Wind Turbine Energy Research Articles

The BMS Tool Monitoring Vertiv UPS and Vision Lithium-Ion Battery System

In my previous work published in an acknowledged publication, titled "Factory test of a TP-100 Lithium Ion Vision Battery System for possible Implementation in Soweto, Johannesburg, South Africa," According to the findings from the study report, the TP-100 Vision Lithium-Ion battery system was found to be suitable for implementing a wind turbine system in Soweto. Furthermore, the study's findings indicated that Lithium-Ion batteries were the optimal choice for storing energy in wind turbines. These batteries provided significant total cost of ownership (TCO) reductions over a 10-year period, without the inconvenience and expenses associated with replacing lead-acid batteries. This research paper aims to validate the precision of parameters obtained from the integration of a 160 kVA Vetiv Three Phase UPS to a TP-100 Vision Lithium Ion Battery system in contrast to those obtained from the Battery Monitoring System (BMS) while using Tool BMS Version 1.3 Software. The results validation was evident as the parameters acquired from the UPS and Battery system were found to be accurate when compared to those observed through the BMS tool. Finally, utilizing the Smart Cloud Management System (SCMS), validity was shown by the ability to remotely monitor the operation of both the UPS and the Li-ion Battery system.

Strain-life behavior of thick-walled nodular cast iron

Abstract For the design of thick-walled cast iron components used for wind energy turbines, large machines, presses, and engine parts made of nodular cast iron, the assessment of fatigue life of critical component areas is a crucial point to consider. Importance increases with the demand to safe resources by applying a lightweight design in combination with a certain resistance against extreme loads and misuse situations. Moreover, this gets more important when notches or deviations in the local microstructure are present. Nevertheless, due to accessibility, a lack in the description of the local strain-based material behavior still exists. Especially new cast iron grades such as ADI are not well investigated but offer an often-discussed potential for a lightweight design of the structure or to increase the highest admissible load on the structure. To determine the cyclic material behavior of nodular cast iron, different grades EN-GJS-400-18LT, EN-GJS-450-18, EN-GJS-500-14, EN-GJS-600-3, EN-GJS-700-2, and ADI in different wall thicknesses were investigated concerning their elastic–plastic material behavior under strain control. Moreover, new possibilities for the trilinear description of the strain-life and the stress–strain curve and the statistical and technological size effect are discussed in the following based on cyclic stress–strain and strain-life curves.

Reinforcement learning to maximize wind turbine energy generation

We propose a reinforcement learning strategy to control wind turbine energy generation by actively changing the rotor speed, the rotor yaw angle and the blade pitch angle. A double deep Q-learning with a prioritized experience replay agent is coupled with a blade element momentum model and is trained to allow control for changing winds. The agent is trained to decide the best control (speed, yaw, pitch) for simple steady winds and is subsequently challenged with real dynamic turbulent winds, showing good performance. The double deep Q-learning is compared with a classic value iteration reinforcement learning control and both strategies outperform a classic PID control in all environments Furthermore, the reinforcement learning approach is well suited to changing environments including turbulent/gusty winds, showing great adaptability. Finally, we compare all control strategies with real winds and compute the annual energy production. In this case, the double deep Q-learning algorithm also outperforms classic methodologies.

OPTIMAL SIZING OF SOLAR-WIND HYBRID MICROGRID USING IMPROVED GREY WOLF OPTIMIZATION ALGORITHM A CASE STUDY OF KADUNA - NIGERIA

This paper presents an improved grey wolf optimization algorithm (IGWOA) for optimal sizing of an isolated photovoltaic (PV), wind turbine (WT), and battery energy storage (BES) hybrid microgrid. To demonstrate the effectiveness of the proposed approach, atmospheric data sets comprising of wind, solar, and temperature of Kaduna International Airport were collected from Nigerian Meteorological Agency while the load demand data was collected from Kaduna International Airport Electricity Distribution Center. The microgrid optimal sizing was formulated as a constrained single objective optimization problem. Constraints including, loss of power supply probability (LPSP), power balance, generation limits and battery state of charge (SOC) were imposed. Three simulation scenarios were considered. Firstly, the target allowable maximum LPSP was fixed at 25% and the algorithm was able to determine the optimal sizing of the hybrid microgrid components and minimize the initial cost from 169,880.00 USD to 112,356.40 USD per annum resulting in 34% savings in cost. Secondly, the effect of the target allowable maximum LPSP variation was investigated, and it was found that the total installed capacity of the system decreases with increase in LPSP thereby decreasing the total cost. Additionally, a novel electricity price index (EPI) was introduced in order to quantify the degree of optimality of the solution. The EPI was found to increase exponentially with increase in LPSP, resulting in an EPI of < 0.05USD/kWh at 20% LPSP. Lastly, to validate the proposed approach, a comparative analysis between the IGWOA and other algorithms was carried out, and the proposed IGWOA proved applicable.

