Wind Turbine Power Generation Research Articles

Modeling and Stability Analysis Based on Internal Voltage Dynamics in Synchronverter

With the large-scale centralized penetration of renewable energy represented by wind turbine and photovoltaic power generation, the equivalent inertia and synchronous torque of traditional power grids have decreased, which is worsening the frequency dynamics and threatening the stability and reliability of power grids. Virtual synchronous generators (VSGs) are a type of grid-friendly inverter in microgrids (MGs) that mimic rotational synchronous generators (SGs) to maintain system stability with the increasing penetration of power electronic converters. In this paper, the stability analysis method of one type of VSG, synchronverter, is investigated based on internal voltage dynamics. The torque components affecting the stability mechanism of synchronizing, inertia, and damping torque is introduced, which offers a physical insight into transient stability and dynamic performance. Insufficiency in either damping or synchronizing torque would increase the unstable possibility. The characterization method of stability mechanisms can be illustrated by the phasor diagram of synchronverter internal voltage. Some cases under different disturbances which change the pattern of synchronizing and damping torque and in turn influence the internal voltage dynamics of synchronverters, are also discussed. In addition, an auxiliary correction control loop is proposed and added in the synchronverter control loop to enhance the robustness of the synchronverter against disturbances. The tunable coefficient of the correction loop is analyzed based on the internal voltage method. Simulation results verify the validity of the internal voltage stability method in synchronverters.

Intelligent digital twin – machine learning system for real-time wind turbine wind speed and power generation forecasting

Wind power is a key pillar in efforts to decarbonise energy production. However, variability in wind speed and resultant wind turbine power generation poses a challenge for power grid integration. Digital Twin (DT) technology provides intelligent service systems, combining real-time monitoring, predictive capabilities and communication technologies. Current DT research for wind turbine power generation has focused on providing wind speed and power generation predictions reliant on Supervisory Control and Data Acquisition (SCADA) sensors, with predictions often limited to the timeframe of datasets. This research looks to expand on this, utilising a novel framework for an intelligent DT system powered by k-Nearest Neighbour (kNN) regression models to upscale live wind speed forecasts to higher wind turbine hub-height and then forecast power generation. As there is no live link to a wind turbine, the framework is referred to as a “Simulated Digital Twin” (SimTwin). 2019-2020 SCADA and wind speed data are used to evaluate this, demonstrating that the method provides suitable predictions. Furthermore, full deployment of the SimTwin framework is demonstrated using live wind speed forecasts. This may prove useful for operators by reducing reliance on SCADA systems and provides a research and development tool where live data is limited.

A Novel Stochastic Blockchain-Based Energy Management in Smart Cities Using V2S and V2G

This paper proposes a secure management framework for optimal scheduling and operation of energy systems in smart cities, which will bring balance in modern power systems. The expanded infiltration of energy systems along with stochastic performance and high data transfer rate can bring new challenges that may severely affect precise energy management. This research work investigates the optimal and secure operation of energy systems in the presence of smart transportation systems in a correlated energy system. To achieve the maximum efficiency from the transportation system, the ideas of vehicle-to-subway (V2S) and vehicle-to-grid (V2G) are utilized to develop a bilateral power flow strategy (charging/discharging or idle mode) to provide the chief charging requirements of energy systems. In this regard, the proposed model suggests a novel stochastic architecture based on unscented transformation (UT) to handle the operational uncertainties of the transportation systems when considering the correlated power generation of wind turbines (WTs). Moreover, blockchain technology is deployed to guarantee the security of data transfer within the smart city. Hence, there is a secure collaboration within all sub-systems in the smart cities, which has triggered the integrated management in this structure. The simulation results are discussed on a typical smart city test system to show the quality of the proposed model.

Potential Evaluation of Power-to-Gas system by using Wind Turbine and PEM

The Power-to-Gas (PTG) is one of the progressing technologies that enables the conversion of electrical energy into hydrogen or methane gas. This study focuses on the comprehensive analysis of energy efficiency, exergy efficiency, and exergy destruction rate within the PTG overall system. The investigation covers the evaluation of the wind turbine for power generation, the proton exchange membrane (PEM) employed in hydrogen production, and the methanation unit dedicated to methane production. The analysis examines the overall system as well as each individual component. By quantifying the energy and exergy efficiencies, this study provides potential improvements for maximizing the PTG system's overall effectiveness. The outcomes of this study contribute to the advancement and deployment of PTG technologies, thereby facilitating the integration of renewable energy sources and promoting sustainable energy solutions.

