of operational risks. This paper suggests that system-level automation of these processes could improve power capacity exploitation and network stability economically and environmentally. Further research is needed to achieve these goals. [ABSTRACT FROM AUTHOR]

In modern electric traction, direct current traction motors have been replaced with asynchronous motors with a short-circuited rotor. The justification is that asynchronous motors are more reliable, cheaper, and have smaller weights and dimensions, so they are more sustainable. In order to start and adjust the speed required in traction, these motors are powered from the contact line using a transformer and a static voltage and frequency converter. As a result, you can use green electricity produced with wind power plants or solar energy converted with photovoltaic panels, increasing sustainability because the consumption of traditional fuels is reduced. This paper presents various simulations emphasizing the negative effects of the distorting regime, with concrete results. The quality of the simulations carried out is increased by using a mathematical model, which uses the variable parameters of the motor dependent on the modulation of the current and the magnetic saturation. In modern 1500 kW electric locomotives, the traction motors are powered by static converters, which means an increase in losses when operating at nominal load on the motors by 38.7 kW and 217.8 kVAR compared to the sinusoidal three-phase power supply. Thus, the research carried out provides qualitatively and quantitatively correct simulations of the non-sinusoidal regime related to the asynchronous traction motor in order to increase the sustainability of this traction system. [ABSTRACT FROM AUTHOR]

The extensive and widespread incorporation of wind farms into transmission networks produces many challenges. One of the necessities is to improve power transmission capability to transport large levels of wind energy over long distances. The development of series compensators is a proven method of increasing the power transmission capacity of existing transmission lines and it is of great importance for the grid integration of large wind farms. At the same time, series compensation lines create sub-synchronous resonance (SSR) problems in turbo generators. As a result, electrical unsteadiness may occur in the sub-synchronous frequency components and there is the possibility of failure in the turbo generator tube of the shaft. This paper proposes a novel approach for analyzing SSR in wind farms connected with voltage source inverter (VSI) based photovoltaic cells. The Genetic Window Optimization (GWO) algorithm is used to analyze the multiple electrical parameters that cause SSR. These parameters are the series compensation range, power generation and wind velocity. Steady-state stability is carried out through eigenvalues and participation factors under the various operating conditions of wind farms. The dynamic stability analysis is also achieved through simulations of electromagnetic transients using PSCAD/EMTDC software for the first IEEE bench mark system. Simulation results of wind farms are interfaced with a prototype of real-time photovoltaic cell. A hardware model of solar cells is also presented. The high-level accuracy of GWO results is compared with existing techniques such as Particle Swarm Optimization (PSO) and FIREFLY algorithms. [ABSTRACT FROM AUTHOR]

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]

The unpredictability and instability of wind have hindered the development and utilization of wind power. To harness wind energy and ensure a secure and stable power grid after wind power integration, precise predictions of wind power generation are imperative. Here, we apply one-year data from a coastal wind farm in Zhejiang to train a Random Forest (RF) model for predicting wind power generation. The results indicate that the RF model (mean bias (MB) 1.33) outperforms traditional linear models (MB 87.70). An evaluation of prediction-measurement bias shows a strong agreement between the RF model's predictions and actual measurements (MB 1.33), especially when wind speeds exceed 5.7 m/s. While the Weather Research and Forecasting (WRF) model yields less accurate results (R2 0.65) compared to using measured wind velocity data (R2 0.97) for wind power prediction, it remains acceptable as it can capture and forecast peak generating power, meeting daily management requirements. Therefore, our machine learning-based approach offers practical guidance for reducing wind power generation uncertainties. Enhancing wind velocity forecasting through the WRF model could further improve the RF model's accuracy in predicting wind power generation. Our study also verified the future application of RF model in coastal areas of China. [ABSTRACT FROM AUTHOR]

This paper studies the reliability function of a weighted $k$ k -out-of- $n$ n system with multiple types of components. A weighted system with $M$ M different types of components is considered and two real-life scenarios for the operation of the system are defined. In the first one, it is assumed that the system operates if the accumulated weight of all active components is more than a predetermined threshold $k$ k , otherwise the system fails. In the second scenario, the system is assumed to operate if the total weight of the operating components of type $i$ i is more than or equal to the corresponding threshold ${k_i}$ k i , for at least $m$ m types of components, $m \le M$ m ≤ M. Our approaches in assessing the reliability function of the system lifetime rely on the notion of survival signature corresponding to multi-type weighted $k$ k -out-of- $n$ n system. The formulas for the proposed survival signature are presented and the corresponding computational algorithms are also given. To examine the theoretical results, the reliability function and other aging characteristics of a wind farm (system) consisting of three plants located in three different regions are assessed numerically and graphically. Finally, an allocation problem to determine the best choice for the distribution of each type in the weighted $k$ k -out-of- $n$ n systems is studied. [ABSTRACT FROM AUTHOR]

