Wind Turbine System Research Articles

Static and dynamic analysis of a new offshore wind turbine system supported with outriggers

This article presents an innovative structural system for offshore wind turbines supported on monopiles. The novelty of this system lies in its enhancement of the lateral stiffness of the tubular tower through the incorporation of an outrigger system, widely employed in high-rise buildings. This novel system is modeled as a Timoshenko beam-column element under both static and dynamic loading scenarios, incorporating geometric nonlinearity and accounting for the effect of soil-structure interaction. A comprehensive numerical analysis was conducted to evaluate the system’s behavior under various static and dynamic load cases, utilizing soil data from the Red Sea region of Egypt. A modal analysis is also incorporated to investigate the eigenmodes of the proposed system. This study offers valuable insights for designers and researchers, providing guidelines and diagrams for the implementation of the proposed system.

Optimal Sizing and Management of Hybrid Wind Turbine-Diesel-Battery System for Reverse Osmosis Seawater Desalination in NEOM City

Optimal sizing and management of hybrid wind turbine-diesel-battery system for reverse osmosis seawater desalination in NEOM city is the objective of the paper. Therefore, the paper explored the different factors to optimize and introduce a technoeconomic evaluation and energy management of a stand-alone wind turbine (WT) system, diesel generator (DG), and battery storage (BS). The suggested WT/DG/BS system is implemented to feed seawater reverse osmosis (SWRO) unit in NEOM. The necessitated desalinated water per day is 100 m3. To determine the optimal size of WT/DG/BS corresponding to the minimum cost of energy (COE) and net present cost, two different ratings of the SWRO units (SWRO-100 and SWRO-150), three control dispatch strategies (load following, cycle charging, and combined dispatch), and five types of batteries are considered. HOMER software is performed to simulate and optimize the WT/DG/BS. The optimization results indicated that the best battery storage is the Trojan SAGM battery. In this case, the COE ranged between $0.337/kWh and $0.564/kWh. The lowest COE of $0.377/kWh is obtained when using a combined control strategy and SWRO-100 unit, whereas the worst COE of $0.564/kWh is obtained when using load following control strategy and SWRO-150 unit. The best option of the WT/DG/BS system to supply the SWRO unit is option number 26. This system includes one wind turbine of 90 kW, DG of 25 kW, 47 Trojan SAGM batteries, a 23.8 kW converter, a SWRO-100 unit, and a combined control strategy. The net present cost and the initial cost are $950,725 and $221,495, respectively. The annual operating cost and annual consumed fuel are $56,409 and 36,396 L, respectively. Compared with using only a 25 kW diesel generator, the COE reduced from $0.373/kWh (using only DG/BS) to $0.337/kWh (using the best option) by around 9.65%. Under this condition, the values for the internal rate of return, return on investment, and simple payback are 11%, 7.8%, and 8.3 years, respectively.

Meeting the Energy Needs of Poultry Houses with Wind Turbine System under Tekirdağ Conditions and Its Environmental Effects

Türkiye has a favorable geographical location for renewable energy sources such as wind and solar. Accordingly, renewable energy sources can be used in the fast-growing bovine, ovine, and poultry farms that perform plant and animal production. Marmara Region is the leading production center of the Turkish poultry production industry. However, there are problems in maintaining environmental conditions in poultry houses. The fan-pad system plays an essential role in maintaining environmental conditions in the poultry houses. For this reason, the fan-pad system required for evaporative cooling in the poultry houses in Tekirdağ region was designed. The project was carried out in poultry houses with 6000 chickens and the dimension of the poultry house is 14 x 30 (420 m2). In the poultry house, a 16.8 m2 pad is needed for cooling, and a circulation pump with 0.2 kWh power is required for water circulation in the system. For mechanical ventilation, 11 units of aspirators with a diameter of 60 cm a power of 0.75 kWh, and a flow rate of 9500 m3h-1 will be sufficient for the system. For the ventilation and cooling system, an 8.45 kWh wind turbine system will supply the required energy. However, due to the variability of wind power, a turbine system larger than 8.45 kWh, such as 10 kWh, should be used. The cost of the cooling system planned with the wind turbine energy system reaches 35953 $. The use of wind turbine systems to generate electricity in agriculture will significantly reduce CO2 emissions to nature. In Tekirdağ conditions, 18250 kWh of electricity can be produced annually with a 10-kWh wind turbine system. With eco-friendly energy generation, it can play a major role in preventing climate change by avoiding the emission of 17155 kg of CO2 which is equivalent to the annual electricity production.

