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Deploying distributed generators (DGs) powered by renewable energy poses a significant challenge for effective power system operation. Optimally scheduling DGs, especially photovoltaic (PV) systems and wind turbines (WTs), is critical because of the unpredictable nature of wind speed and solar radiation. These intermittencies have posed considerable challenges to power grids, including power oscillation, increased losses, and voltage instability. To overcome these challenges, the battery energy storage (BES) system supports the PV unit, while the biomass aids the WT unit, mitigating power fluctuations and boosting supply continuity. Therefore, the main innovation of this study is presenting an improved moth flame optimization algorithm (IMFO) to capture the optimal scheduling of multiple dispatchable and non-dispatchable DGs for mitigating energy loss in power grids, considering different dynamic load characteristics. The IMFO algorithm comprises a new update position expression based on a roulette wheel selection strategy as well as Gaussian barebones (GB) and quasi-opposite-based learning (QOBL) mechanisms to enhance exploitation capability, global convergence rate, and solution precision. The IMFO algorithm's success rate and effectiveness are evaluated using 23rd benchmark functions and compared with the basic MFO algorithm and other seven competitors using rigorous statistical analysis. The developed optimizer is then adopted to study the performance of the 69-bus and 118-bus distribution grids, considering deterministic and stochastic DG's optimal planning. The findings reflect the superiority of the developed algorithm against its rivals, emphasizing the influence of load types and varying generations in DG planning. Numerically, the optimal deployment of BES + PV and biomass + WT significantly maximizes the energy loss reduction percent to 68.3471 and 98.0449 for the 69-bus's commercial load type and to 54.833 and 52.0623 for the 118-bus's commercial load type, respectively, confirming the efficacy of the developed algorithm for maximizing the performance of distribution systems in diverse situations. [ABSTRACT FROM AUTHOR]

This research paper discusses a wind turbine system and its integration in remote locations using a hybrid power optimization approach and a hybrid storage system. Wind turbine systems' optimization controllers operate MPPT strategies efficiently, optimizing the system's overall performance. The proposed approach is HTb(P&O/FLC), which combines the P&O and FLC methods. This innovative hybrid MPPT strategy takes advantage of the two methods. It maximizes the wind power thus minimizing stress on the storage system. For storage, batteries are important in isolated renewable energy systems due the interminent renewable sources. Unfortunately, they have some drawbacks as lower energy density, and limited charge-discharge. Besides, supercapacitors (SCs) have advantages as quick charging and discharging and long cycle life and some drawbacks as lower energy density and specific voltage requirements. This paper proposes a hybrid storage system combining batteries and SCs. To manage the two storage technologies, an effective algorithm isproposed focusing on managing their storage systems. The findings indicate that the proposed strategy effectively maintains the state of charge (SOC) of both the batteries and SCs in the intended limits. Simulation under MATLAB and Real-time with RT-LAB were carried out to validate the proposed control approach for the wind turbine system. [ABSTRACT FROM AUTHOR]

Purpose: The reliable and continuous operation of wind turbines is of utmost importance, making it necessary to thoroughly understand their dynamic behavior under various operational and environmental conditions. Methods: To achieve this, a data acquisition system distributed throughout the tower height is designed. The system records data such as the acceleration, temperature, and relative humidity from the sensors, along with the rotor speed, wind speed, temperature, pitch angle, nacelle direction, and wind direction from the data acquisition system of the turbine. The acquired data is synchronized and processed by Autonomous and Continuous System Identification system based on the poly-reference Least Squares Complex Frequency method. The extensive dataset, gathered over a 7-month period, allows for the estimation of modal parameters of the wind turbine. The modal parameters are then correlated with the operational and environmental conditions that were recorded. The relationships between these conditions are thoroughly analyzed and explained. Additionally, the operational principles of the wind turbine are elucidated in detail. The correlations between the modal parameters and operational or environmental factors are presented and interpreted, shedding light on the complex interplay between wind turbine dynamics and external conditions. Conclusion: It can be said that changes in operational and environmental conditions affect the modal parameters of the wind turbine differently across various structural modes. Without considering these effects, structural health monitoring systems may produce false alarms. Failure to consider these effects in the development of structural health monitoring systems may lead to incorrect damage alarms. [ABSTRACT FROM AUTHOR]

In recent years, the utilization of wind turbines to harness wind power has experienced significant growth, driven by technological advancements and increasing emphasis on sustainability. Developing nations, including India, are strategically implementing wind power initiatives in regions characterized by high annual average wind speeds. However, due to the inherently unpredictable nature of meteorological variables, wind energy production lacks a predictable schedule. Consequently, the energy sector faces the challenge of accurately forecasting wind velocity to effectively manage and optimize wind electricity generation. This research aims at Andhra Pradesh, India, to see whether wind energy is feasible. This paper presents a multilayer perceptron-neural network (MLP-NN) forecasting model that utilizes machine learning. The model extracts features with the Harris Hawks Optimization-K-Nearest Neighbors (HHO-KNN) technique. By employing this strategy, open-access data gathering will be used to improve wind power predictions. As the result, the proposed model attains an accuracy of 97.0996%, a precision of 99.16731%, an F1 score of 97%, and a recall of 96%. According to the results, the Prakasam district has the most potential for producing wind energy. Even though the sites are small in comparison with larger-scale projects, they have the potential to produce considerable amounts of electricity. Strategic decisions in renewable energy planning are informed by this research, emphasizing the significance of sophisticated forecasting approaches for wind energy optimization. [ABSTRACT FROM AUTHOR]

