Turbine Generator Research Articles

Design guidelines for HAWT / VAWT decision-making in the context of underserved communities

Underserved communities in rural areas often lack access to appropriate technologies that provide sustainable and reliable energy sources vital to their well-being and economic activities. In this context, the potential of small-scale wind turbine generators (WTGs) as a renewable energy source is investigated, as they can provide enough energy while being relatively easy to operate and maintain. Nevertheless, limited research is carried out about the comparative advantages of vertical axis wind turbines (VAWTs) and horizontal axis wind turbines (HAWTs) for these resource-limited conditions. The research approach includes a literature review, a field study in Kenya, and a multi-criteria decision analysis (MCDA) tailored to local wind conditions and humanitarian engineering design principles. Structural, operational, and performance differences between HAWTs and VAWTs are explored as well as the local needs of underserved communities. The MCDA results highlight design principles for the deployment of small wind turbines in rural areas and provide useful design guidelines for WTG development in underserved regions.

Control of Hybrid Wind Turbine and Diesel Generators using PLC

This paper shows path of controlling hybrid system with wind and diesel generators via a programmable logic controller, these systems produce the required electrical power from different sources. This system can be used in different areas that have wind speeds between (10-180) km/h. When the winds are within this range, then the wind turbine generator is starting and suppling the electrical power to the load, if there is any fault is occurred or the wind is not within the working range then the wind turbine generator ceases and the diesel generator will start and supply the electrical power to the load.

Exploring the Unexpected: An AI Model Uncovers Rare Earth Elements

Throughout Earth’s history, powerful geological processes have shaped the planet, formed diverse landscapes, and hidden valuable resources. Like silent architects, these processes play a crucial role in creating the world we see today. One important aspect of this geological story is the formation of critical minerals—essential elements for modern technology. These minerals are not random features but are closely tied to the larger narrative of Earth’s evolution. Scientists and engineers are exploring the connections between geological processes and the creation of critical mineral deposits to understand better how to identify, extract, and produce these deposits to help meet the demand for resources considered the building blocks of an electrified future. Used in various applications, from energy production and national defense to transportation and communication technologies (think batteries for electric vehicles, permanent magnets for wind turbine generators), critical minerals are defined as non-fuel minerals or mineral materials essential to economic or national security and have a supply chain vulnerable to disruption. The International Energy Agency (IEA), in its latest Critical Minerals Market Review, reported that the record deployment of clean energy technologies has propelled the huge demand for critical minerals such as lithium, cobalt, nickel, and copper. “From 2017 to 2022, the energy sector was the main factor behind a tripling in overall demand for lithium, a 70% jump in demand for cobalt, and a 40% rise in demand for nickel,” the report said. “The market for energy transition minerals reached $320 billion in 2022 and is set for continued rapid growth, moving it increasingly to center stage for the global mining industry.” In the US, critical mineral supply chains are heavily dependent on imports. In 2021, the US was 100% import-reliant for 14 of the 50 critical minerals identified by the US Geological Survey (USGS) in 2022. Additionally, according to the USGS, most of the critical minerals that are essential for meeting decarbonization goals have a net import reliance of greater than 50%. Securing Supply of Rare Earth Elements Critical minerals categorized as rare earth elements (REE) are particularly important to reaching decarbonization goals. With a name reminiscent of science fiction superheroes, the lanthanides are a series of 15 elements—plus scandium and yttrium—collectively regarded as REE. REEs range from light (LREE) to heavy (HREE). While not geologically rare, extraction and processing are complex, expensive, and environmentally hazardous. In addition to being a leading source of critical minerals gallium, germanium, and graphite, China dominates REE production and processing. According to a report by The Oxford Institute for Energy Studies, the country is home to some of the most productive and lowest-cost REE-containing geological formations, which the government has been developing since the 1970s. China holds 37% of the world’s REE reserves, as compared to the US, which held only 1% of the reserves in 2019. US critical mineral supply chains have the potential to be disrupted by adverse foreign actions, pandemics, natural disasters, and other global events. For this reason, developing domestic resources to secure supply chains and meet future critical mineral demand has become a national priority, with the signing of Executive Order No. 14017 by US President Joe Biden in February 2021.