State Reliability of Wind Turbines Based on XGBoost–LSTM and Their Application in Northeast China

The use of renewable energy sources, such as wind power, has received more attention in China, and wind turbine system reliability has become more important. Based on existing research, this study proposes a state reliability prediction model for wind turbine systems based on XGBoost–LSTM. By considering the dynamic variability of the weight fused by the algorithm, under the irregular fluctuation of the same parameter with time in nonlinear systems, it reduces the algorithm defects in the prediction process. The improved algorithm is validated by arithmetic examples, and the results show that the root mean square error value (hereinafter abbreviated as RMSE) and the mean absolute error value (hereinafter abbreviated as MAPE) of the improved XGBoost–LSTM algorithm are decreased compared with those for the LSTM and XGBoost algorithms, among which the RMSE is reduced by 8.26% and 4.15% and the MAPE is reduced by 24.56% and 27.99%, respectively; its goodness-of-fit R2 value is closer to 1. This indicates that the algorithm proposed in this paper reduces the existing defects present in some current algorithms, and the prediction accuracy is effectively improved, which is of great value in improving the reliability of the system.

Research on fault detection and remote monitoring system of variable speed constant frequency wind turbine based on Internet of Things

In order to study the operating characteristics of variable speed constant frequency wind turbine under different working conditions and the monitoring system of wind turbine. In this paper, the simulation model of each component system of wind turbine is established by MATLAB/Simulink module, and the influence law of different wind speed and ground fault types on the output power of wind turbine is studied. The active power of wind turbines under different short-circuit fault types is compared. At the same time, in order to realize real-time monitoring of wind turbine speed and output power, an online monitoring system for wind turbine operation based on industrial Internet of Things is proposed, and the composition and operation characteristics of this remote monitoring system are given. The practical application shows that the on-line monitoring system can accurately and remotely monitor the running status of the wind turbine and avoid the unstable running of the wind turbine. The research conclusions of this paper can provide reference for the design and construction of wind turbines and the operation of connecting to the power grid.

Exploring Motion Stability of a Novel Semi-Submersible Platform for Offshore Wind Turbines

The stability of offshore floating wind turbine foundation platforms is a fundamental requirement for the efficiency and safety of wind power generation systems. This paper proposes a novel small-diameter float-type semi-submersible platform to improve system stability. To evaluate the superior motion stability of the proposed floating platform, a comprehensive frequency–domain response analysis and experimental study were conducted in comparison with the OC4-DeepCwind platform developed by the National Renewable Energy Laboratory (NREL). The respective comparison of the frequency–domain response analysis and the experimental results demonstrated that the proposed floating wind turbine platform shows better hydrodynamic characteristics and resonance avoidance capability. This not only reduces the Response Amplitude Operators (RAOs), but also enhances the system stability, namely, effectively avoiding the regions of concentrated wave loading and low-frequency ranges. Furthermore, the proposed small-diameter semi-submersible platform has the potential to reduce manufacturing costs, providing valuable insights for the manufacturing of offshore floating wind turbine systems.

