Wind Turbine Power Generation Research Articles

Spatio-temporal wind speed prediction based on Clayton Copula function with deep learning fusion

Accurate prediction of wind speed plays an important role in increasing the power generation of wind turbines and realizing efficient use of wind energy. However, due to the large number of wind turbines in the wind farm and the complex wake effects between the units, the coupling degree and spatial correlation of the wind speed of the wind turbines are increased. Accordingly, this paper proposes a wind speed prediction model based on spatio-temporal dependency analysis. The proposed model first uses long short-term memory(LSTM) neural network to predict the wind speed of each wind turbine to obtain its residuals, which can extract the time correlation of the wind-speed series; Then by applying the Clayton Copula function to analyze the correlation between the residual series and wind-speed series to get the joint-distribution function. The joint-distribution function can be used to calculate the prediction error of the wind speed and complete the wind speed prediction. The validity of the method in this work is verified using the measured wind-speed data of a wind farm. Experimental results show that the method effectively solves the problem of finding spatio-temporal dependency of wind speed and significantly improves the prediction accuracy of wind speed.

Adaptive Frequency Control Strategy for PMSG-Based Wind Power Plant Considering Releasable Reserve Power

Wind power plants (WPPs) are required to provide primary frequency regulation services when a high proportion of wind power is connected to the grid. The reserve power of wind turbine generators (WTGs), including rotor kinetic energy and deloading power, is determined by its current rotor speed and active power, thus, different frequency regulation levels of WTGs will be formed because of the variable wind speed and the wake effect in a WPP. This article proposes an adaptive frequency control strategy considering releasable reserve power that can enhance the frequency regulation capacity in the permanent magnet synchronous generator (PMSG)-based WPP. The inertial control gains of frequency controller for PMSGs are adjusted adaptively, resting with the releasable kinetic energy, which is a function of current rotor speed. Moreover, primary frequency control gains are adjusted adaptively, resting with the releasable deloading power, which is a function of the current active power, thus, a PMSG operating at a higher rotor speed and active power can release more rotor kinetic energy and deloading power to provide greater frequency regulation capability. The proposed adaptive frequency control strategy is tested using a PSCAD/EMTDC simulator. The simulation analysis indicates that the reserve power of PMSGs in WPP can be utilized effectively while ensuring their stable operation. Meanwhile, the frequency regulation capability would be further enhanced.

Soft switched DC-DC converter for hybrid power generation photo voltaic panels using fuzzy logic controller

Maximum Power Point Tracking (MPPT) with a Z source cascaded inverter based on Fuzzy Logic Control (FLC) is proposed in this research for a hybrid photo-voltaic (PV) and wind turbine (WT) system. In the absence of PV or WT power generation, the suggested system offers an uninterrupted power supply to the grid. PV functions as a current source and creates maximum power at maximum power point (MPP), supplying just the deficiency power. This FLC-integrated technique minimises the complexity of typical PID controllers while also improving overall performance. To reduce switching losses, current, and voltage switching stress, a traditional dc/dc boost converter with soft switching has been introduced. To reduce switching stress, a boost converter with zero voltage switching is used. This research focuses on a Zero Voltage Switching (ZVS) technique that uses fuzzy logic control to meet the PV system's MPPT and output voltage regulation requirements. All these PV and wind hybrid systems, a Z-Source network, and a five-level cascaded inverter are part of the proposed topology. The advantages of a Z-source inverter outweigh the disadvantages of a traditional voltage-source inverter. The required stepped output voltage is achieved by boosting the DC link voltage from the PV system using a shoot through state. The suggested system has been simulated with MATLAB/SIMULINK, and the closed loop control technique has been investigated and verified.

Characteristic Curve Fitting Method of Wind Speed and Wind Turbine Output Based on Abnormal Data Cleaning

The characteristic curve of wind speed and power reflects the output state of wind turbine, and its characteristics are helpful to the accurate prediction of wind power. With the improvement of wind turbine power generation technology and other engineering applications, the data collected by SCADA system contains a large number of outliers, which makes it difficult to accurately fit the wind speed power curve. Firstly, this paper analyzes the types and causes of outliers in the actual data of wind turbines. Then, an abnormal data cleaning method based on Tukey’s method considering the operation parameters of wind turbines is proposed to clean the data of 12 wind turbines. Finally, the new data are clustered separately in the wind speed range by K-means, and the wind speed power characteristic curve is fitted. Compared with the uncleaned data, this method can significantly improve the fitting accuracy of wind speed power characteristic curve.