An improved kinetic energy control strategy for power smoothing of PMSG-WECS based on low pass filter and fuzzy logic controller

• This paper develops an improved kinetic energy control method to mitigate output power fluctuations. A low pass filter is introduced to generate the output power smoothing reference. • A fuzzy logic controller is designed to generate the difference coefficient for balancing wind turbine stability and power generation efficiency. • The setting principles of important parameters in the control strategy are discussed, such as the filter time constant and delay time interval. • Experiments are carried out to make up for the lack of experimental verification in this research direction. The output power of wind energy conversion system (WECS) fluctuates randomly due to the intermittence of wind speed, which will endanger the power quality and the stable operation of the grid. This paper develops an improved kinetic energy control method to mitigate output power fluctuations. In this method, a low pass filter is introduced to generate the output power smoothing reference. A fuzzy logic controller is designed to generate the vacancy coefficient for balancing wind turbine stability and power generation efficiency. The proposed method has the advantage in precisely tracking the smoothing command and reducing dependence on energy storage devices. Additionally, the setting principles of important parameters in the control strategy are discussed, such as the filter time constant and delay time interval. The direct-drive permanent magnet synchronous generator based wind energy conversion system (PMSG-WECS) is modeled both in the simulation and experiment platform to verify the effectiveness of the proposed power smoothing strategy.

Control Strategy and Economic Benefit Analysis of Wind Turbine Blade Replacement

This paper takes a 1.5 MW wind turbine wind farm in Jilin province as an example. It analyzes the improvement of blade lengthening in wind turbine power generation by optimizing control strategy and adopting intelligent control technology. And it compares the economic benefits of replacing 82 blades and 89 blades and provides a theoretical and practical basis for blade lengthening transformation of other wind farms.

Performance of DFIG fed wind turbine under fault conditions

This paper discuss the performance of doubly fed induction generator (DFIG) fed wind turbine system under fault conditions. The proposed performance analysis is demonstrated in MATLAB/Simulation software. A sailent pole rotor type induction generator and three phase converter comprises with IGBT switches are employed to propose the DFIG system. The proposed design has ability to extract utmost energy under minimum wind speed constraints. In order to achieve utmost energy, various control designs such as pitch angle control, wind turbine speed, DC bus potential, grid potential and reactive power control are presented in this paper. The simulation results obtained are helps in understanding the dynamic performance of the DFIG fed wind turbine power generation system.

Improved Operation and Stability of a Wind-Hydro Microgrid by Means of a Li-Ion Battery Energy Storage

This article presents an isolated microgrid which combines two renewable power generators: a Hydraulic-Turbine-Generator (HTG) and a Wind-Turbine-Generator (WTG) with a Li-ion battery-energy-storage (BES). Depending on the generator(s) which supply active power, the microgrid can operate in three modes: Hydro-Only (HO), Wind-Hydro (WH) and Wind-Only (WO). In WH mode, the HTG supplies the difference between the power demanded by the consumers and the power supplied by the WTG. This net demanded power can be negative when the WTG power is greater than the load and this situation can lead to a microgrid collapse. This article shows by means of simulations how the BES is controlled to consume the WTG power excess guaranteeing the microgrid stability. Additionally, when the negative net demanded load is persistent the microgrid must transition from WH mode to WO mode, where only the WTG supplies active power, and this WH-WO transition is also simulated. In the simulations in WO mode, the BES is controlled to regulate the microgrid frequency. The needed controls to command the BES in WH and WO modes and in the WH-WO transition are also explained. The simulations show the effectiveness of using the BES since the microgrid stability and reliability is improved.