A Comprehensive Review on Advanced Control Methods for Floating Offshore Wind Turbine Systems above the Rated Wind Speed

This paper presents a comprehensive review of advanced control methods specifically designed for floating offshore wind turbines (FOWTs) above the rated wind speed. Focusing on primary control objectives, including power regulation at rated values, platform pitch mitigation, and structural load reduction, this paper begins by outlining the requirements and challenges inherent in FOWT control systems. It delves into the fundamental aspects of the FOWT system control framework, thereby highlighting challenges, control objectives, and conventional methods derived from bottom-fixed wind turbines. Our review then categorizes advanced control methods above the rated wind speed into three distinct approaches: model-based control, data-driven model-based control, and data-driven model-free control. Each approach is examined in terms of its specific strengths and weaknesses in practical application. The insights provided in this review contribute to a deeper understanding of the dynamic landscape of control strategies for FOWTs, thus offering guidance for researchers and practitioners in the field.

Control of DFIG-based wind power generation system under unbalanced grid voltage conditions

Doubly Fed Induction Generator (DFIG)-based wind turbines have become increasingly popular in recent years due to their capacity to operate at varying speeds. Weaknesses in the DFIG system can arise from issues with the power grid due to the stator's direct connection and the excitation converter's power rating limitation. Under situations of unbalanced grid voltage, this study aims to explore the efficacy of the Direct Power Control (DPC) approach in managing wind turbine systems based on DFIG. Throughout the experimental investigation, we evaluated the system in standard and unbalanced grid voltage settings. MATLAB/SIMULINK simulations implement DPC, specifically tailored for a 9 MW DFIG-based wind farm. The results of these simulations show that the changed control method effectively reduces torque oscillations by making it possible to create active and reactive power references for the rotor-side converter. This eliminates the requirement for sequence component excitation, which was previously necessary. Furthermore, the research highlights the intrinsic link between control techniques and grid circumstances, showing this connectivity's crucial role in improving wind energy systems' stability and operational efficiency based on DFIG.

Preparation and characterization of phase change material emulsions and their applications in liquid cooling systems for wind turbines

In order to improve the effect of liquid cooling system for wind turbine, this study used ultrasonic preparation method, stearic acid as phase change material, sorbitan trioleate and sodium lauryl sulfate as compound surfactants successfully prepared stearic acid phase change material emulsion as a cooling medium for investigation. The characterization, thermophysical properties, and fluidity of stearic acid phase change material emulsions at varying concentrations were examined through differential scanning calorimetry, thermal constant analysis, and viscometry. To investigate the cooling effects, a stator axial liquid cooling model was developed for both water and a 14 % stearic acid phase change material emulsion. Findings revealed that the 14 % stearic acid phase change material emulsion exhibited a latent heat of phase change of 22.5 J/g, reaching a peak melting temperature of 68.7 °C. Additionally, the latent heat of solidification was measured at 21.08 J/g, accompanied by a peak solidification temperature of 60.3 °C. The maximum thermal conductivity reached 0.5801 W/(m·K), and the specific heat capacity peaked at 5219.8 J/(K·mol), marking a notable improvement of 1.26 times. Under an inlet temperature of 30 °C and a flow rate of 1 L/min, stearic acid phase change material emulsion effectively lowered the temperatures of the windings, stator, rotor, and casing by 19.2 °C, 23.6 °C, 19.2 °C, and 9.6 °C, respectively. stearic acid phase change material emulsion demonstrates excellent temperature control effectiveness at 3600 r/min and a low flow rate of 1 L/min, showcasing efficient energy utilization efficiency.

Coordinated power management strategy for reliable hybridization of multi-source systems using hybrid MPPT algorithms