Due to the growing demand for green energy, there is a shortage of land available for the location of wind farms. Therefore, the distances between turbines are being reduced, and the power of the turbines is being increased. This results in increased wake loss. The article describes a study of the impact of wind speed deficit and loss of wind turbine output due to wake loss on the decrease in energy efficiency of a wind farm. Two proposed wind farms, where the maximum number of turbines are located, were analyzed. The facilities were designed for implementation in Central Europe. The basic costs of construction and operation of the wind farms (WFs) were estimated. Based on the results of wind measurements and the performance characteristics of wind turbines, the productivity of the WFs was determined. The impact of removing individual turbines with the largest wake losses from the wind farm on the economic outcome of the project was studied. Evaluation criteria were proposed to quantify losses, which can serve as a benchmark for evaluating other wind farms. It was found that the higher the turbine's power rating, the faster the payback resulting from the wake losses of a single turbine. [ABSTRACT FROM AUTHOR]

Tip loss correction is necessary for all aerodynamic computations based on the blade-element momentum theory (BEMT) that plays an essential role in wind turbine design. Glauert tip-loss correction, which is most widely used, assumes the rotor is lightly loaded and thus lose some accuracy for modern wind turbines that are highly loaded. In the present work, a new g-function involving the rotor's thrust-coefficient is introduced, which connects the tip loss correction with the rotor's load condition and reduces the error of the tip loss correction. Computational tests are performed on two rotors that are distinct in size and tip shape. The new g-function results in good consistency between the computed forces and the reference data, exhibiting excellent role in improving the accuracy of the BEMT computations. In general, the new g-function combines good effectiveness and applicability, and thus has great potential for engineering applications. [ABSTRACT FROM AUTHOR]

Wind energy is a promising renewable source, necessitating effective monitoring of wind turbine (WT) conditions for reliable and cost-effective energy production, amidst environmental challenges. Condition monitoring of WTs employs traditional methods, signal processing, and emerging artificial intelligence (AI) approaches. AI-driven techniques excel in data-driven decision-making, addressing big data challenges in condition monitoring. This review paper presents a comprehensive overview of all streams of condition monitoring associated with WT, offering detailed insights into the related tasks. It also provides details on AI-based approaches and their application in executing various tasks within condition monitoring for WT. Finally, the study summarises the current trends, advantages, and disadvantages of AI-based techniques for real-world decision making in condition monitoring. This systematic review covers fundamentals to future developments in AI-driven approaches in condition monitoring for WT, serving as a valuable resource for readers and novice researchers in this field. [ABSTRACT FROM AUTHOR]

This research proposes a hybrid SCSO-QNN method for designing a FOPID controller in a three-area power system to regulate load frequency. The proposed hybrid approach combines quantum neural networks with sand cat swarm optimization, and it is commonly named as the SCSO-QNN method. The proposed method's primary goal is to overcome load disturbances and attain the intended output of the interconnected system. The SCSO generates controller parameters tuned by the FOPID controller, providing a significant improvement in performance, while the QNN is employed to forecast the optimal control signal of the converter. Reducing steady-state errors and improving system stability are areas where the FOPID performs better than a traditional integer-order PID controller. Moreover, SCSO-QNN optimization technique exhibits excellent convergence properties, resulting in improved control performance. The proposed method ensures system frequency control by controlling frequency deviation and power fluctuations in the connect line during load disturbances. The proposed method is executed on the MATLAB platform and contrasted with current techniques. The proposed technique outperforms all current techniques, including ABC-FOPID, Genetic Algorithm, and Artificial Bee Colony PID. The proposed method Area control error is 1.8%, which is less than other existing methods. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive review of the most recent papers and research trends in the fields of wind energy and artificial intelligence. Our study aims to guide future research by identifying the potential application and research areas of artificial intelligence and machine learning techniques in the wind energy sector and the knowledge gaps in this field. Artificial intelligence techniques offer significant benefits and advantages in many sub‐areas, such as increasing the efficiency of wind energy facilities, estimating energy production, optimizing operation and maintenance, providing security and control, data analysis, and management. Our research focuses on studies indexed in the Web of Science library on wind energy between 2000 and 2023 using sub‐branches of artificial intelligence techniques such as artificial neural networks, other machine learning methods, data mining, fuzzy logic, meta‐heuristics, and statistical methods. In this way, current methods and techniques in the literature are examined to produce more efficient, sustainable, and reliable wind energy, and the findings are discussed for future studies. This comprehensive evaluation is designed to be helpful to academics and specialists interested in acquiring a current and broad perspective on the types of uses of artificial intelligence in wind energy and seeking what research subjects are needed in this field. [ABSTRACT FROM AUTHOR]