RNN-Based Time Series Analysis for Wind Turbine Energy Forecasting

One significant source of renewable energy is wind power, which has the potential to generate sustainable energy. However, wind turbines have many challenges, such as high initial investment costs, the dynamic nature of wind speed, and the challenge of locating wind-efficient energy regions. Wind power predicting is crucial for effective planning of wind power generation, optimization of power generation, grid integration, and security of supply. Therefore, highly accurate forecasts ensure the efficient and sustainable operation of the wind energy sector and contribute to energy security, economic stability, and environmental sustainability. This study proposes a deep learning (DL) approach based on recurrent neural networks (RNNs) for long-term wind power forecasting utilizing climatic data. The input data that forms the basis of this study is obtained directly from a wind turbine system operating under real-world conditions. The proposed model in this study is based on a multilayer back-propagation neural network (RNN) architecture specifically designed to effectively handle complex data sets and time-dependent series. The architecture of the model is built on an RNN consisting of four separate layers, each with 50 hidden neurons, carefully structured to increase its capacity to capture complex features. To improve the robustness of the model and avoid overlearning, each RNN layer is followed by a dropout (regularizing) layer that randomly deactivates 20% of the neurons to enhance the generalization ability of the network. To finalize the prediction capability of the model, a linear function was chosen in the last layer to directly match the actual values. Evaluating the model performance metrics, the proposed architecture achieved a prediction accuracy of 91% R2 on the test dataset. The findings indicate that proposed method based on multilayer RNN can successfully capture the relationships between the sequential data of the wind turbine.

Statistical study of the influence of the data sampling interval on the estimation of wind turbine energy

Main deficiency for wind power is variability. It is really difficult to predetermine the wind’s potential since wind velocity cannot be controlled or predicted with pinpoint accuracy. In this paper, two different methods are applied in order to estimate the monthly wind energy for different sites; those estimations are done using Weibull distribution and calculating energy with direct integration methodology. In additional, the influence of the data sampling interval is studied. Results show that the computational method based on the integration of the power is more accurate. Also, it is shown that the hourly time resolution provides satisfactory accuracy in wind resource estimation, in comparison with one minute’s resolution. Results allow better design of wind or hybrid systems. Moreover, wind speed acquisitions are minimized. A shorter time of treatment and less expensive measurement equipments are required.

Implementation of Grid-Connected Wind Energy during Fault Analysis Using Moth Flame Optimization with Firebug Swarm Optimization

In modern trends, the voltage profile has become increasingly critical when incorporating wind turbine energy sources because of changes in fault ride-through capabilities throughout voltage reaction. Ripple, voltage magnitude changes,and injected harmonics due to conversion switches are power quality issues for grid-integrated doubly fed induction generators (DFIG) wind sources. In this study, FACTS (flexible alternating current transmissionsystem) devices like the static VAR compensator (SVC), thyristor controlled series compensator (TCSC), unified power flow controllers (UPFC), and static synchronous compensators (STATCOM) are used to stabilise wind energy with DFIGs. The simulation test cases using MATLAB also analyse three lines to ground fault (LLL-G) of fault measures, which showed a 9 MW that transferred to utility grid. Therefore, it is suggested to inject or absorb reactive power to stabilise the system using moth flame optimization with firebug swarm optimization (MFO-FSO). The simulation results clearly demonstrate that the proposed MFO-FSO based STATCOM devices outperform the current non-linear generalized predictive control based STATCOM, which only achieves 0.9815 per unit voltage stability, by achieving ahigher voltage profile of 0.9925 per unit voltage stability with a reactive power injection of 1.82 MVAR.