In the current model, the unclear and unreasonable method of revenue sharing among wind-solar-storage hybrid energy plants may a lso hinder the effective measurement of energy storage power station costs. This lack of clarity discourages energy storage from effectively collaborating with renewable energy stations for green power trading and spot trading. Therefore, this study proposes an optimal revenue sharing model of wind-solar-storage hybrid energy plant under medium and long-term green power trading market to facilitate the coordinated operation and equitable revenue allocation. Firstly, a method for decomposing transaction volume of green power is introduced by considering the uncertainty of spot market prices and physical delivery characteristics of green power trading. Then, a coordinated scheduling strategy of hybrid renewable energy plant is proposed to maximize revenues generated from both the green power and spot markets. Consequently, a cost-benefit contribution index system is developed to quantify the contribution of energy storage in the wind-solar-storage hybrid power plant. The revenue sharing model based on the minimum cost-remaining savings (MCRS) method can significantly increase overall revenue for renewable energy plants by reducing deviation penalties. It also enhances the operating revenue of energy storage power stations by considering the contributions of both energy storage and renewable energy plant in the green power market. The superiority of the proposed cooperation revenue sharing model for profitability enhancement of energy storage is validated through comparative case studies. [ABSTRACT FROM AUTHOR]

The European Union’s efforts to increase the share of renewable energy translate into a growing number of wind farms. Post-operation wind turbines become increasing waste. Recycling wind turbine blades is a challenge due to the materials used (composites) and high costs. The article examines the influence of recyclate from wind turbine blades (derived from mechanical recycling) on the image of topography and Lateral Force Microscopy (LFM) in atomic force microscopy (AFM). Upon adding the recycled material, an increase in surface roughness is observed despite its prior polishing. The Young’s modulus determined from force spectroscopy (AFM mode) indicated a small difference in values between the matrix and reinforcement areas. This proves the significant influence of the primary and secondary interphase on the properties of secondary composites, which is an area for further research. [ABSTRACT FROM AUTHOR]

It is very important to analyze and forecast energy production for investments in renewable energy resources. In this study, the energy production of wind and solar power plants, which are among the leading renewable energy sources, was estimated using deep learning. For a solar power plant, three different solar power plants with 1MW installed power were examined. Three-year energy production data of power plants were taken. These data were used with the deep learning method long short-term memory (LSTM) and seasonal autoregressive moving average (SARIMA). Results were obtained for each dataset; they were subjected to five different (MSE, RMSE, NMSE, MAE, and MAPE) error performance measurement systems. In the LSTM model, the highest accuracy rate was 81% and the lowest accuracy rate was 59%. In the SARIMA model, the highest accuracy rate was 66% and the lowest accuracy rate was 41%. As for wind energy, wind speeds in two different places were estimated. Wind speed data were taken from meteorological stations. Datasets were tested with MAPE, R2, and RMSE error performance measurement systems. LSTM, GRU, CNN-LSTM, CNN-RNN, LSTM-GRU, and CNN-GRU deep learning methods were used in this study. The CNN-GRU model achieved a maximum accuracy of 99.81% in wind energy forecasting. [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]

Nowadays, intermittent energy resources, such as wind turbines, and direct current (DC) transmission have been extensively integrated into power systems. This paper proposes an identifying method for weak lines of novel power systems with intermittent energy resources and DC lines integration, which aims to provide decision making for control strategies of novel power systems and prevent system blackouts. First, from the perspective of power system safety and stability, a series of risk indicators for the risk assessment of vulnerable lines is proposed. Then, lines in the system are tripped one by one. The calculation method for the proposed risk indicators is introduced. The impact of each line outage on system safety and stability can be fairly evaluated by these proposed risk indicators. On this basis, each risk assessment indicator is weighted to obtain a comprehensive risk assessment indicator, and then the risk caused by each line outage on the system can be quantified efficiently. Finally, the test system of a modified IEEE-39 bus system with wind farms and DC lines integration is used to verify the applicability of the proposed method, and the effectiveness of the proposed method is also demonstrated by comparing with existing methods. [ABSTRACT FROM AUTHOR]

With the integration of large-scale wind power clusters into the power system, wind farms play a crucial role in grid reactive power regulation. However, the range of its reactive power remains uncertain, posing challenges in formulating a viable program for regulating reactive power to ensure the safe and cost-effective operation of the power system. Based on this, this paper develops a bi-level reactive power optimization for wind clusters integrating the power grid while considering the reactive capability. Firstly, this paper carries out a refined analysis of the wind power clusters, taking into account the characteristics of different areas to estimate the exact value of the reactive power capability in wind power clusters. Secondly, a bi-level reactive power optimization model is established. The upper-layer optimization aims to minimize active losses and voltage deviation in power system operation, while the lower-layer optimization focuses on maximizing reactive power margin utilization in wind farms. To solve this bi-level optimization model, an improved artificial fish swarm algorithm (AFSA) is employed, which decouples real variables and integer variables to enhance the optimization ability of the algorithm. Finally, the effectiveness of our proposed optimization strategy and algorithm is validated through the simulation results. [ABSTRACT FROM AUTHOR]