The wind turbine fault feature has strong uncertainty due to various factors such as wind speed and equipment parameters. Based on the analysis of the vibration characteristics of wind turbines with unknown prior conditions, this paper conducts research on the enhancement method of fault features. First, analyzing the change in fault features caused by the change in operating conditions to lay the foundation for subsequent research; Second, for the uncertainty of wind turbine fault feature frequency, searching for fault features by constructing the energy in each spectral interval, then use signal noise optimal resonance to transfer noise energy to low frequency fault features to achieve the purpose of adaptive optimal resonance enhancement of fault feature; Finally, validation of the proposed method by simulation experiments and fault signals of actual wind turbine rolling bearings. The case shows that the proposed algorithm can effectively realize the uncertain fault feature of wind turbines search and enhancement. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In this paper, the fault detection and isolate problem is investigated for the pitch system of floating wind turbine. In the addressed system model, the system noises and measurement noises are correlated, and the measurement is affected by the missing phenomena. A Kalman filter is designed to handle the correlated noises and estimate the pitch angle, and a residual of the measurement of the pitch system is constructed to detection the faults. Then the fault isolation algorithm is presented based on a multi-attention mechanism one-dimensional convolutional neural network, which is employed to accurately isolate the faults. The simulation results show that the proposed method can significantly improve the accuracy of fault detection and isolation, which the fault isolation accuracy of the simulation results reaches 99.15%. [ABSTRACT FROM AUTHOR]

In this paper, an energy management strategy is developed in a renewable energy-based microgrid composed of a wind farm, a battery energy storage system, and an electolyzer unit. The main objective of energy management in the studied microgrid is to guarantee a stable supply of electrical energy to local consumers. In addition, it encompasses hydrogen gas production by using part of the available excess energy in the system, which has some economic benefits. Also, energy management can protect the battery bank from damage by preventing the possibility of it being overcharged. These objectives should be achieved by developing a robust and effective control technique for DC-DC converters that are connected to energy storage devices. For this purpose, an advanced control technique based on Model Predictive Control, which is recognized as a popular control technique for industrial and process applications, is developed. This technique has a fast dynamic response and good tracking features and is simple to implement. The simulation results prove the effectiveness of the proposed control strategy and control technique for energy management in the studied renewable energy-based microgrid. [ABSTRACT FROM AUTHOR]

The implementation of renewable energy strategies has been on the rise due to recent global initiatives on sustainable development. In this work, meteorological data obtained from geographically separated stations in Nigeria is utilized to assess the economic benefits of off‐grid renewable energy projects specifically WT/battery, PV/battery, and PV/WT/battery systems for upcoming projects in six different sites, with anticipated start dates in 2023, 2026, and 2029. By utilizing future forecasts of capital costs, this analysis aims to facilitate the decision‐making process regarding the optimal timing and placement of these installations. The WT/battery systems have a levelised cost of electricity (LCOE) that is 20–35 times more in comparison to both PV/WT/battery and PV/battery systems. In 2023, the LCOE for photovoltaic (PV)/battery systems varies from $0.16/kWh in Yola to $0.169/kWh in Port Harcourt. On the other hand, PV/WT/battery systems have the lowest LCOE, ranging from $0.261/kWh in Jos to $0.319/kWh in Port Harcourt. The analysis additionally demonstrated that in the event of project delays, PV/battery systems are projected to exhibit the lowest LCOE across all locations by 2029. This is attributed to the anticipated accelerated reduction in PV and battery expenses compared to the decline in costs associated with WTs. In the specified year, Yola exhibited the most favorable LCOE at $0.0748 per kilowatt‐hour, while Port Harcourt recorded the highest LCOE at $0.113 per kilowatt‐hour. It has been discovered that solar energy exhibits more cost‐effectiveness compared to wind energy, mostly attributable to its superior ability to match generation with load on an hourly and monthly basis, particularly in the presence of heavy loads. This study provides a comprehensive geographical overview that will assist policymakers in the strategic selection of cities in Nigeria for the deployment of off‐grid renewable energy (RE) systems. [ABSTRACT FROM AUTHOR]

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This study presents a techno-economic and environmental analysis of hybrid renewable energy systems to identify the optimal configuration for supplying the planned 850 MW renewable energy plant in Yanbu city, Saudi Arabia. Ten grid-connected system designs combining photovoltaic (PV), wind turbine (WT), and battery storage were simulated and optimized using the HOMER Grid software (1.10.2 pro edition). A site suitability analysis was conducted to evaluate potential locations based on climatic, topographic, and infrastructure-related factors. A sensitivity analysis considered variations in solar irradiation, wind speed, temperature, load demand, and economic parameters. The results showed that the PV-only system with an 850 MW capacity achieved the lowest net present cost (NPC) of USD 201 million and levelized cost of energy (LCOE) of 0.0344 USD/kWh, making it the most economically feasible option. However, a hybrid WT–PV configuration of 212.5 MW WT and 637.5 MW PV was also proposed to support local manufacturing. All proposed systems provided over a 91% renewable energy contribution while reducing CO2 emissions by 53% compared to grid supply only. Up to 1152 jobs are estimated to be created through renewable energy deployment in Yanbu city. [ABSTRACT FROM AUTHOR]