Photovoltaic panel and wind turbine as an electric energy source for water evaporation and desalination

The purpose of the article is to study the process of heating and desalination of seawater due to electricity from wind turbines and solar panels. This electric current is collected and transmitted through wires to be used to power electrical devices. The advantages of using solar panels as an alternative energy source include environmental cleanliness, long service life, no noise, low cost of operation and no emissions of harmful substances. Boiling water using wind turbine energy is a great way to use renewable wind energy to support daily activities. By placing a wind turbine in an area with a steady wind, the turbine can collect kinetic energy from the air and convert it into mechanical energy that can be used to rotate the generator. This generator can then convert mechanical energy into electrical energy, which can be used to power an electric boiler. The boiler can then use electrical energy to heat the water, producing steam to boil the water.

The Frequency Regulation Strategy for Grid‐Forming Wind Turbine Generator and Energy Storage System Hybrid System in Grid‐Connected and Stand‐Alone Modes

This paper proposes a coordinated frequency regulation strategy for grid‐forming (GFM) type‐4 wind turbine (WT) and energy storage system (ESS) controlled by DC voltage synchronous control (DVSC), where the ESS consists of a battery array, enabling the power balance of WT and ESS hybrid system in both grid‐connected (GC) and stand‐alone (SA) modes. The newly developed GFM framework, i.e., DVSC, is adopted in both WT and ESS, which utilizes DC voltage dynamics for synchronization purposes. In this paper, the GC mode and SA mode are transferred by changing the status of the series‐connected switch, and it is necessary to meet the grid connection conditions when the system is transferred to the GC mode, namely, voltage, frequency, and phase sequence. For the GC mode, the inertia response from WT can be realized using the reserved energy of the DC capacitor, while the ESS serves to eliminate the steady‐state frequency deviation and reduce the DC voltage fluctuation of WT using the designed secondary frequency regulation scheme. For the SA mode, the proposed strategy can keep the power balance without external power sources. The small‐signal model of the WT and ESS hybrid system is derived. The stability analysis in both GC and SA modes is fully conducted utilizing the modal analysis method, where the impacts of control parameters on stability are assessed. The performance of the proposed strategy in a weak system is evaluated. Simulation studies are carried out under various power system contingencies to verify the effectiveness of the proposed strategy and validate the correctness of the theoretical analysis.

Multi-objective distributed control of WT-PV-BESS integrated weak grid via finite time containment

This paper proposes a finite time containment control scheme to achieve distributed control of a weak grid which is integrated with photovoltaic (PV), wind turbine (WT) and battery energy storage system (BESS). Taking advantage of the containment control in multi-dimensional control, we proposed a multi-objective control scheme of the WT-PV-BESS system in a weak grid, wherein four control objectives are specified, namely, bus voltage regulation, grid frequency control, peak load shaving and economic benefits optimization for the system operator. Firstly, the sizing approach of BESSs to fulfill the operation mission requirements is proposed; meanwhile, the general coordination strategy for the PV, WT and BESS to achieve the demand–supply balance is proposed. Subsequently, the predefined four control objectives are modeled and transferred into the long/short time frame operational constraints of the BESSs. Afterwards, the finite time containment control algorithm is proposed to force the BESSs into achieving containment status within prescribed time. Simulation case studies are conducted on a modified IEEE 9-bus system to show the effectiveness and performance of the proposed scheme.

Effects of the WEC shape on the performance of a novel hybrid WEC-FOWT system

A novel wind-wave energy hybrid concept is presented in this paper, consists of a floating semi-submersible wind turbine and multiple point absorption wave energy converters (WECs). Numerical simulations are conducted using the hydrodynamic analysis program AQWA, based on the three-dimensional (3D) potential theory. To ensure the reliability of the numerical model, the hydrodynamic responses of the semi-submersible platform and the WECs are validated using available experimental data. Finally, three WECs with different shapes are designed to investigate the shape on the performance of this hybrid system, including platform motion, mooring line tension and power captured by the WECs. The results demonstrate that the hybrid system with the circular truncated conical WECs has the best performance compared to the other two shaped WECs. In addition, the influence of the platform motion on the performance of the WEC array is negative. Overall, this study could provide some insights for the design of other wind-wave integrated systems.