With the rapid development of sensors and other devices, precise control for the generation of new energy, especially in the context of highly stochastic wind power generation, has been strongly supported. However, large-scale wind farm grid connection can cause the power system to enter a low inertia state, leading to frequency instability. Battery energy storage systems (BESSs) have the advantages of a fast response speed and high flexibility, and can be applied to wind farm systems to improve the frequency fluctuation problem in the process of grid connection. To address the frequency fluctuation problem caused by the parameter error of the fuzzy membership function in the fuzzy control of a doubly fed induction generator (DFIG) and a BESS, this paper proposes an improved Artificial Bee Colony (ABC) algorithm based on multi-source sensor data for optimizing the fuzzy controller to improve the frequency control ability of BESSs and DFIGs. A Gaussian wandering mechanism was introduced to improve the ABC algorithm and enhance the convergence speed of the algorithm, and the improved ABC algorithm was optimized for the selection of fuzzy control affiliation function parameters to improve the frequency response performance. The effectiveness of the proposed control strategy was verified on the MATLAB/Simulink simulation platform. After optimization using the proposed control strategy, the oscillation amplitude was reduced by 0.15 Hz, the precision was increased by 40%, and the steady-state frequency deviation was reduced by 26%. The results show that the method proposed in this paper provides a great improvement in the frequency stability of coordinated systems of wind farms and BESSs. [ABSTRACT FROM AUTHOR]

As offshore wind farms move into deeper waters, around 80 m, the high costs necessitate replacing bottom-fixed turbines with floating offshore wind turbines, which require mooring systems to maintain stability within design limits. Data from previous projects in China indicate that mooring systems can constitute about 20% of the total investment. Thus, reducing mooring system costs can significantly benefit the development of next-generation floating wind farms. This paper discusses the reliability analysis of mooring chains for floating wind turbines to optimize inspection plans and strategies, thereby saving on maintenance costs over their design lifetime. A case study on S-N curve based fatigue reliability analysis is conducted using both Monte Carlo Simulation and First Order Reliability Method (FORM), with consistent results from both methods. Additionally, three sensitivity analysis cases identify key parameters for the fatigue reliability analysis. [ABSTRACT FROM AUTHOR]

Growing global concern about how to dispose of wind turbine blades has sparked an investigation into more sustainable alternatives. This research delves into finding eco-friendly solutions for managing the waste generated by wind turbine blades, specifically by exploring options for repurposing them. To put this into action, this study has focused on a New Zealand wind farm with 196 wind turbine blades at the end of its life cycle. Through a thorough review of documents, 60 potential ways were identified to repurposing these blades. Options falling under the downcycling category were excluded, thus narrowing down to 45 promising solutions to repurpose. Futher 7 practically applied global solutions are identified. Implementing these repurposing solutions would divert 331 tonnes of waste material from landfills from the New Zealand Wind Farm. This study is significant because it highlights the potential for a circular and sustainable approach and provides an example of how wind turbine blade waste can be diverted from landfill. The findings of this study contribute to practical insights and support initiatives across New Zealand to encourage the adoption of wind turbine blade repurposing. This, in turn, will foster environmentally conscious waste management practices within the renewable energy sector and promote a more sustainable future. [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]

Wind turbine components' failure prognosis allows wind farm owners to apply predictive maintenance techniques to their fleets. Determining the health status of a turbine's component typically requires verifying many variables that should be monitored simultaneously. The scope of this study is the selection of the more relevant variables and the generation of a health status indicator (Failure Index) to be considered as a decision criterion in Operation and Maintenance activities. The proposed methodology is based on Gaussian Mixture Copula Models (GMCMs) combined with a smoothing method (Cubic spline smoothing) to define a component's health index based on the previous behavior and relationships between the considered variables. The GMCM allows for determining the component's status in a multivariate environment, providing the selected variables' joint probability and obtaining an easy-to-track univariate health status indicator. When the health of a component is degrading, anomalous behavior becomes apparent in certain Supervisory Control and Data Acquisition (SCADA) signals. By monitoring these SCADA signals using this indicator, the proposed anomaly detection method could capture the deviations from the healthy working state. The resulting indicator shows whether any failure is likely to occur in a wind turbine component and would aid in a preventive intervention scheduling. [ABSTRACT FROM AUTHOR]