A new approach to wind turbine power generation forecasting, using weather radar data based on Hidden Markov Model

A new approach to wind turbine power generation forecasting, using weather radar data based on Hidden Markov Model

Analytical Modeling for Prediction of Horizontal-Axis Wind Turbines Power Generation in Wind Farms Based on an Analytical Wake Model

The development of models that predict power production of wind farms (WFs) by considering the interacting wakes is important; because wakes of the turbines exert a significant influence on power production of turbines, and hence on the layout of wind turbines in WFs. Thus, the purpose of present study was to provide an innovative analytical method for the prediction of power generation of the WFs that have a flat terrain and are consisted of horizontal-axis wind turbines (HAWTs) with the same hub height. The methodology employed utilized an analytical Gaussian model of HAWT wake to develop an analytical model that calculates the effective wind velocity acting on the downstream HAWT(s), which is further used for reading its generated power from the turbine’s catalog; thus, providing the generated power of the WF as the output. The results of presented model were validated by the field measurements data of Horns Rev WF and also were compared to two analytical models for predicting the generated power. The results were compared with two numerical simulations of the literature, and the output data of three commercial software. Moreover, the error analysis revealed that the presented model mostly showed superior accuracy in predicting the field measurements data.

Investigation on the Performance of a Portable Power Generation System with a Low-Cost Vertical Axis Wind Turbine

The purpose of this project was to develop an innovative, small-scale, and portable vertical axis wind turbine for power generation. The wind turbine was simple in design and economical. Wind speeds ranging from 2.0ms-1 to 7.0ms-1 were tested on the proposed wind turbine. The experiments revealed that the turbine required a minimum wind speed of 3.9ms-1 to operate. According to the results, the proposed turbine achieved its maximum power output of 5.6W at a rotational speed of 65rpm when the wind speed was 7.0m/s. Additionally, voltage and current increased proportionately with increasing wind speed. The proposed system showed an average coefficient factor between 0.10 and 0.12. This portable wind turbine potentially revolutionizes industry while raising public awareness about clean and renewable energy.

Simulation and Experimental Study on the Ultrasonic Micro-Vibration De-Icing Method for Wind Turbine Blades

In cold and humid regions, ice accretion sometimes develops on the blades of wind turbines. Blade icing reduces the power generation of the wind turbine and affects the safe operation of the wind farm. For this paper, ultrasonic micro-vibration was researched as an effective de-icing method to remove ice from the wind turbine blade surface and improve the efficiency of wind turbine power generation. A blade segment with NACA0018 airfoil and the hollow structure at the leading edge was designed. The modal analysis of the blade was simulated by ANSYS, and the de-icing vibration mode was selected. Based on the simulation results, the blade segment sample with PZT patches was machined, and its natural frequencies were measured with an impedance analyzer. A return-flow icing wind tunnel system, and a device used to measure the adhesive strength of ice covering the airfoil blade, were designed and manufactured. The experiments on the adhesive strength of the ice were carried out under the excitation of the ultrasonic vibration. The experimental results show that the adhesive strength of the ice, which was generated under the dynamic flow field condition, was lower than the ice generated by water under the static flow field condition. Under the excitation of the ultrasonic vibration, the adhesive strength of the ice decreased. When the excitation frequency was 21.228 kHz, the adhesive strength was the lowest, which was 0.084 MPa. These research findings lay the theoretical and experimental foundations for researching in-depth the application of the ultrasonic de-icing technology to wind turbines.

An efficient hybrid reconfigurable wind gas turbine power management system using MPPT algorithm

To improve power management scheme in standalone mode using hybrid wind-gas turbine system. To reduce electrical fluctuations due to permanent magnet synchronous generator (PMSG) in wind turbine system. For power generation, the wind turbine system is a main source. When there is reduction in wind turbine power generation, then gas turbine gets activated immediately and produces the required electricity in effective manner. This reconfigurable power generation system is controlled by perturb and observe maximum power point tracking (P&O_MPPT) algorithm. The proposed wind-gas power management system algorithm and device performance was analysed and simulated using MATLAB R2021a for various wind turbine experiment parameters. The simulation result shows that the proposed model and algorithm effectively meets the load demand when the wind turbine speed falls below the minimum required value.