Two-body dynamic simulations of a combined semi-submersible floating offshore wind turbine and torus wave energy converter

Dynamic responses of a combined wind and wave energy system which consist of a 5MW semi-submersible floating offshore wind turbine (FOWT) and a torus-shaped wave energy converter (WEC) are investigated. In the investigated configuration, the torus WEC is constrained to move only in the heave direction with respect to the FOWT. This results in a two-body dynamic system that allows for the extraction of wave energy through the relative heave motion. A Modelica-based multi-body system (MBS) code is used to perform fully coupled dynamic analyses of the combined system. The wind turbine loads are calculated using the state-of-the-art Blade Element Momentum (BEM) method, while the wave power take-off (PTO) system is modelled as a linear spring and a viscous damper. A case study is presented where the mass of the torus is varied to investigate the impact on the two-body dynamics of the combined system. The system performance is assessed in terms of the maximum wave power absorption and the quality of wind power production.

Assessing the progression of wind turbine energy yield losses due to blade erosion by resolving damage geometries from lab tests and field observations

Assessing the progression of wind turbine energy yield losses due to blade erosion by resolving damage geometries from lab tests and field observations

Search strategy and line association analysis of cascading failure accident chain in new energy power systems

As the penetration rate of new energy in the power system gradually increases and the complexity of cascading faults increases, it is of great significance for the power system to comprehensively explore the chain of cascading faults in the new energy power system and quickly determine the closely related lines in the cascading faults. In response to the lack of consideration in existing research of the changes in the importance of transmission lines after the introduction of new energy, this paper proposes a cascading failure prediction index that integrates the importance and operational status of transmission lines in new energy power systems and applies it to the search for cascading failures in new energy power systems. First, the development characteristics of cascading faults were analyzed, and the main factors influencing cascading faults were identified: the importance of the transmission line and operating status of the new energy power system. Based on these factors, a prediction index for cascading faults was established, and the accident chain was searched using this index. Then, the FP-growth algorithm was used to analyze the lines in the fault chain concentration, and based on the analysis results, the correlation relationship suitable for the cascading failure lines in the new energy power system was determined. Finally, a simulation was conducted on an IEEE 10 machine 39 node system containing new energy wind turbines, and the results verified the effectiveness of the proposed indicators and strategies.

Interference effect on flow features and aerodynamic mechanism over the roof between two tall buildings

Interference effect of an upwind building on flow features and wind power potential over roof of principal building was investigated through numerical simulations. Flow features were examined at different wind directions and spacing ratios (d/B = 2, 3, 4, 5, and 6), and associated aerodynamic mechanisms were analyzed. Turbulence intensity was less affected by interfering building than mean velocity. d/B exerted a more important impact on mean velocity at θ = 0° and 180° than those at oblique wind directions. The isolated building had large power potential at leeward locations due to strong flow separation at θ = 0° compared with principal building. The largest skew angle βmax fluctuated greatly with roof locations (from −15° to 80°) of isolated building at θ = 0°. In comparison, βmax over principal building changed slightly with roof locations (from −16° to 3°) due to smooth flow separation and reattachment at θ = 0°. Effect of d/B on the largest wind energy and hub height of roof-mounted wind turbines depended on θ. Recommendations for the siting of HAWTs on roofs were finally provided.

Multi-Agent Systems and Machine Learning for Wind Turbine Power Prediction from an Educational Perspective

Artificial intelligence (AI) is an umbrella term that encompasses different fields of study, and topics related to these fields are addressed separately or within the scope of AI. Multi-agent systems (MASs) and machine learning (ML) are the core concepts of AI that are taught during AI courses. The separate explanation of these core research areas is common, but the emergence of federated learning has triggered their combined usage. This paper describes a practical scenario in the energy domain where these technologies can be used together to provide a sustainable energy solution for predicting wind turbine active power production. The projects in the AI course were assigned prior to the step-by-step learning of MASs and ML. These concepts were applied using a wind turbine energy dataset collected in Turkey to predict the power production of wind turbines. The observed performance improvements, achieved by applying various agent architectures and data partitioning scenarios, indicate that boosting methods such as LightGBM yield better results even when the settings are modified. Additionally, a questionnaire about the assignments was filled out by the student groups to assess the impact of learning MASs and ML through project-based education. The application of MASs and ML in a hybrid way proves valuable for learning core concepts related to AI education, as evidenced by feedback from students.