Wind farm layout optimization (WFLO) is focused on utilizing algorithms to devise a more rational turbine layout, ultimately maximizing power generation efficiency. Traditionally, genetic algorithms have been frequently employed in WFLO due to the inherently discrete nature of the problem. However, in recent years, researchers have shifted towards enhancing continuous optimization algorithms and incorporating constraints to address WFLO challenges. This approach has shown remarkable promise, outperforming traditional genetic algorithms and gaining traction among researchers. To further elevate the performance of continuous optimization algorithms in the context of WFLO, we introduce a multi-local search-based SHADE, termed MS-SHADE. MS-SHADE is designed to fine-tune the trade-off between convergence speed and algorithmic diversity, reducing the likelihood of convergence stagnation in WFLO scenarios. To assess the effectiveness of MS-SHADE, we employed a more extensive and intricate wind condition model in our experiments. In a set of 16 problems, MS-SHADE's average utilization efficiency improved by 0.14% compared to the best algorithm, while the optimal utilization efficiency increased by 0.3%. The results unequivocally demonstrate that MS-SHADE surpasses state-of-the-art WFLO algorithms by a significant margin. [ABSTRACT FROM AUTHOR]

Current researches mainly focus on the participation of wind farms in primary frequency regulation, including overspeed load shedding control, propeller control and their coordinated control, etc. The frequency support is realized by reserving reserve capacity of wind turbines, but the influence of the dynamic characteristics of maximum power point tracking (MPPT) on the overall frequency regulation characteristics of wind turbines during frequency support is ignored. To this end, firstly, the frequency response model of the system is constructed, and the main factors that affect the frequency dynamic characteristics are revealed. Secondly, the principle of the grid-forming VSC with the function of self-inertial synchronization is introduced, the influence of the dynamic characteristics of MPPT on the frequency regulation characteristics of the wind turbine generator is clarified. Finally, based on the PSCAD/EMTDC electromagnetic transient simulation software platform, the influence of the dynamic characteristics of MPPT on the frequency regulation characteristics are verified by combining the inertia self-synchronous network control technology. The simulation results indicate that with the increase of setting of droop control, the decrease of output power of MPPT becomes large so as to weaken the frequency support capability. [ABSTRACT FROM AUTHOR]

Wake effect is a significant factor contributing to power loss in wind farms. Studies have shown that wake steering control can mitigate this power loss. Currently, wind farm wake control strategies primarily utilize fixed yaw control due to limitations in the accuracy and efficiency of dynamic wake models. However, fixed yaw control fails to fully exploit the power improvement potential of wake steering control. Therefore, in this study, we first propose a dynamic wake model for wind farms based on the physics-guided neural network (PGNN) approach. This model can predict the dynamic wake flow field within wind farms in real time using instantaneous inflow wind speed and turbine operational states. Then, by employing the PGNN dynamic wake model as the predictive model, a wind farm dynamic wake control strategy based on the model predictive control method is proposed. To quantify the advantages of the proposed control strategy, both fixed yaw control and dynamic yaw control are tested on a wind farm with a 3 × 2 layout. Results from large eddy simulations demonstrate that the proposed dynamic wake control strategy increases the power output of the wind farm by 11.51% compared to a 6.56% increase achieved with fixed yaw control. [ABSTRACT FROM AUTHOR]

Based on the costs to produce PTX products, a simple computational model is presented to determine the costs to ship and to reform them into hydrogen. In this model, wind farms are the sole energy source for PTX production. As an example, the costs of transporting power‐to‐X (PTX) products NH3, MeOH, liquid organic hydrogen carrier (LOHC), and Fischer‐Tropsch diesel from Chile (Haru Oni) and Morocco (Tarfaya) are determined. These costs are compared with those of hydrogen production at three domestic locations with very good to very moderate wind conditions in the order Dornum (North German coast), Senftenberg (Lausitz), and Ludwigshafen (Rhine Graben). In addition, a cost comparison for Fischer‐Tropsch diesel is made and land requirements for domestic production are determined. [ABSTRACT FROM AUTHOR]

With the integration of large-scale wind power into the power grid, the impact on system stability, especially the issue of low-frequency oscillations caused by small disturbances, is becoming increasingly prominent. Therefore, this paper proposes a damping quantitative analysis method for regional interconnected power systems incorporating large-scale wind power. Using the cross-entropy particle swarm optimization (CE-PSO) algorithm, the control parameters of wind turbines are optimized to suppress low-frequency oscillations in interconnected systems. The method begins with the state equation of the interconnected power system in two regions; it deduces the characteristic polynomial of the interconnected system, including wind farms, and takes into account the influence of wind power integration on the electrical connectivity of the system. Subsequently, the influence of wind turbine control parameters on the system is quantified, and a quantitative analysis model of the impact of wind power integration on system damping characteristics is constructed. Based on this, an optimization model for wind turbine control parameters is established, and the CE-PSO algorithm is utilized to achieve suppression of low-frequency oscillations in interconnected power grids with wind power integration. Finally, the accuracy and effectiveness of the proposed method are verified through a typical electromagnetic transient simulation model of the two-region interconnected power system. [ABSTRACT FROM AUTHOR]