This research discusses the solar and wind sourcesintegration in aremote location using hybrid power optimization approaches and a multi energy storage system with batteries and supercapacitors. The controllers in PV and wind turbine systems are used to efficiently operate maximum power point tracking (MPPT) algorithms, optimizing the overall system performance while minimizing stress on energy storage components. More specifically, on PV generator, the provided method integrating the Perturb & Observe (P&O) and Fuzzy Logic Control (FLC) methods. Meanwhile, for the wind turbine, the proposed approach combines the P&O and FLC methods. These hybrid MPPT strategies for photovoltaic (PV) and wind turbine aim to optimize its operation, taking advantage of the complementary features of the two methods. While the primary aim of these hybrid MPPT strategies is to optimize both PV and wind turbine, therefore minimizing stress on the storage system, they also aim to efficiently supply electricity to the load. For storage, in this isolated renewable energy system, batteries play a crucial role due to several specific benefits and reasons. Unfortunately, their energy density is still relatively lower compared to some other forms of energy storage. Moreover, they have a limited number of charge–discharge cycles before their capacity degrades significantly. Supercapacitors (SCs) provide significant advantages in certain applications, particularly those that need significant power density, quick charging and discharging, and long cycle life. However, their limitations, such as lower energy density and specific voltage requirements, make them most effective when combined with other storage technologies, as batteries. Furthermore, their advantages are enhanced, result a more dependable and cost-effective hybrid energy storage system (HESS). The paper introduces a novel algorithm for power management designed for an efficient control. Moreover, it focuses on managing storage systems to keep their state of charge (SOC) within defined range. The algorithm is simple and effective. Furthermore, it ensures the longevity of batteries and SCs while maximizing their performance. The results reveal that the suggested method successfully keeps the limits batteries and SCs state of charge (SOC). To show the significance of system design choices and the impact on the battery’s SOC, which is crucial for the longevity and overall performance of the energy storage components, a comparison in of two systems have been made. A classical system with one storage (PV/wind turbine/batteries) and the proposed system with HESS (PV/wind turbine system with batteries). The results show that the suggested scenario investigated with both wind and solar resources appears to be the optimum solution for areas where the two resources are both significant and complementary. The balance between the two resources seems to contribute to less stress on storage components, potentially leading to a longer lifespan. An economical study has been made, using the Homer Pro software, to show the feasibility of the proposed system in the studied area.

Hardware-in-the-loop simulation to validate the fractional-order neuro-fuzzy power control of variable-speed dual-rotor wind turbine systems

New and efficient command strategies and algorithms are needed in the field of wind turbine energy generation to ensure power quality according to international standards. However, conventional linear strategies are still used to command doubly-fed induction generators (DFIGs). Thus, inadequate power quality is obtained, which can cause network-level disturbances. This Hardware-in-the loop (HIL) study presents a new command scheme for a DFIG-based dual-rotor wind turbine (DRWT) system. The new command is a combination of a neuro-fuzzy algorithm and fractional-order control that overcomes the declining power quality of the DFIG-DRWT system. The new strategy is based on using pulse width modulation to generate command pulses for the rotor-side converter of DFIG. In the designed command system, the reference value of the active power is determined using maximum power point tracking to provide efficient power conversion performance under different operating conditions. First, MATLAB software is used to implement the fractional-order neuro-fuzzy (FONF) control technique, and the behavior of the proposed strategy is studied in comparison to that of the traditional direct power command (DPC) technique. A comparison is performed in terms of the ripple minimization ratio, durability, overshoot, steady-state error, reference tracking, and current quality. The results of the comparison show the high performance of the FONF technique. Second, the FONF technique is implemented in HIL test using the dSPACE 1104 Card, where two different forms of wind speed are used to study the characteristics of the proposed strategy and confirm its effectiveness and performance in HIL test compared to DPC technique. The experimental results obtained in the two tests confirm the efficiency, effectiveness, and high performance of the proposed FONF technique compared to DPC technique in improving the energy system characteristics, and the simulation obtains the same results.

Analysis of frequency characteristics of power system with wind farm-energy storage coordinated inertial control

With the increasing penetration level of wind power in the power system, the decoupling of wind turbine rotor speed and system frequency has led to the loss of the inertia of wind farms (WF), which can no longer be neglected. With the continuous development of energy storage (ES) technology, WF-ES coordinated inertial control has attracted attention as an effective means for wind turbines (WT) to participate in inertia response. How to build a WF-ES coordinated inertial control strategy and analyze system frequency characteristics has become a key research issue. In this paper, the frequency response model of a power system with WF-ES is established using the system frequency characteristic analysis method. The wind speed zones of the WF are taken into account, and different WF-ES control schemes are adopted under different wind speeds, aiming to ensure the active participation of WTs in the system frequency process across all wind speed conditions and effectively prevent frequency secondary drop. Finally, a power system frequency response simulation model is built in Matlab/Simulink to analyze the frequency characteristics of the power system with WF-ES coordinated inertial control and verify the feasibility of the proposed WF-ES coordinated inertial control strategy under various wind speeds.