Traditional sources of energy are expensive, finite, and pollute the environment when used. Utilizing renewable energy resources is necessary to meet human societies' energy needs and promote sustainable development. This paper presents a hybrid approach to analyze the efficiency and economic assessment of pumped hydro-compressed air storage coupled with alkaline fuel cells. The proposed hybrid method is the Siberian tiger optimization (STO) and spiking deep residual networks (SDRN). Hence, it is named as STO–SDRN approach. By integrating these advanced optimization techniques, the aim is to optimize system performance and minimize total operation costs. Wind energy is globally available in various intensities, while alkaline fuel cells offer energy provision as long as fuel and oxidant are supplied. Integrating these energy systems can enhance overall system effectiveness. Wind turbines and alkaline fuel cells convert wind kinetic energy and fuel chemical energy into power, correspondingly. Excess power produced by wind turbines and alkaline fuel cells is stored in the pumped hydro-compressed electrical energy system. Through meticulous control and optimization facilitated by STO–SDRN, the study seeks to overcome the limitations of existing methodologies. Implemented in MATLAB, the proposed model is compared with existing approaches like the heap-based optimizer (HBO), cuckoo search algorithm (CSA), and the salp swarm algorithm (SSA). Comparative analysis demonstrates that the proposed system achieves significantly higher efficiency, with 94% efficiency compared to 85% for HBO, 76% for CSA, and 68% for SSA. Furthermore, the proposed system incurs lower costs, with $1.3 compared to $2.5 for HBO, $3.5 for CSA, and $4.1 for SSA, showcasing its cost-effectiveness and efficiency over existing approaches. This study contributes a rigorous high-level discussion and quantitative assessment of the proposed approach's performance, addressing key energy system challenges while offering tangible benefits for sustainable energy development. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method using a machine learning-based Adaptive Neuro-Fuzzy Inference System (ANFIS) to optimize Maximum Power Point Tracking (MPPT) in variable-speed Wind Turbines (WT). The ANFIS algorithm, blending artificial neural networks and fuzzy logic, addresses issues with traditional wind speed sensors, such as cost, imprecision, and susceptibility to adverse weather conditions. An initial offline-trained ANFIS is suggested to understand turbine power characteristics, and subsequently estimate varying wind speed, addressing strong nonlinearity due to WT aerodynamics and wind speed fluctuations. A second ANFIS efficiently tracks the maximum power point, overcoming limitations of linear controllers. Implemented in Matlab/Simulink for a 3.5 kW WT, the approach demonstrates effectiveness, precision, and faster response time in wind speed estimation and accurate MPPT compared to alternatives. A notable advantage is its independence from instantaneous wind speed measurement, providing a cost-effective solution for wind energy systems. [ABSTRACT FROM AUTHOR]

Wind farm power curves are particularly relevant in different applications. A better understanding of wind farm power curves under different atmospheric regimes is sought. In the present work, the atmospheric stability regime was identified by the vertical gradient of the potential temperature. Vertical profile of the wind velocity presented a clearly different pattern during the stable and unstable regimes. Analysis was performed considering 5 years of data from three wind farms, each corresponding to different mesoscale wind. Power curves are presented as a function of atmospheric stability. For each wind farm, we utilized wind velocity and temperature data measurements of a correspondent near the tower location. Clearly different patterns of wind farm power curves were observed as a function of the stability regime, with clear differences in the dependence of mesoscale wind regimes. For the same mean velocity, wind power production is greater for higher values of turbulence intensity TI. [ABSTRACT FROM AUTHOR]

To delve into the aerodynamic changes of wind turbines during pitching, this study employs the user-defined function (UDF) and dynamic mesh technique. The dynamic aerodynamic performance of a 3.3 MW turbine blade during variable pitch is investigated via numerical simulation. The results show that the optimal pitch angle increases with increasing incoming wind speed. At the initial and final stages of pitch change, the wind turbine experiences the influence of impact loads, leading to minor fluctuations in torque and thrust coefficients. Within the dynamic pitch change process, the pitch angle notably affects the aerodynamic performance of airfoils at blade x / c = 0~0.6 c cross sections. The primary stress area of the airfoil contracts, with the blade root experiencing comparatively less impact. In the constant-wind-speed pitching scenario, the low-speed region in the immediate wake of the wind turbine diminishes, resulting in a 10.7% enhancement in the average wind speed at 2.5D wake. [ABSTRACT FROM AUTHOR]

Dynamic stall is a serious phenomenon that restricts aeronautical vehicles' maneuverability. It happens on the blades as their angle of attack increase, especially on the blade of wind turbines. In this paper, two airfoils are investigated in actual conditions. To alleviate dynamic stall the geometries of the airfoils are modified by drooping and rounded leading-edge tip method and also combination of both. To study the effect of the modifications, the unsteady flow fields around the pitching airfoils, numerically simulated using URANS. Results illustrates, in addition to alleviate dynamic stall, the methods enhance aerodynamic characteristics by reducing drag force and preventing sudden jump of lift force, especially at the maximum angle of attack. Finally, a detailed investigation is conducted on the flow behavior around the airfoil to discover the supporting physics behind the improvements made by the present methods. Which revealed that these two passive methods, aim to prevent the formation of leading-edge vortex on the airfoil which finally delays the dynamic stall. [ABSTRACT FROM AUTHOR]

Considering the physical characteristics of wind turbine wing icing, icing synthesis rate, and icing type, we selected the icing type and surface roughness of ice-coated blades as sensitive parameters. The focus of our research was on the equivalent particle roughness height correction model, and we numerically analyzed the two icing processes (frost ice and clear ice) on wind turbine blade surfaces by combining FENSAP-ICE and FLUENT analysis tools. We predicted the ice type on blade surfaces using a multi-time step method and analyzed how variations in icing shape and ice surface roughness affect the aerodynamic performance of blades during frost ice formation or clear ice formation. Our results indicate that differences in blade surface roughness and heat flux lead to disparities in both ice formation rate and shape between frost ice and clear ice. Clear ice has a greater impact on aerodynamics compared to frost ice, while frost ice is significantly influenced by the roughness of its icy surface. These findings can serve as valuable references for wind power operators and manufacturers seeking solutions to issues related to blade surface icing under extremely cold conditions. [ABSTRACT FROM AUTHOR]