The integration of wind turbines and wind farms into the power grid presents significant challenges, including the variable and uncertain nature of wind power, the need for efficient transmission and distribution of power, and the need for advanced control and monitoring systems. Recent advances in wind turbine and wind farm technology have greatly improved their ability to integrate into the power grid. This includes the use of variable speed generators and power electronics, such as inverters, which allow for more accurate control of the power output and better alignment with the grid's frequency. Additionally, advanced control algorithms, such as model predictive control and feedback linearization, have been developed to improve the stability and reliability of wind power systems. Communication systems have also been integrated into wind power systems to allow for real-time monitoring and control of the wind farm's performance. This improves the ability of the wind farm to provide ancillary services, such as frequency regulation and voltage control. In this work, the major interconnection requirements for the U.S. grid for the expected mass integration of wind farms were discussed focusing on sustainable and reliable control strategies. The paper explores various control schemes that are used for providing response for grid reliability and sustainability. The analysis also included the future steps that OEMs of wind turbines need to take and the regulatory framework that is needed for ancillary services to provide an equitable platform for all generating units – conventional and renewables. [ABSTRACT FROM AUTHOR]

Offshore wind power plants have become an important element of the European electrical grid. Studies of metocean site conditions (wind, sea state, currents, water levels) form a key input to the design of these large infrastructure projects. Such studies rely heavily on reanalysis datasets which provide decades-long model time series over large areas. In turn, these time series are used for assessing wind, water levels and wave conditions and are thereby key inputs to design activities such as calculations of fatigue loads and extreme loads and platform elevations. In this article, we address a known deficiency of one these reanalysis datasets, ERA5, namely that it underestimates strong wind speeds offshore. If left uncorrected, this poses a design risk (high and extreme wind, waves and water level conditions are underestimated). Firstly, comparisons are made against CFSR/CFSv2 reanalyses as well as high-quality wind-energy-specific in situ measurements from floating lidar systems. Then, the ERA5 surface drag formulation and its sea state dependency are analysed in detail, the conditions of the bias identified, and a correction method is suggested. The article concludes with proposing practical and simple ways to incorporate publicly available, high-quality wind energy measurement datasets in air–sea interaction studies alongside legacy measurements such as met buoys. [ABSTRACT FROM AUTHOR]

This research article presents a robust approach to optimizing the layout of pressure sensors around an airfoil. A genetic algorithm and a sequential quadratic programming algorithm are employed to derive a sensor layout best suited to represent the expected pressure distribution and, thus, the lift force. The fact that both optimization routines converge to almost identical sensor layouts suggests that an optimum exists and is reached. By comparing against a cosine-spaced sensor layout, it is demonstrated that the underlying pressure distribution can be captured more accurately with the presented layout optimization approach. Conversely, a 39 %–55 % reduction in the number of sensors compared to cosine spacing is achievable without loss in lift prediction accuracy. Given these benefits, an optimized sensor layout improves the data quality, reduces unnecessary equipment and saves cost in experimental setups. While the optimization routine is demonstrated based on the generic example of the IEA 15 MW reference wind turbine, it is suitable for a wide range of applications requiring pressure measurements around airfoils. [ABSTRACT FROM AUTHOR]

Mesoscale weather systems cause spatiotemporal variability in offshore wind power, and insight into their fluctuations can support grid operations. In this study, a 10-year model integration with the kilometre-scale atmospheric model COnsortium for Small-scale MOdelling – CLimate Mode (COSMO-CLM) provided a wind and potential power fluctuation analysis in the Kattegat, a midlatitude sea strait with a width of 130 km and an irregular coastline. The model agrees well with scatterometer data away from coasts and small islands, with a spatiotemporal root-mean square difference of 1.35 m s -1. A comparison of 10 min wind speed at about 100 m with lidar data for a 2-year period reveals very good performance, with a slight model overestimation of 0.08 m s -1 and a high value for the Perkins skill score (0.97). From periodograms made using the Welch's method, it was found that the wind speed variability on a sub-hourly timescale is higher in winter compared to summer. In contrast, the wind power varies more in summer when winds often drop below the rated power threshold. During winter, variability is largest in the northeastern part of the Kattegat due to a spatial spin-up of convective systems over the sea during the predominant southwesterly winds. Summer convective systems are found to develop over land, driving spatial variability in offshore winds during this season. On average over the 10 summers, the mesoscale wind speeds are up to 20 % larger than the synoptic background at 17:00 UTC with a clear diurnal cycle. The winter-averaged mesoscale wind component is up to 10 % larger, with negligible daily variation. Products with a lower resolution like ERA5 substantially underestimate this ratio between the mesoscale and synoptic wind speed. Moreover, taking into account mesoscale spatial variability is important for correctly representing temporal variability in power production. The root-mean square difference between two power output time series, one ignoring and one accounting for mesoscale spatial variability, is 14 % of the total power generation. [ABSTRACT FROM AUTHOR]