Optimal constant power control of wind turbine generators based on Takagi-Sugeno fuzzy model

Resource shortages and environmental pollution of the world are becoming more and more serious, wind Turbine generation as a kind of clean energy power generation has attracted much attention. The difficulty of wind power generation lies in the randomness of natural wind energy, which leads to the instability of the output power of Wind Turbine Generators (WTGs). The traditional variable pitch controller cannot realize global precision control of the wind power system. To solve the problem, this paper combines Takagi-Sugeno (T-S) fuzzy model with optimal control theory to design a robust controller. The unique novelty of the controller is that when the wind speed is higher than the rated, no matter how much the wind speed is, even if it is far from the balance point of the system, the controller can make the output power at a constant value, that is, increase the global stability of the wind power system. Specifically, the wind power system was divided into several linear subsystems, and the state feedback controller of each subsystem was designed through optimal control. Then, the subsystem controllers were merged by superposing membership functions, forming a controller of the entire system. Finally, wind turbine generators (WTGs) were modeled and simulated in Matlab/Simulink. The results show that the proposed controller kept the output power of WTGs constant under the global step wind of 13–25 m/s and the stochastic wind of 14–15 m/s. Our design enables the precision control at any wind speed, and improves the stability of power output of WTGs.

Layout and power generation estimation of 50 MW wind turbine in Dayingpo

In the feasibility study of wind power generation project, wind turbine selection, layout and power generation estimation of wind farm are the core contents. According to the analysis needs of Dayingpo project, this paper first introduces the geographical location and wind energy resources of Lan county Dayingpo 50MW wind power generation project. Based on the above work, the optional fans are selected. As a result, WTG1-2200 and​ WTG2-2500 are selected for layout. According to the actual terrain and wind energy resources of Dayingpo, a total of 22 fans are arranged, including 17 WTG 1-2200 and 5 WTG 2-2500. According to the above contents, the annual on grid power reduction coefficient of the wind farm, the annual on grid power of the wind farm, and the single unit power generation results of WTG1-2200 and WTG2-2500 are estimated in detail, respectively. Finally, according to the actual terrain and fan layout scheme, the suggestions for the next practical project are proposed.

Comparative models for multi-step ahead wind speed forecasting applied for expected wind turbine power output prediction

This paper investigates six of the most widely used wind speed forecasting models for a combination of statistical and physical methods for the purpose of Wind Turbine Power Generation (WTPG) prediction in Cameroon. Statistical method based on both single static and dynamic neural networks architectures and two hybrid neural networks architectures in comparison to ARIMA model are employed for multi-step ahead wind speed forecasting in two Datasets in Bapouh, Cameroon. The physical method is used to estimate 1 day ahead expected WTPG for each Dataset using the previous predicted wind speed from better forecasting models. The obtained results of multi-step ahead forecasting showed that the ARIMA and nonlinear autoregression with exogenous input neural network (NARXNN) models perform well the wind speed forecasting than other forecasting models in both Datasets. The better performances of ARIMA are achieved with one-step ahead and two-step ahead forecasting, while NARXNN is better with one-step ahead forecasting. But NARXNN models have more computational time than other models such as ARIMA models. Furthermore, the effectiveness of employed hybrid method for WTPG prediction is proven.

Maximizing the total power generation of faulty wind turbines via reduced power operation

The ever-increasing energy demand makes wind power more and more popular each passing day. However, due to long-term work in a complex environment, wind turbines are prone to failures. When a failure occurs, wind turbines are directly shut down for maintenance. However, this is not always needed since the turbines can continue operating by reducing their power output and hence improve the total power generation. In this study, continuous operation time is calculated by analyzing the status of each operating state parameter after a failure. Furthermore, a method of reducing power and increasing the continuous operation time of faulty wind turbines is proposed. The feasibility of this method is analyzed through three common wind turbine gearbox failures. Then, the relationships between continuous operation time and reduced power are studied. Finally, the total power generation and the maintenance cost for different power reduction rates are analyzed. The results show that the proposed method can extend the continuous operation time of wind turbines and increase the total generation after a gearbox failure.