Backstepping Control of an AC/DC/AC Converter in a Grid Connected Wind Power System

The contribution of this paper is to study a nonlinear control approach known as the backstepping control method to manage reactive and active power using new parametric values for the grid connected wind power generation system. Specifically, the research focuses on a system containing a wind turbine system, a permanent magnet synchronous generator (PMSG), and a grid connected via AC/DC/AC converter. A comprehensive mathematical model for both the wind turbine system, PMSG, and the grid connected via AC/DC/AC converter was developed for this purpose. The Simulation results of the grid-connected wind power generation system are presented and analyzed to show the performance of the nonlinear backstepping control method. The characteristics of the response to variations in generator speed and the power injected into the grid show the highlight of the performance of the backstepping method, under varying wind conditions.

Open-circuit fault diagnosis method of inverter in wind turbine yaw system based on GADF image coding

Considering the concealment of inverter open-circuit faults in yaw system of wind turbines, a method for diagnosing open-circuit fault of yaw system inverter is proposed based on Gramian Angular Difference Field (GADF) image coding in this article. Firstly, the current vector phase in the vicinity of relatively high yaw speed is collected as monitoring data. Secondly, considering the insufficient fault feature extraction ability of Convolutional Neural Network (CNN) structures with single dimensional data input during model training, the one-dimensional current vector phase is encoded into two-dimensional image by GADF image coding and an effective CNN fault diagnosis model is obtained with fewer fault samples. Finally, by comparing with actual monitoring data of wind turbine, the effectiveness of yaw system simulation model is verified. The proposed method can achieve identification and localization of open-circuit fault for single and two power devices, which is found to have better anti-noise interference effect and the results are not affected by yaw angle and mechanical torque. It is approved that the proposed method is effective with a simulation on the RT-LAB platform.

A Modified Reduced-Order Generalized Integrator–Frequency-Locked Loop-Based Sensorless Vector Control Scheme Including the Maximum Power Point Tracking Algorithm for Grid-Connected Squirrel-Cage Induction Generator Wind Turbine Systems

A Modified Reduced-Order Generalized Integrator–Frequency-Locked Loop-Based Sensorless Vector Control Scheme Including the Maximum Power Point Tracking Algorithm for Grid-Connected Squirrel-Cage Induction Generator Wind Turbine Systems

Dynamic programming strategy in optimal controller design for a wind turbine system

Dynamic programming strategy in optimal controller design for a wind turbine system

Optimal detection and classification of grid connected system using MSVM-FSO technique.

This paper, a hybrid method, is proposed for protecting the hybrid photovoltaic (PV) and wind turbine (WT) system. The proposed protecting method is the hybrid wrapper of both the multiple support vector machine (MSVM) and firebug swarm optimization (FSO), commonly named as MSVM-FSO method. The proposed technique is diagnosing the appropriate fault occurring in the hybrid system. The main purpose of the proposed system is to assure the system with lower complexity for the fault diagnosis and detection (FDD) for improving the power quality (PQ) of hybrid method. Here, the MSVM approach is used to detect the fault conditions of grid-tied system. To evaluate the events of voltages, fault and the currents of hybrid systems are analyzed at the feeder of buses. The FSO categorizes the types of fault, which is occurred in grid-connected system. By then, the proposed method's performance is done in the MATLAB software and it is contrasted with different existing methods. From this, the proposed method provides accuracy as 99.7% and efficiency as 98%, which is high compared to existing methods.