Potential of Small-wind Turbine for Power Generation on Offshore Oil and Gas Platforms in Malaysia

On most of the offshore oil and gas platforms, the current means of generating power are through the use of generators i.e. gas turbines and diesel power generators, or micro-generators for some smaller equipment. These generator sets are less reliable, especially on unmanned platforms. Hence, the deployment of renewable energy, such as the use of wind turbines, would be better for energy security, economic development and also protection of the environment. Instead of using wind power to power up the whole platform, small wind turbines can be utilised to power up some utilities and instrumentations on the platform, while having generators as back up, as the wind speed is beyond control. However, the capability of a small wind turbine in generating enough power is constantly under doubt as it is yet to be widely employed and only meagre data is available. This is caused by the issue of having insufficient studies regarding the implementation of small wind turbines for power generation on offshore oil and gas platforms. Hence, this paper studied the capability of small wind turbines for power generation on offshore platforms in Malaysia. Several models of small wind turbines were selected and their abilities in generating power to fulfil the annual energy consumption on a typical offshore platform were examined through precise calculations. The common offshore locations in Malaysia were identified and the average wind speeds from 2017 to 2019 at these locations were analysed. The result shows that certain models of small wind turbines are able to provide a significant amount of power for an offshore platform especially to power up the low power machineries. It was found that Kerteh, Terengganu is the most suitable offshore location to harness wind power due to its averagely high wind speed throughout the year. The highest amount of energy that can be produced was around 1445kWh per annum at Kerteh by the small wind turbine with the largest swept area and the lowest cut-in speed. This paper aims to serve as numerical validation on the plausibility of integrating small wind turbines for the generation of electricity on offshore platforms in Malaysia while also providing the recommended locations that are suitable for this region.

A Novel Wake Control Approach for Power Generation Improvement of Three Wind Turbines in a Wind Farm

Abstract The total power generation of a wind farm is significantly affected by the adverse wake generation of upstream wind turbines on the aerodynamic performance of the downstream wind turbines. This paper aims to provide a novel hybrid wake control strategy to improve the power generation of horizontal-axis wind turbines in a wind-farm layout. A numerical simulation based on the actuator line method with large eddy simulation (ALM–LES) is performed to investigate the airflow around three-dimensional National Renewable Energy Laboratory (NREL) 5 MW turbines in a three-by-one layout. The yaw angle (θ) and tilt angle (ϕ) of the wind turbines are in the range of (−30 deg<θ<30 deg) and (0<ϕ<35 deg), respectively. Firstly, the combined effects of yaw-control and tilt-control methods on the velocity profile, vorticity generation and turbulent kinetic energy in the wake region of the multiscale wind farm are investigated. Afterwards, the total power generation of the wind farm is compared with previous wake control methods of wind turbines. It was observed that the proposed hybrid wake control method could improve the total power generation by 9.94% compared to the previous wind turbine wake control techniques. The hybrid control strategy can deviate the wake much better than typical single-control methods. An optimization analysis is also provided to find the most appropriate yaw angles and tilt angles of the wind turbines subject to varying wind speeds.

Power Generation Prediction Method of Offshore Wind Turbines Based on Cascaded Deep Learning

Aiming at the problems of low prediction accuracy, long time, and poor results in current wind turbine generation power prediction methods, an offshore wind turbine generation power prediction method based on cascaded deep learning is proposed. Using deep belief networks, stacked autoencoding networks, and long short-term memory networks, a cascaded deep learning method is proposed to predict the power generation of offshore wind turbines. Multiple feature extractors are used to extract and fuse high-level features to form a unified feature with richer information to predict the power generation sequence of offshore wind turbines. According to the modeling strategy and port design strategy, using the stacked autoencoding networks as the basic unit, a cascaded deep learning model for generating power prediction of offshore wind turbines is established. Through the selection of input variables, the variables that have a great correlation with wind power are obtained. The layer-by-layer greedy algorithm is used for training from bottom to top, and supervised learning is used to fine-tune the network parameters from top to bottom to realize the generation power prediction of the offshore wind turbine. The experimental results show that the proposed method is effective in predicting the power generation of offshore wind turbines, which can effectively improve the prediction accuracy and shorten the prediction time.