Blades are essential components of wind turbines. Reducing their fatigue loads during operation helps to extend their lifespan, but it is difficult to quickly and accurately calculate the fatigue loads of blades. To solve this problem, this paper innovatively designs a data-driven blade load modeling method based on a deep learning framework through mechanism analysis, feature selection, and model construction. In the mechanism analysis part, the generation mechanism of blade loads and the load theoretical calculation method based on material damage theory are analyzed, and four measurable operating state parameters related to blade loads are screened; in the feature extraction part, 15 characteristic indicators of each screened parameter are extracted in the time and frequency domain, and feature selection is completed through correlation analysis with blade loads to determine the input parameters of data-driven modeling; in the model construction part, a deep neural network based on feedforward and feedback propagation is designed to construct the nonlinear coupling relationship between the unit operating parameter characteristics and blade loads. The results show that the proposed method mines the wind turbine operating state characteristics highly correlated with the blade load, such as the standard deviation of wind speed. The model built using these characteristics has reasonable calculation and fitting capabilities for the blade load and shows a better fitting level for untrained out-of-sample data than the traditional scheme. Based on the mean absolute percentage error calculation, the modeling accuracy of the two blade loads can reach more than 90% and 80%, respectively, providing a good foundation for the subsequent optimization control to suppress the blade load. [ABSTRACT FROM AUTHOR]

The safe and reliable operation of the pitch system is essential for the stable and efficient operation of a wind turbine (WT). The pitch fault data collected by supervisory control and data acquisition systems (SCADA) often contain a wide variety of variables, leading to redundant features that interfere with the accuracy of final diagnosis results, making it difficult to meet requirements. Also, the problem of extracting only local features while ignoring global information is present in the feature extraction process using the deep Convolutional Neural Network (CNN) model. To address these issues, the global average correlation coefficient is proposed in this article to measure the correlation between multiple variables in SCADA data. By considering the correlation among multiple variables comprehensively, redundant features are effectively eliminated, enhancing the accuracy of fault diagnosis. Furthermore, a new local amplification fusion architecture network (LAFA-Net) based on multi-head attention (MHA) is introduced. An efficient local feature extraction module, designed to enhance the model's perception of detailed features while maintaining global context information, is first introduced. LAFA-Net integrates the advantages of CNN and MHA, efficiently extracting and fusing valuable features from filtered data for both local and global aspects. Experiments on real pitch fault data demonstrate that the global average correlation coefficient effectively screens out redundant features in the dataset that negatively impact fault diagnosis results, thereby improving diagnosis efficiency and accuracy. The LAFA-Net model, capable of accurately diagnosing multiple types of pitch faults, shows a superior classification effect and accuracy compared to several advanced models, along with a faster convergence speed. [ABSTRACT FROM AUTHOR]

A BP neural network incorporated regression forecast technique based upon fragment swarm optimization (PSO) is proposed to design the state of crucial components of wind turbine so regarding realize mistake identification and detection. Firstly, specification recognition is carried out on the collection and tracking information of the system, and parameters connected to fault detection are extracted. Then, the residual optimization issue is made use of to establish the forecast model of nonlinear state evaluation and semantic network combination, and the gearbox temperature level or generator bearing are input as criteria right into the semantic network combination model and single model specifically, and the precision of the design is mirrored by the examination index. Lastly, BP design and PSO-BP combined forecast model are developed respectively by using the actual operation data of wind ranch SCADA, and the mistake state is evaluated according to whether the anticipated residual exceeds the set threshold, so regarding keep an eye on the temperature level of wind turbine transmission and generator bearing. By contrasting the data videotaped prior to and after the failing and making the information prediction analysis, the speculative results show that the forecast model established in this paper is viable for the device element fault detection. [ABSTRACT FROM AUTHOR]

Simulating wind turbine blade icing quickly is important for wind farms to issue early warnings and effectively deal with the adverse effects of cold weather. However, current numerical simulation methods suffer from high computational costs and lack straightforward acceleration techniques for practical ice prediction. Here, we developed a fast and simple blade icing simulation method via an integrated physical simulation and statistical analysis method. This method consists of two steps: firstly, numerical simulation with CFD, and secondly, table look-up calculations. Over 10,000 sets of wind turbine blade icing simulations based on FENSAP-ICE and an NACA64-A17 wing were conducted to develop this method and analyze the influences of environmental factors on blade icing. The results show that ice thickness generally increases with an increase in wind speed, a decrease in temperature, and an increase in liquid water content (LWC), but there is a nonlinear relationship between them. For example, ice thickness has a linear relationship with the LWC within a certain range but hardly changes with a LWC beyond that range. The validation results show that the fast simulation method established in this paper has good consistency with the original numerical simulation method. It can greatly improve the computational efficiency of icing simulations while retaining the accuracy of numerical simulations. It takes less than 1 s to complete over 1000 sets of icing simulations, which offers potential for the fast prediction of wind turbine blade icing in the future. [ABSTRACT FROM AUTHOR]