We propose a modification to the Fitch wind farm parameterization implemented in the Weather Research and Forecasting (WRF) model. This modification, derived from 1D momentum theory, employs a wind-speed-dependent induction factor to correct the local grid wind speed back to freestream before computing the turbine's power and thrust. While the original implementation underestimates power, the modified version shows good agreement with the power curve. We strongly recommend employing the modification for all studies that model at maximum one turbine per WRF grid cell. For simulations with more turbines per grid cell, additional inner-cell wake losses have to be considered. [ABSTRACT FROM AUTHOR]

The interaction of large wind farm clusters with the thermally stratified atmosphere has emerged as an important physical process that impacts the productivity of wind farms. Under stable conditions, this interaction triggers atmospheric gravity waves (AGWs) in the free atmosphere due to the vertical displacement of the atmospheric boundary layer (ABL) by the wind farm. AGWs induce horizontal pressure gradients within the ABL that alter the wind speed distribution within the farm, influencing both wind farm power generation and wake development. Additional factors, such as the growth of an internal boundary layer originating from the wind farm entrance and increased turbulence due to the wind turbines, further contribute to wake evolution. Recent studies have highlighted the considerable computational cost associated with simulating gravity wave effects within large-eddy simulations (LESs), as a significant portion of the free atmosphere must be resolved due to the large vertical spatial scales involved. Additionally, specialized boundary conditions are required to prevent wave reflections from contaminating the solution. In this study, we introduce a novel methodology to model the effects of AGWs without extending the LES computational domain into the free atmosphere. The proposed approach addresses the wave reflection problem inherently, eliminating the need for these specialized boundary conditions. We utilize the recently developed multi-scale coupled (MSC) model of to estimate the vertical ABL displacement triggered by the wind farm, and we apply the deformation to the domain of an LES that extends only to the inversion layer. The accuracy in predicting the AGW-induced pressure gradients is equivalent to the MSC model. The AGW modeling technique is verified for two distinct free-atmosphere stability conditions by comparing it to the traditional approach in which AGWs are fully resolved using a domain that extends several kilometers into the free atmosphere. The proposed approach accurately captures AGW effects on the row-averaged thrust and power distribution of wind farms while demanding 12.7 % of the computational resources needed for traditional methods. Moreover, when conventionally neutral boundary layers are studied, there is no longer a need to solve the potential temperature equation, as stability is neutral within the boundary layer. The developed approach is subsequently used to compare the global blockage and pressure disturbances obtained from the simulated cases against a solution characterized by zero boundary layer displacement, which represents the limiting case of very strong stratification above the boundary layer. This approximation, sometimes referred to as the "rigid lid", is compared against the full AGW solution using the MSC model. This is done for different values of inversion strength and free atmosphere lapse rate, evaluating the ability of the "rigid lid" to predict blockage, wake effects, and overall wind farm performance. [ABSTRACT FROM AUTHOR]

The paper presents some aspects regarding the identification of the wind potential, the location of the areas with high wind potential and the way of wind energy utilization. At the beginning, details are presented regarding the characteristics of wind energy and aggregates that ensure the conversion of primary wind energy into electricity. Ways of scientific research and evaluation of the energy of the wind and the factors on which the degree of conversion of wind energy into electricity depends are presented. While Romania possesses a significant theoretical wind potential, the practical exploitation of this potential is of greater interest for energy development forecasts. The practical potential is considerably lower than the theoretical potential due to land use constraints and market conditions. The results of the assessment of the possibilities of building wind farms for various areas of the country are presented. Romania's installed wind energy capacity, which is mainly derived from onshore wind farms, has been growing steadily over the past decade. Wind energy has become the country's second-largest renewable energy source, following hydropower. By focusing on these aspects, Romania can effectively evaluate and exploit its wind energy potential to enhance its renewable energy portfolio. [ABSTRACT FROM AUTHOR]

The wind power market is expanding quickly and the cost of wind power equipment is decreasing, making wind power technology a key player in the world's energy transition. Assessing wind potential and selecting the right wind turbine site are crucial parameters for developing a wind farm. Vietnam focuses on onshore and nearshore wind power projects due to its promising wind power potential and supportive policies. However, Vietnam has diverse climate characteristics and wind patterns. Therefore, initial basic research is necessary to evaluate the feasibility of investing in wind power projects. This study examines the technical feasibility of a typical wind power project in the Southeast region of Vietnam using Wind Atlas Analysis and Application Program (WAsP) software. The results indicate that the wind turbine's type and installation location significantly affect wind power plants' efficiency. The total power output of the wind power project after factoring with losses at a rate of 17%, is 304,149 MWh. [ABSTRACT FROM AUTHOR]