A sequential three-way risk sorting model with the cautionary principle under probabilistic linguistic environment

Risk assessment plays a crucial role in managing risks associated with systems, products and services. In practical risk assessment, the cautionary principle is widely adopted by individuals to prevent accidents, and the degree of caution varies with the risk level of potential hazardous activities (PHAs). Therefore, it is necessary to explore a sequential risk assessment model integrating the cautionary principle. To this end, we design an improved risk assessment model based on sequential three-way decision (STWD) model and cautionary principle. Firstly, a risk distance measure under probabilistic linguistic environment is proposed to effectively reflect varying degrees of caution when individuals analyzed PHAs of different risk levels. Then, a comprehensive risk weight optimization model is constructed on the consideration of the importance ranking and correlations of risk attributes. Subsequently, STWD and the as low as reasonably practicable (ALARP) principle are integrated to build the risk sorting model, and both the risk prioritization and classification of PHAs are obtained from the sequential risk sorting process. Finally, we verify the reliability of our proposed model through an example of a floating offshore wind turbine system. The findings validate the crucial role of cautionary principle in risk assessment.

Application of an active learning method for cumulative fatigue damage assessment of floating wind turbine mooring lines

Long-term cumulative fatigue damage of mooring lines is crucial for the design of floating wind turbine structures (FWTs). Although many efforts are carried out for the offshore floating platforms, there still needs to be an efficient approach for assessing the long-term fatigue damage of the floating wind turbine mooring lines due to the complex loading in FWTs. An active learning approach named AK-DA (Adaptive Kriging Damage Assessment) was recently proposed for the cumulative fatigue damage assessment of wind turbine structures. However, in the original work, the AK-DA approach was only tested on fatigue damage assessment of a 5MW wind turbine tower with monopile support structures. It is unclear whether it is applicable to other parts of the wind turbine system, especially considering the complex loading of FWTs. Therefore, in this work, the AK-DA approach is used to assess the cumulative fatigue damage of mooring lines. The Gaussian process regression (Kriging) model is used to predict the fatigue damage of the mooring line under different wind-wave cases. The cumulative fatigue damage of the mooring lines in the IEA 15MW semi-submersible wind turbines is assessed with the AK-DA approach. The numerical simulation results show that the AK-DA approach can efficiently and accurately estimate the cumulative fatigue damage of the mooring lines. Compared to the traditional simulation approach, the AK-DA approach can increase efficiency by more than 45 times, and the absolute error is less than 1%. This active learning approach could serve as a helpful tool for offshore mooring system designers, facilitating the cumulative fatigue damage assessment during the design process.

Anomaly detection of wind turbine based on norm-linear-ConvNeXt-TCN

The supervisory control and data acquisition (SCADA) system of wind turbines continuously collects a large amount of monitoring data during their operation. These data contain abundant information about the operating status of the turbine components. Utilizing this information makes it feasible to provide early warnings and predict the health status of the wind turbine. However, due to the strong coupling between the various components of the wind turbine, the data exhibits complex spatiotemporal relationships, multiple state parameters, strong non-linearity, and noise interference, which brings great difficulty to anomaly detection of the wind turbine. This paper proposes a new method for detecting abnormal operating conditions of wind turbines, based on a cleverly designed multi-layer linear residual module and the improved temporal convolutional network (TCN) with a new norm-linear-ConvNeXt architecture (NLC-TCN). Initially, the NLC-TCN deep learning reconstruction model is trained with historical data of normal behavior to extract the spatiotemporal features of state parameters under normal operational conditions. Subsequently, the condition score of the unit is determined by calculating the average normalized root mean square error between the reconstructed data and actual data. The streaming peaks-over-threshold real-time calculation of the anomaly warning threshold, based on extreme value theory, is then used for preliminary fault monitoring. Moreover, by shielding the fault alarm for low wind speeds and implementing a continuous delay perception mechanism, issues related to wind speed fluctuations and internal and external interference are addressed, enabling early warning for faulty units. Finally, the effectiveness and reliability of the proposed method are validated through comparative experiments using actual offshore wind farm SCADA data. The performance of the proposed method surpasses that of other compared methods. Additionally, the results of the proposed method were evaluated using the uniform manifold approximation and projection dimensionality reduction technique and kernel density estimation.