The combined complexity of wind turbine systems and harsh operating conditions pose significant challenges to the accuracy of operational data in Supervisory Control and Data Acquisition (SCADA) systems. Improving the precision of data cleaning for high proportions of stacked abnormalities remains an urgent problem. This paper deeply analyzes the distribution characteristics of abnormal data and proposes a novel method for abnormal data cleaning based on a classification processing framework. Firstly, the first type of abnormal data is cleaned based on operational criteria; secondly, the quartile method is used to eliminate sparse abnormal data to obtain a clearer boundary line; on this basis, the Random Sample Consensus (RANSAC) algorithm is employed to eliminate stacked abnormal data; finally, the effectiveness of the proposed algorithm in cleaning abnormal data with a high proportion of stacked abnormalities is verified through case studies, and evaluation indicators are introduced through comparative experiments to quantitatively assess the cleaning effect. The research results indicate that the algorithm excels in cleaning effectiveness, efficiency, accuracy, and rationality of data deletion. The cleaning accuracy improvement is particularly significant when dealing with a high proportion of stacked anomaly data, thereby bringing significant value to wind power applications such as wind power prediction, condition assessment, and fault detection. [ABSTRACT FROM AUTHOR]

Nowadays, wind turbines are one of the most popular devices for producing clean and renewable electric energy. The rotor blades catch the wind's kinetic energy to produce rotational energy from the turbine and electric energy from the generator. In small-scale wind turbines, there are several methods to operate the blades to obtain the desired speed of rotation and power outputs. These methods include passive stall, active stall, and pitch control. Pitch control sets the angular position of the blades to face the wind to achieve a predefined relationship between turbine speed or power and wind velocity. Typically, conventional Proportional Integral (PI) controllers are used to set the angular position of the rotor blades or pitch angle. Nevertheless, the quality of speed or power regulation may vary substantially. This study introduces a rotor speed controller for a pitch-controlled small-scale wind turbine prototype based on fuzzy logic concepts. The basics of fuzzy systems required to implement this kind of controller are presented in detail to counteract the lack of such material in the technical literature. The knowledge base of the fuzzy speed controller is composed of Takagi–Sugeno–Kang (TSK) fuzzy inference rules that implement a dedicated PI controller for any desired interval of wind velocities. Each wind velocity interval is defined with a fuzzy set. Simulation experiments show that the TSK fuzzy PI speed controller can outperform the conventional PI controller in the speed and accuracy of response, stability, and robustness over the whole range of operation of the wind turbine prototype. [ABSTRACT FROM AUTHOR]

In this study, we investigate a yaw control strategy in a two-turbine wind farm with 3.5 MW turbines, aiming to optimize power management. The wind farm is equipped with a nacelle-mounted multi-plane LiDAR system for wind speed measurements. Using an analytical model and integrating LiDAR and SCADA data, we estimate wake effects and power output. Our results show a 2% power gain achieved through optimal yaw control over a year-long assessment. The wind predominantly blows from the southwest, perpendicular to the turbine alignment. The optimal yaw and power gain depend on wind conditions, with higher turbulence intensity and wind speed leading to reduced gains. The power gain follows a bell curve across the range of wind inflow angles, peaking at 1.7% with a corresponding optimal yaw of 17 degrees at an inflow angle of 12 degrees. Further experiments are recommended to refine the estimates and enhance the performance of wind farms through optimized yaw control strategies, ultimately contributing to the advancement of sustainable energy generation. [ABSTRACT FROM AUTHOR]

This paper proposes a new fault diagnosis model for wind power systems called residual convolution nested long short-term memory network with an attention mechanism (rlaNet). The method first preprocesses the SCADA data through feature engineering, uses the Hermite interpolation method to handle missing data, and uses the mutual information-based dimensionality reduction technique to improve data quality and eliminate redundant information. rlaNet combines residual networks and nested long short-term memory networks to replace traditional convolutional neural networks and standard long short-term memory architectures, thereby improving feature extraction and ensuring the abstractness and depth of the extracted features. In addition, the model emphasizes the weighted learning of spatiotemporal features in the input data, enhances the focus on key features, and improves training efficiency. Experimental results show that rlaNet achieves an accuracy of more than 90% in wind turbine fault diagnosis, showing good robustness. Furthermore, noise simulation experiments verify the model's resistance to interference, providing a reliable solution for wind turbine fault diagnosis under complex operating conditions. [ABSTRACT FROM AUTHOR]

Hydrogen has become an essential element in the pursuit of sustainable and clean energy solutions. Especially with the fast-paced advancement in demand, supply, and policy environment, its impact on hybrid renewable energy (HRE) management is becoming increasingly relevant. Efficient energy consumption, cost reduction, and enhanced user comfort are now critical factors in energy optimization. The production of green hydrogen, which is generated through water electrolysis using renewable energy sources (RES), has shown great potential as a sustainable energy solution. It offers several advantages, such as zero greenhouse gas emissions, high energy density, and versatile applications. This paper presents a detailed study on the power management and control of a hybrid renewable system (HRES) equipped with a diesel generator (DG) as a backup power source. The main objectives of the hybrid system are to satisfy the load power demand, ensure the most efficient use of the HRES, and keep the battery bank charged to prevent blackouts and extend the battery's life. To guarantee the system's reliability, the DG should be sized to meet the peak load demand when the RES generates less electricity than the load demand. This study explores the feasibility of modified versions of the load following and cycle charging control strategies to overcome the limitations of managing generation and storage systems' operations in different operating modes and to enhance the performance of an HRES with a DG that supplies electricity to a small and remote location. The proposed method not only maximizes the use of RES production but also enables multi-energy source management under different power generation and load demand scenarios. The study's outcomes demonstrate the feasibility of this proposed power dispatch strategy in a remote location environment. The paper includes a detailed discussion of overall control, mathematical models, energy storage in the battery model, and energy dispatching based on load following. To design and simulate the hybrid model system, MATLAB-SIMULINK is used, and the results are analyzed to identify the appropriate operation requirements, component selection, and energy management of the hybrid renewable energy system. [ABSTRACT FROM AUTHOR]