MADV-DAQ: Multi-channel Arduino-based differential voltage data acquisition system for remote strain measurement applications

Wind turbine power generation, both onshore and offshore, has gained significant popularity over the past few decades. However, the design of a turbine’s foundation, capable of supporting a tall structure subject to large horizontal forces, remains challenging. Complex wind loading and intricate soil-structure interaction between the foundation and the supporting soil requires consideration. Although commercial structural health monitoring (SHM) systems provide several advantages, they remain cost prohibitive. This paper demonstrates the development, testing, fabrication, installation and validation of a low-cost, multi-channel, Arduino-based differential voltage data acquisition system (MADV-DAQ) suitable for remote, battery powered measurements of multiple Wheatstone bridge-based (strain) sensors. The instrumented wind turbine (120 m high, 3.45 MW generation capacity) forms part of a newly constructed onshore wind farm in South Africa. The developed MADV-DAQ system proved valuable in measuring strains associated with the wind turbine tower, quantifying the true magnitude of the loads being transferred to the underlying foundation. MADV-DAQ was designed to relay the real-time measurements to two, independent cloud platforms for aggregation, visualization and subsequent analysis. MADV-DAQ was purposefully designed as a universal data acquisition system, compatible with any Wheatstone bridge-based sensor design, including strain gauges, tensiometer and similar MEMS-based sensors.

Experimental Examination for the Electric Power Generation of a Commercial Small-scale Wind Turbine with Modified Aerodynamic Design

Small-scale horizontal axis wind turbines (SSHAWT) have recently gained more interest due to fuel shortages and environmental issues. Generally, they are installed close to the ground in the wind boundary layer, where the velocity is relatively small. This study aims to evaluate the performance of a commercial SSHAWT and to modify rotor design based on wind velocity in the field. The experimental measurements are performed to examine a commercial 1.5 m radius rotor of 1 kW SSHAWT, which was designed at a rated velocity of about 8 m/s. The CFD calculations are performed as well to understand flow characteristics and to verify the experimental measurements. In the experimental procedure, the test of the wind turbine with a permanent magnet synchronous generator showed that at a wind velocity of 5 and 6 m/s, the highest generated power is 163.9 and 306 W corresponding to power coefficients of 0.255 and 0.28 when AC voltage of the generator is 24.4 and 37.76 V, the DC voltage after rectified 22 and 34 V, and the load current 7.45 and 9 A, respectively. Then the available wind velocity was measured in the field, and the average velocity was 4 m/s. Therefore, a more appropriate rotor is designed based on field velocity. The CFD calculations are used to test the new rotor. The results clarify that for the new rotor, the maximum blade's power coefficient is enhanced by more than 60 % compared to the commercial rotor.

Mathematical Model for Household Off-Grid Simulation (Off-Grid System Sizing)

Abstract The paper presents the results of the research, which was initiated in our previous publication. The main goal of the research is to develop and validate our own multi-objective simulation tool for determination of optimal mix and sizing of off-grid and grid-connected microgrid systems. The first version of the developed model was tailored specifically for simulation of household off-grid system, which consisted of solar photovoltaics (PV), micro wind turbine, electric batteries, and backup power generator. Proposed algorithms are based on simulation of mentioned resources and hourly electric loads of off-grid system with the objective to reduce unsupplied energy volumes and total system costs. Several alternatives were considered with different configurations of the off-grid system and dispatching strategies of available resources. The developed model was validated with calculations of real off-grid system and results were compared to those, which were made in the previous publication, using Homer Pro software.

DRL‐based frequency response of wind turbine generators adapting their variable regulation capabilities

Frequency response of wind turbine generators (WTGs) is a crucial technology to ensure safe integration of increasing future wind power into power grids. WTGs' regulation capabilities are variable compared to conventional generations due to uncertain wind speed and dynamic deloading factors, which affects the design of their frequency response strategy. This paper proposes a novel deep reinforcement learning (DRL)-based frequency response framework of WTGs, combining a deep deterministic policy gradient (DDPG)-based control and a proportional power sharing (PPS) strategy. The DDPG-based control dynamically determines the optimal total regulation power of WTGs and the PPS strategy allocates the total regulation power to each WTG. Benefiting from the ability of DDPG algorithm to handle multi-dimensional continuous system states and actions, the proposed DDPG-control enables a wind farm to dynamically determine the optimal total frequency regulation power under WTGs' variable regulation capabilities. Besides, the application of deep neural networks (DNN) in DDPG algorithm makes the DDPG-based control possible to be implemented in complex nonlinear systems. Simulations on an IEEE 39-bus system demonstrate that effectiveness of the proposed frequency response strategy of WTGs.