EUROPEAN OFFSHORE NETWORK WITH WIND POWER PLANTS

Problems The growth of electricity consumption and the change in climatic conditions present society with the challenge of ensuring sustainable and ecologically clean electricity production. For many countries, especially in the European Union, the transition to sustainable and ecologically clean energy sources has become a priority.
 Objective A general overview and analysis of trends in the development of the network of offshore wind power plants located in the open sea on the continental shelf of European countries
 Methodology of implementation The strategy for the development of a single European offshore network with wind power plants was used for research. The study of the effectiveness and advantages of offshore power plants was carried out in the variability of the advanced countries of the European Union..
 Results An overview of the electric power industry of European countries that pursue an active policy in the field of offshore energy is given. The statistics on the generation of wind energy, the number and development of wind turbines in Europe are given. The main projects that have been implemented or are in the process of preparation have been considered. An analysis of the measures introduced by the European Union to increase the use of wind energy and reduce dependence on traditional energy sources and achieve carbon neutrality was performed.
 Conclusions The European offshore network with wind power plants has become an important component of sustainable energy development in the region. Statistics on the development of offshore wind energy indicate that European countries are actively working on the development of this sector, investing significant efforts and resources.

Provisional Microgrid Frequency Regulation by Brain Emotional Learning Based Intelligent Controller and Implementation Through FPGA

With the advancement of technology and human movement towards evolution, intelligent control methods are becoming more important. One of the areas of progress is related to the development of new frameworks for electricity generation and distribution systems, and the microgrid structure with economic capabilities is one of the frameworks. Accordingly, this paper presents a new practical method for controlling the frequency of microgrids and is able to cover the following issues at the same time. 1- It considers the nonlinear model of provisional microgrid which has a hybrid structure (AC and DC) in addition to renewable energy sources. 2- Introduces a method for microgrid frequency control under different operational conditions that performs based on the brain emotional learning. 3- Ensures the operation and applicability of the control method for the provisional microgrid through implementation of FPGA for the first time 4- Confirms the robustness of the proposed method under extreme load changes. So, in the simulation scenarios, the effects of wind turbine and solar energy are considered and under the influence of various changes in load and system uncertainties, the robustness and efficiency of the proposed method are well demonstrated.

Using conditional generative adversarial networks for the prediction of stresses in an adhesive composite

AbstractFiber reinforced adhesives are an integral part of wind energy turbine blades and play an important role in evaluation of structural integrity of the blades. Digitization of manufacturing processes and demand for fast and efficient stress analysis argues for the application of deep learning. As a step forward in using deep learning to predict structural properties in composite, the work would explore the applicability of deep learning methods to 2D fiber reinforced composite. In this work, a composite adhesive used in wind energy turbine blades is used as an illustrative example. The composite adhesive when viewed using a CT scan machine shows a heterogeneous structure with two phases, a softer matrix phase and a much stiff fiber phase. For this purpose, several CT scan images of the fiber adhesive composite are used. The images on subsequent FEA analysis serves as the training data for the deep learning framework. Once trained, the neural network would be able to predict the stress distributions in the structure. We apply conditional adversarial networks as general purpose solutions to problems focused on stress prediction, which could be thought of as an image‐to‐image translation from the microstructure domain to the stress domain. These networks in addition to learning the mapping from input image to output image also learn a loss function to train for this mapping. This makes the method more generic because otherwise different loss formulations for different problems have to be formulated.

Blade linearization as a method of optimizing annual energy production and cost of energy of horizontal axis wind turbines

Green energy cost has been in constant decline in the past two decades. Nevertheless, the cost of energy remains the obstacle towards sustainability. This paper presents a methodology for enhancing both the affordability and the manufacturability of horizontal-axis wind turbines without compromising on the aerodynamic performance. But rather combining the gains of an enhanced annual energy production and a reduced cost of energy, aiming to decrease the payback period of investment. The original blade configuration suggested by Blade Element Momentum theory is compared with three different blade configurations using Blade Element Momentum calculations as a preliminary step, then the best performing configuration is compared to the original configuration using Computational Fluid Dynamics modelling. The annual energy production for each configuration was calculated based on wind data from the site of design in West Nile, Al Minya, Egypt. The cost of energy of each turbine was estimated using recent market prices, while the payback period of investment was calculated using the local tariff, ensuring a consistent analysis. As a result, linearization resulted in a reduction of 9.25% in Cost of Energy accompanied by an increase of 1.48% in annual energy production. Consequently, the payback period of investment was decreased by 9.26%.