The total social cost evaluation of two wind and PV energy development modes: A study on Henan of China

In Henan province of China, the power demand is vigorous, and the existing provincial generators are mostly high-carbon. Henan faces greater challenges in electricity revolution than other provinces. Currently, there are two development modes for generating low-carbon electricity in Henan: the “large-scale and long-distance transmission” (LSLDT) mode and the “local development and consumption” (LDC) mode. To make a better choice on development mode, following works have been done. Firstly, as there is still no macro evaluation on the self-sufficiency ratio by photovoltaic (PV) power in each region of Henan, a rooftop PV installation potential evaluation method based on the roof covering rate is proposed. And two development scenes for wind and PV power generation are set to fit the development trend of Henan in the near future. Secondly, as the load can only be stably supplied by the cooperation of wind turbine, PV equipment and other controllable generation, the real cost of wind turbine and PV power generation is never only the equipment investment. Different from the commonly used levelized cost of electricity method, a bi-level generation planning model based on the total social cost is established. Finally, generation planning schemes are made and the power supply cost per kWh electricity is evaluated under the two development scenes mentioned above. It is found that there is a significant cost advantage when wind and PV electricity are generated with the LDC mode in both scenes. What is more, the existence of reliability cost, wheeling cost and grid-loss cost makes the LSLDT mode difficult to cut down the wind and PV power supply cost. Wind and PV power generation of the LDC mode should be focused on in Henan.

Ultra Short-Term Wind Power Forecasting Based on Sparrow Search Algorithm Optimization Deep Extreme Learning Machine

Improving the accuracy of wind power forecasting is an important measure to deal with the uncertainty and volatility of wind power. Wind speed and wind direction are the most important factors affecting the power generation of wind turbines. In this paper, we propose a wind power forecasting method that combines the sparrow search algorithm (SSA) with the deep extreme learning machine (DELM). Based on the DELM model, the length of the time series’ influence on the performance of the neural network is validated through the comparison of the forecast error indexes, and the optimal time series length of the wind power is determined. The sparrow search algorithm is used to optimize its parameters to solve the problem of random changes in model input weights and thresholds. The proposed SSA-DELM model is validated using the measured data of a certain wind turbine, and various forecasting indexes are compared with several current wind power forecasting methods. The experimental results show that the proposed model has better performance in ultra-short-term wind power forecasting, and its coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE) are 0.927, 69.803, and 115.446, respectively.

Diagonal inflow effect on the wake characteristics of a horizontal axis wind turbine with Gaussian model and field measurements

This paper investigates the diagonal inflow effect on the wake characteristics of a HAWT (Horizontal Axis Wind Turbine) in a wind farm. In this study, a HAWT with the generator capacity of 30 kW and the rotor diameter of 10.0 m is used. Firstly, the effect of pitch angle on the power and thrust performances of wind turbine are investigated through field experiment. On this basis, the wake characteristics of the HAWT are examined with different wind directions and pitch angles. The velocity field is measured by the ultrasonic anemometers, the three-cup type anemometer and the wind vane. Finally, the annual power generation of the downstream wind turbine is predicted by different Gaussian models. As a result, the maximum power coefficients Cp are 0.31, 0.33 and 0.27, corresponding to the tip speed ratios λ = 7.5, 7.4 and 6.8 and the pitch angles β = 0°, 2° and 4°, respectively. The standard deviations of wind direction are divided into four regions between σθ = 5° and 20°. The maximum deficit values of the wake velocity decreases from 0.27 to 0.16, and the full wake widths at half maximum Dr increases from 1.35 to 1.71 with σθ increasing from 5°∼9°–14°∼20°. The amount of annual energy production predicted by the wind tunnel experiment model is about 51 % of the field experiment model. This study provides a better understanding of the wake characteristics and a guiding significance for the layout optimization of wind farms.