A comparison and evaluation of the AEP(Annual Energy Production) of a wind farm were conducted in this study with a feasibility study and using the actual operation data from the S wind farm on Jeju Island from January 2020 to December 2022. The free wind speed data were selected from the data measured from a nacelle anemometer, the correlation equation between wind speed and AEP was obtained, and the annual average wind speed for the past 20 years was predicted using the MCP method. As a result, comparing the AEP from the operation data with that estimated in the feasibility study, we found that the AEP was reduced by approximately 2.40% in 2020 and 12.14% in 2021, and increased by 6.76% in 2022. The wind speeds over the 20-year lifetimes of the wind turbines were obtained, and the AEP that could be generated at the S wind farm indicated that it could be used for operation. In the future, the S wind farm will operate at between 25% and 30% availability for the remaining 17 years of operation. If the availability falls below 25%, there will be a need to check the reasons for the deterioration of wind turbine performance and the frequency of failures. [ABSTRACT FROM AUTHOR]

This work addresses issues that appear from wind turbines equipped with frequency converters and presents a new fixed-speed wind turbine (FSWT) concept based on a split power transmission, along with a Mechatronic Control Model. The wind turbine rotor drives the first transmission shaft, while a servomotor with variable speed powers the second input. The differential gear transmission output is linked to the electric grid through an asynchronous generator. To optimize the power extracted from the wind energy while minimizing excessive dynamic loads on the wind turbine, a mechatronic control model of a 750 kW-FSWT is applied using a proportional and integral controller (PI). The paper suggests employing neural networks and evolutionary algorithms for determining appropriate PI gains. For collective servomotor control of a 750 kW-FSWT, a radial basis function (RBF) neural networks based on PI controller is proposed. To acquire an optimum dataset for RBF training, the particle swarm optimization (PSO) evolutionary algorithm is harnessed. The robustness and effectiveness of the proposed model and controller are verified and confirmed through simulation results. Hands-on experience is conducted using the 20-sim software package. [ABSTRACT FROM AUTHOR]

This article aims to present the results of tunnel tests and field tests of small-scale horizontal-axis wind turbines. The article proposes a new concept of turbine rotor adapted to improve efficiency at low wind speeds. The methodology for calculating the rotor and generator is shown. The turbine construction solution is presented briefly, along with the technology for manufacturing turbine components and assembly. An analysis of the obtained results is also conducted. [ABSTRACT FROM AUTHOR]

A wind turbine is susceptible to harsh environmental conditions during its design life, which induces excessive vibrational loads on the turbine. These vibrations also affect the quality of the power generation. To address these issues, vibrational response control devices are generally employed. Passive tuned mass dampers (TMD) are commonly utilized for structural control due to their simplicity, inherent stability, and low maintenance. Their operation is independent of any external stimuli. A TMD is only effective if its parameters are tuned appropriately. In this paper, an optimal methodology for designing Tuned Mass Dampers (TMDs) for onshore wind turbines is presented. The design of TMDs is formulated as a control optimization problem, and the methodology can effectively account for model uncertainties. The design methodology is experimentally validated using real-time hybrid simulation (RTHS), where the tower-nacelle assembly is tested physically. The effect of the TMD and the external forces is simulated using a computer model and applied to the physical test specimen using an electromagnetic shaker. The performance of the TMD is assessed for two different external loading scenarios: aerodynamic (wind) loading and earthquake. The experimental results obtained from RTHS demonstrate the effectiveness of the optimal TMD. Also, the RTHS framework presented in this paper provides an economical means for validating optimal TMD for wind turbines. [ABSTRACT FROM AUTHOR]

Flow control techniques used on wind turbines have been shown to significantly increase energy generation when compared to traditional wind turbines. Although various flow control methods have been introduced in the last two decades, the comparison between these methods is still the least conducted by researchers. Therefore, the present study aims to evaluate the performance of an airfoil utilizing both single and dual passive flow control methods, such as droop, flap, microcylinder, slot, and spoiler with optimal parameters. In this study, a numerical model was developed and applied with the same boundary conditions as those in the experiment. The results of the developed numerical simulation were then validated with experimental and other numerical studies. Mosaic mesh was utilized and the results were compared with conventional mesh types. Even though the mosaic mesh requires a lower number of computational elements, it demonstrated higher computational accuracy when compared to hexcore, polyhedra and tetrahedral type meshes. After obtaining an accurate numerical model, parametric studies were then conducted. The findings mainly highlighted that the airfoil with a microcylinder consistently generated higher performance than droop, flap, spoiler, slot and conventional airfoil. The mean relative improvement was about 2.6%. In an extensive study, eight combinations of flow controls were proposed and evaluated. The highest performances were achieved with the combination of microcylinder and flap, up to 27.9% and the combination of microcylinder and slot, reaching up to 50.2%, for low and high AOAs, respectively. [ABSTRACT FROM AUTHOR]