Nowadays, the wind power market is growing rapidly, while the cost of wind power equipment is decreasing, so Vietnam is currently building many wind power projects to ensure green energy development in its power system. However, some barriers in price mechanisms, and economic and technical conditions have also caused disadvantages in the process of developing wind power projects. This paper studies the SWOT-TOWS analysis to evaluate in greater detail the strengths, weaknesses, opportunities, and threat factors linked to it. Then the former compares the internal and external factors influencing the wind power industry on the way it exploits potential strategies. In general, Vietnam has satisfactory wind power development potential, and the wind power sector has been supported by a number of strong but unstable policies in the past. The rapid development of wind power capacity in recent times has also led to great challenges for investors and managers in actual operating conditions. Currently, investment costs for wind power plants are still quite high, Feed in Tariff (FIT) prices are not stable, transmission grid capacity is limited, and environmental treatment issues during construction and operation have not been fully considered. [ABSTRACT FROM AUTHOR]

This study addresses inherent challenges stemming from uncertainty associated with the integration of wind energy into the electricity market. A novel approach is proposed to leverage the capabilities of dynamic transformers to optimize the utilization of uncertain wind power output, thereby enhancing financial investment efficiency for wind power stakeholders. The flexible combination of wind turbines (WTB), transmission transformers (TTS), and Energy Storage Systems (ESS) can actively reserve or provision electricity. Electricity generation control is based on optimal analysis results using linear integer programming algorithms that consider temperature fluctuations, lifespan of transformers, and electricity market prices. Maximizing the dynamic transformer's efficiency as proposed and optimizing revenue and costs from the fluctuating wind power output significantly improves financial performance metrics when investing in wind farm projects. Financial figures highlighted in the paper emphasize notable benefits, particularly for wind farm expansion projects. The potential return on investment ratio is expected to increase up to 5.64 times compared to conventional wind farm investment scenarios, with an improvement to increase from 4.4% to 24.8%. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny 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 fully harness the potential of wind turbine systems and meet high power demands while maintaining top-notch power quality, wind farm managers run their systems 24 h a day/7 days a week. However, due to the system's large size and the complex interactions of its many components operating at high power, frequent critical failures occur. As a result, it has become increasingly important to implement predictive maintenance to ensure the continued performance of these systems. This paper introduces an innovative approach to developing a head-mounted fault display system that integrates predictive capabilities, including deep learning long short-term memory neural networks model integration, with anomaly explanations for efficient predictive maintenance tasks. Then, a 3D virtual model, created from sampled and recorded data coupled with the deep neural diagnoser model, is designed. To generate a transparent and understandable explanation of the anomaly, we propose a novel methodology to identify a possible subset of characteristic variables for accurately describing the behavior of a group of components. Depending on the presence and risk level of an anomaly, the parameter concerned is displayed in a piece of specific information. The system then provides human operators with quick, accurate insights into anomalies and their potential causes, enabling them to take appropriate action. By applying this methodology to a wind farm dataset provided by Energias De Portugal, we aim to support maintenance managers in making informed decisions about inspection, replacement, and repair tasks. [ABSTRACT FROM AUTHOR]

This study presents a novel solar updraft tower power plant (SUTPP) system, which has been designed to achieve the simultaneous utilization of solar and wind energy resources in desert regions, in response to the pressing demand for sustainable and efficient renewable energy solutions. The aim of this research was to develop an integrated system that is capable of harnessing and converting these abundant energy sources into electrical power, thereby enhancing the renewable energy portfolio in arid environments. The methodology of this study involved the design and construction of a prototype SUTPP, comprising a 53 m high tower, a 6170 m2 collector, five horizontal-axis wind turbines, and a thermal energy storage layer made up of pebbles and sand. The experimental setup was meticulously detailed, and experiments were conducted to collect data on the system's performance under various environmental conditions. Subsequently, three-dimensional numerical simulations were performed to explore the effects of ambient wind speed and solar radiation on the output power of the SUTPP. The results indicate that the output power of the system increases with the increase in ambient wind speed and solar radiation. The impact of solar irradiation on output power was observed to diminish as ambient wind speeds increased. Notably, as the inlet wind speed rose from 4 m/s to 12 m/s, the output power showed a substantial increase of 727%. The numerical simulations revealed that ambient wind speed has a more pronounced effect on power output compared to solar radiation. Furthermore, it was found that the influence of solar radiation is significant at low wind speeds, with its impact decreasing as wind speed increases. This research provides essential guidance for the design and engineering of highly efficient solar thermal energy utilization projects, representing a significant advancement in the field of renewable energy technology deployment in desert environments. [ABSTRACT FROM AUTHOR]