Control Algorithm for Parallel Connected Offshore Wind Turbine Generators

A control algorithm for Parallel Connected Offshore Wind Turbines with permanent magnet synchronous Generators (PCOWTG) is presented in this paper. The algorithm estimates the optimal collective speed of turbines based on the estimated mechanical power of wind turbines without direct measurement of wind speed. In the proposed topology of the wind farm, direct-drive Wind Turbine Generators (WTG) is connected to variable low-frequency AC Collection Grids (ACCG) without the use of individual power converters. The ACCG is connected to a variable low-frequency offshore AC transmission grid using a step-up transformer. In order to achieve optimum wind power extraction, the collective speed of the WTGs is controlled by a single onshore Back to Back converter (B2B). The voltage control system of the B2B converter adjusts voltage by keeping a constant Volt/Hz ratio, ensuring constant magnetic flux of electromagnetic devices regardless of changing system frequency. With the use of PI pitch compensators, wind power extraction for each wind turbine is limited within rated WTG power limits. Lack of load damping in offshore wind parks can result in oscillatory instability of PCOWTG. In this paper, damping torque is increased using P pitch controllers at each WTG that work in parallel with PI pitch compensators.

The value of airborne wind energy to the electricity system

AbstractAirborne wind energy (AWE) is a new power generation technology that harvests wind energy at high altitudes using tethered wings. The potentially higher energy yield, combined with expected lower costs compared to traditional wind turbines (WTs), motivates interest in further developing this technology. However, commercial systems are currently unavailable to provide more detailed information on costs and power generation. This study estimates the economic value of AWE in the future electricity system, and by that indicates which cost levels are required for AWE to be competitive. A specific focus is put on the relation between AWE systems (AWESs) and WTs. For this work, ERA‐5 wind data are used to compute the power generation of the wind power technologies, which is implemented in a cost‐minimizing electricity system model. By forcing a certain share of the annual electricity demand to be supplied by AWESs, the marginal system value (MSV) of AWE is investigated. The MSV is found to be affected by the AWE share, the wind resource, and the temporal distribution of the AWES's electricity generation. The MSV of AWE is location‐ and system‐dependent and ranges between 1.4 and 2.2 at a low share of AWE supply (0%–30%). At higher shares, the MSV drops. The power generation of WTs and AWESs are related, implying that the wind technologies present a similar power source and can be used interchangeably. Thus, the introduction of AWESs will have a low impact on the cost‐optimal wind power share in the electricity system, unless an AWES cost far below the system‐specific MSV is attained.

Experimental Study of Vertical Axis Wind Turbine Performance under Vibration

An experimental study was conducted to study the effects of flow uniformity on vibration and power generation of a small vertical axis wind turbine (VAWT). Previous studies have confirmed that one of the sources of vibration in the turbine is due to aerodynamic forces, which are due to incident wind. Firstly, understanding vibration is essential before proceeding to the measurements. In this experiment, further understand the vibrations of the turbine in operation, the operating deflection shape (ODS) technique was used. A wind tunnel and flow conditioner were fabricated. Experimental modal analysis (EMA) was conducted, and the dynamic characteristics are gathered. The ODS was conducted for operating the turbine at different speeds, with and without the flow conditioner. Results from EMA and ODS are correlated to explain the behavior of structures. In conclusion, the flow conditioner tested did have a big impact on the response of the structure in terms of vibration up to 30% indifference, but not so much in power generated about 2% indifference.

Quantile based probabilistic wind turbine power curve model

Wind turbine power curve is an indicator of wind turbine performance and important input of wind farm design or power prediction, therefore can serve the system planning and operation. However, a good power curve model is difficult to obtain because of the uncertain relationship between wind speed and its power output. Existing works focus on a deterministic model or use probabilistic distribution to represent such uncertain relation, which is not easy to be employed by the following decision-makers. This paper presents a novel concept termed as quantile power curve, which generates a series of power curves under any confidence level. Quantile loss based neural network algorithm is proposed to establish the quantile power curve. Index to measure the wind turbine performance and power generation uncertainty is also proposed based on the quantile power curve. Based on the operational data of a Chinese wind farm, the proposed model and index are validated and employed to estimate the wind energy yield when planning a system with wind, solar and electric vehicle charging loads. The results show that quantile power curve provides more comprehensive information about the uncertainty during the power generation process and helps to improve the renewable supply rate to charging loads.