Fault diagnosis in wind turbines based on ANFIS and Takagi–Sugeno interval observers

Wind turbine power generation is becoming one of the most critical renewable energy sources. As wind power grows, there is a need for better monitoring and diagnostic strategies to maximize energy production and increase its security. In this paper, a fault diagnosis approach based on a data-driven technique, which represents the system behavior employing a Takagi–Sugeno (TS) model, is developed. An adaptive neuro-fuzzy inference system (ANFIS) method is used to obtain a set of polytopic-based linear representations and a set of membership functions to interpolate the linear models of the convex TS model. Then, considering the TS model, a fault diagnosis strategy based on convex state observers generate residuals to detect and isolate sensor faults. Unlike other methods, this proposal only needs to be trained with fault-free data. The proposed methodology is tested under different fault scenarios on a well-known wind turbine benchmark built upon fatigue, aerodynamics, structures, and turbulence (FAST). The results demonstrate the method’s effectiveness in detecting and isolating different sensor faults.

Evaluation of wind energy potential at four provinces in Morocco using two-parameter Weibull distribution function

In the last years, the use of wind turbines for power generation has increase due to the advance's technology used and the stable cost. In this paper, due to the low investment in the desert regions of Fezouata, Ouijjane, El Ouatia, and Taroudannt in Morocco, we have considered wind resources as an ideal option. Using meteorological statistics, Weibull's distribution function was able to estimate the wind power and potential of four various wind turbines for each site with various nominal powers, ranging from 250 kW to 2000 kW, for use in wind farms. 24 years of wind speed data to fit the Weibull distribution. For the four provinces examined, the annual mean value of wind speed and frequency distributions were collected. The desert province of El Ouatia shows that it is economically feasible to install a wind farm for the production of green energy.

An intelligent deep learning based prediction model for wind power generation

This paper proposes a novel deep learning method based on lower-upper-bound-estimation (LUBE) and long short-term memory (LSTM) models to capture the uncertainty effects in the power generation of wind turbines. The LUBE model deploys the prediction interval concept as well as the LSTM approach to make a robust and resilient model. The LSTM approach is applied to construct the optimal prediction intervals with an appropriate upper bound and lower bound with a certain confidence level. In addition, the fuzzy set theory is proposed in the model to let adjust LSTM parameters based on the decision maker's ideas. This approach empowers operators to satisfy both coverage probability and width indicators of prediction intervals to achieve the optimal solution. The collective decision optimization algorithm is introduced to provide more flexibility in tuning the LSTM parameters. The efficiency and quality of the proposed scheme are studied using some datasets gathered from the Australia wind farms.

Artificial cell swarm optimization and vapor liquid equilibrium for energy management system in smart grid

AbstractThis article proposes a hybrid approach for energy management system in smart grid. The smart grid system contains photovoltaic, wind turbine, micro turbine, battery. The proposed hybrid approach is the combination of artificial cell swarm optimization (ACSO) and vapor liquid equilibrium (VLE); therefore, it is termed as ACSO‐VLE. The aim of the proposed approach is minimization of fuel cost, operation and maintenance cost, hourly power variation in the grid connected micro grid system. The necessary load demand of grid connected micro grid system is continually monitored by ACSO. The VLE is enhanced the perfect consolidation of micro grid with respect to predicted load demand. During the micro grid operation, the first approach is focused the scheduling of various renewable energy sources to lessen the cost of electricity. The aim of the second method is to balance the power flow and diminish the impacts of predicting errors depending on rule summarized from the scheduled power reference. The proposed model is carried out in MATLAB; its efficiency is examined to with and without grid of micro grid system. The effectiveness of ACSO‐VLE technique is analyzed through the comparison analysis using the existing techniques. The proficiency is analyzed utilizing cost analysis including power generation of photovoltaic, micro and wind turbine, battery. The Root mean square error (RMSE), MAPE and Mean bias error (MBE) under 50 counts of trails of the proposed technique are 9.3, 4.2 and 2.7, l. Likewise, the RMSE, MAPE and MBE under 100 counts of trails are 13.5, 3.9 and 5.7. The mean, median, standard deviation attains 0.9681, 0.9062, and 0.1099.

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.