Nonstationary fault signals collected from wind turbine planetary gearboxes and bearings often exhibit close-spaced instantaneous frequencies (IFs), or even crossed IFs, bringing challenges for existing time-frequency analysis (TFA) methods. To address the issue, a data-driven TFA technique, termed CTNet is developed. The CTNet is a novel model that combines a fully convolutional auto-encoder network with the convolutional block attention module (CBAM). In the CTNet, the encoder layer is first designed to extract coarse features of the time-frequency representation (TFR) calculated by the general linear Chirplet transform (GLCT); second, the decoder layer is combined to restore and conserve details of the key time-frequency features; third, the skip connections are designed to accelerate training by linking extracted and reconstructed features; finally, the CBAM is introduced to adaptively explore channel and spatial relationships of the TFR, focusing more on close-spaced or crossed frequency features, and effectively reconstruct the TFR. The effectiveness of the CTNet is validated by numerical signals with close-spaced or crossed IFs, and real-world signals of wind turbine planetary gearbox and bearings. Comparison analysis with state-of-the-art TFA methods shows that the CTNet has high time-frequency resolution in characterizing nonstationary signals and a much better ability to detect wind turbine faults. ● A data-driven TFA technique is proposed for fault diagnosisof wind turbine planetary gearboxes and bearings. ● The proposed CTNet is a novel model that combines a convolutional network with the attention mechanism. ● The effectiveness of the CTNet is validated through the multiple sets of simulated and real-world signals. ● The CTNet can effectively characterize signals with close-spaced or crossed IFs and detect wind turbine faults. [ABSTRACT FROM AUTHOR]

With the fast-growing demand for wind energy, small wind turbines are becoming a viable and cost-effective source of clean and renewable electricity for residential, businesses, small industries, and remote applications. To harness the maximum power, wind turbines (WT) should be operated at full capacity; hence, it is crucial to design an effective maximum power point tracking (MPPT) control scheme. This paper presents an effective MPPT control algorithm based on fuzzy logic control (FLC) which tracks the slope of the mechanical power of the WT as a function of the rotational speed. The output of this MPPT generates the duty cycle for the DC–DC converter to allow the WT to extract the maximum power under a wide range of wind speed and loading conditions. The electrical generator used in the studied wind energy conversion system is the permanent magnet synchronous generator (PMSG). The proposed FLC-MPPT strategy is simple to implement and does not require any prior knowledge of the WT parameters. The proposed FLC-MPPT technique for WT is simulated using MATLAB/Simulink and validated experimentally on a laboratory setup using a dSPACE1104 board. In addition, a comparison between the proposed FLC-MPPT and other methods under significant wind speed fluctuations and variable load conditions is presented to demonstrate its effectiveness. The results show that the proposed MPPT has a fast convergence with a high tracking accuracy and exhibits low steady-state oscillations. [ABSTRACT FROM AUTHOR]

Vibration is inevitable in operation of mechanical equipment, but severe vibrations will cause serious harm to wind turbines (WTs). This paper takes the drivetrain of 8 MW WT as the object, established a multi-body electromechanical coupling model of WT and verified the accuracy of the established model through the WT drivetrain in the laboratory. Subsequently, based on this model, time-frequency domain analysis was conducted on the vibration response of the WT drivetrain under different control strategies of the generator, and the influence of the generator control strategy on the vibration of the drivetrain was studied. The result shows that the generator control strategy has a significant impact on the vibration response amplitude of the drivetrain, but has a relatively small impact on the frequency component. Meanwhile, the sliding mode controller designed in this paper has good anti-interference ability, which can make the system quickly reach dynamic stability, and effectively suppress the vibration of the main components in the drivetrain. Under the new controller's control, the transverse vibration displacement of some parts of the WT drivetrain can be reduced by up to nearly 60 %. This paper provides new ideas and new methods for the active vibration reduction control of WT drivetrain. [ABSTRACT FROM AUTHOR]

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

The rise of high-rise vertical farms in cities is helping to mitigate urban constraints on crop production, including land, transportation, and yield requirements. However, separate issues arise regarding energy consumption. The utilisation of wind energy resources in high-rise vertical farms is therefore on the agenda. In this study, we investigate the aerodynamic performance of an ellipsoidal tall building with large openings to determine, on the one hand, the threshold income wind that could impact human comfort, and on the other, the turbulence intensity at specific locations on the roof and façade where micro-wind turbines could operate. To this end, we calculate the wind pressure coefficient and turbulence intensity of two scale models tested within a wind tunnel facility and compare the results with a separate CFD simulation completed in the past. The results confirm that the wind turbines installed on the building façade at a height of at least z/h = 0.725 can operate properly when the inlet wind speed is greater than 7 m/s. Meanwhile, the wind regime on the roof is more stable, which could yield higher energy harvesting via wind turbines. Furthermore, we observe that the overall aerodynamic performance of the models tested best under wind flowing at angles of 45° and 60° with respect to their centreline, whereas the turbulence at the wind envelope compares to that of the free wind flow at roof height. [ABSTRACT FROM AUTHOR]