When employing stepwise inertial control (SIC), wind power generation can offer significant frequency support to the power system, concurrently mitigating energy shortages and suppressing secondary frequency drop. Nonetheless, further investigation is imperative for implementing stepped inertia control due to variations in frequency regulation capabilities and operational safety among diverse wind farm groups. Consequently, this paper advocates a multi-wind farm ladder timing SIC method designed to alleviate secondary drops in system frequency. Initially, the paper introduces the fundamental principles of stepped inertia control for a doubly-fed induction generator (DFIG) and deduces the relationship between support energy, wind power deficit at the exit, and SIC’s inertia power and support duration. Subsequently, the wind farm group is categorized into three groups using the classic wind speed zoning method, and the additional power reference for SIC is computed based on rotor safety requirements. A ladder time for the wind farm groups is introduced to address the potential exacerbation of secondary frequency drop issues resulting from the simultaneous activation of stepwise inertia control across all wind farms. The relationship between the initial kinetic energy loss of the wind farm group, the power deficit upon the exit of stepwise inertia control, and the ladder time is deduced. The NSGA-II multi-objective optimization algorithm is then employed to adjust the ladder time among multiple wind farm groups in the system. Finally, a modified IEEE-9 test system comprising three wind fields is constructed in MATLAB/Simulink to validate the proposed method. The results demonstrate the efficacy of the ladder time SIC coordinated strategy in providing robust frequency support, suppressing secondary frequency drops, streamlining control tasks, and enhancing the safety of wind power frequency regulation. [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.)

Investigating the aerodynamic performance and wake characteristics of wind farms under different levels of wake effects is crucial for optimizing wind farm layouts and improving power generation efficiency. The Large Eddy Simulation (LES)–actuator line model (ALM) method is widely used to predict the power generation efficiency of wind farms composed of multiple turbines. This study employs the LES-ALM method to numerically investigate the aerodynamic performance and wake characteristics of a single NREL 5 MW horizontal-axis wind turbine and three such turbines under different wake interaction conditions. For the single turbine case, the results obtained using the LES-ALM method were compared with the existing literature, showing good agreement and confirming its reliability for single turbine scenarios. For the three-turbine wake field problem, considering the aerodynamic performance differences under three cases, the results indicate that spacing has a minor impact on the power coefficient and thrust coefficient of the middle turbine but a significant impact on the downstream turbine. For staggered three-turbine arrangements, unilateral turbulent inflow to the downstream turbine causes significant fluctuations in thrust and torque, while bilateral turbulent inflow leads to more stable thrust and torque. The presence of two upstream turbines causes an acceleration effect at the inflow region of the downstream single turbine, significantly increasing its power coefficient. The findings of this study can provide methodological references for reducing wake effects and optimizing the layout of wind farms. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive study on optimizing wind farm efficiency by controlling wake effects using the WFSim dynamic simulation model. Focusing on five key factors—yaw wind turbine position, yaw angle, wind farm spacing, longitudinal wind turbine spacing, and yaw rate—we qualitatively analyze their individual and combined impact on the wind farm's wake behavior and mechanical load. Through a quantitative approach using the orthogonal test method, we assess each factor's influence on the farm's overall power output. The findings prioritize the following factors in terms of their effect on power output: yaw wind turbine position, yaw angle, wind farm spacing, longitudinal spacing, and yaw rate. Most significantly, this study identifies optimal working conditions for maximizing the wind farm's average power output. These conditions include a wind turbine longitudinal spacing of 7.0D, a wind farm spacing of 15.0D, a yaw angle of 30°, and a yaw rate of 0.0122 rad/s, with the first and second rows of turbines in a yaw state. Under these optimized conditions, the wind farm's average power output is enhanced to 35.19 MW, marking an increase of 2.86 MW compared to the farm's original configuration. Additionally, this paper offers an analysis of wake deflection under these optimal conditions, providing valuable insights for the design and management of more efficient wind farms. [ABSTRACT FROM AUTHOR]

Due to the substantial rise in wind power generation, the direct-drive permanent magnet synchronous generator has emerged as a leading technology for efficient variable speed operation, meeting grid demands effectively. This paper presents a comparative analysis of control strategies for permanent magnet synchronous generator based wind turbine using real variable wind speed data from a 2 MW of Tetouan wind farm in Morocco. The proposed approach is based on evaluating two primary control strategies: the adaptive fuzzy-proportional-integral controller and the conventional proportional-integral controller aimed at enhancing the wind turbine's output power. The simulation performed on MATLAB-Simulink indicates that pitch control mechanisms play a crucial role in optimizing power generation, also demonstrating its ability to achieve satisfactory performance. [ABSTRACT FROM AUTHOR]