Fractional-Slot Concentrated Windings for Offshore Wind Turbine Generators: Opportunities and Challenges

Fractional-Slot Concentrated Windings for Offshore Wind Turbine Generators: Opportunities and Challenges

Research and simulation of hydraulic turbine speed control system based on fuzzy control

The rotational speed of the hydraulic turbine generator set will change with the load change of the power system, therefore, the speed control system of the unit needs to adjust its operating parameters in time according to the load change. The conventional PID control algorithm is widely used in the current hydraulic turbine speed control system, but it is unable to self-tune the parameters, which makes it difficult to achieve the desired control effect. In order to solve the above problems, this paper combines the neural network and fuzzy control to optimize the PID control parameters, and through the Matlab simulation experiments, it is verified that the control described in this paper can make the hydraulic turbine speed control system have satisfactory control effects in different operating conditions.

Numerical simulation of a novel small water turbine generator for installation in a deep-flow hydroponics system

Hydroponics systems are crucial for providing sustainable and cost-effective choices when soils are unavailable for conventional farming. The application of water flow rates within hydroponics systems to generate electricity is another idea that can be used in the field of power generation. This paper presents the determination of the mechanical power efficiency of a novel small water turbine generator for use in a deep-flow hydroponics system (DFT). The system was designed, analysed, and calculated for the most suitable geometries of the water pipeline inlet, DFT system, main structure of the PVC Tee Pipe Fitting, and a water turbine wheel using computational fluid dynamics software. The diameter of the water turbine wheel in this research was 48 mm. A DFT hydroponic system was modelled for the purposes of this research. We conducted a numerical simulation with water flow rates of 6, 8, and 10 l/min to evaluate the turbulent kinetic energy distribution in the DFT hydroponic system. The numerical simulation employed the control volume methodology, and the k-epsilon turbulence model was applied to obtain the computational conclusions. The highest torque and power that a novel small water turbine for installation in a DFT system could generate at a maximum flow rate of 0.000167 m3/s were 0.082 N.m. and 1.9568 watts, respectively. The forces generated by the fluid's speed and pressure can then be transferred to the building process of a novel small water turbine wheel. The FEA numerical result shows that the maximum value of the total deformation at a wheel speed of 228 rpm is 7.0 x 10-5 mm. The numerical simulations used in this study could potentially be used to further develop prototypes for innovative miniature water turbines that generate commercial electricity.

Oct-TCN-based wind turbine generator failure fault prediction research

Oct-TCN-based wind turbine generator failure fault prediction research

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.

Influence of Wind-Turbine-Generator Power Control on the Performance of a Virtual Synchronous Machine

Implementation of renewable energy sources (RESs) in power systems can reduce the dependence on fossil-fuel-based thermal power generation systems. At the same time, however, the system inertia decreases as synchronous generators decrease; this is crucial for maintaining the stability of the power system. Virtual inertia control (VIC) can regulate the output of an inverter-based resource (IBR) by increasing the inertia. For a wind turbine generator (WTG), output control factors such as pitch angle control and maximum power point tracking (MPPT) significantly affect the performance of the VIC. This paper theoretically clarifies that the pitch angle control contributes to improvements in the performance of the VIC and helps avoid a loss of operation of the WTG in frequency drop events by analyzing the movement of the operational point of the WTG based on the physical characteristic of the WTG and control characteristics of the pitch angle control and MPPT; an electromagnetic transient (EMT) simulation, performed to verify the analysis, is also presented.

Grid-Forming Control of DFIG-based Wind Turbine Generator by Using Internal Voltage Vectors for Asymmetrical Fault Ride-Through

Grid-Forming Control of DFIG-based Wind Turbine Generator by Using Internal Voltage Vectors for Asymmetrical Fault Ride-Through

Analysis of a Portable Generator Turbine for Small and Medium Industry by Using a Business Model Approach

Analysis of a Portable Generator Turbine for Small and Medium Industry by Using a Business Model Approach

Flying Trap (Fly-T): An Automatic Termite Trapping Based on IoT and Hybrid Energy System using NodeMCU

This paper proposes an automated productive caste termite trap device based on a hybrid energy system and the Internet of Things called Flying Trap (Fly-T). This tool is equipped with ultraviolet light with a frequency of 365 nm which is used to attract termites to enter and trap into Fly-T storage tank until they die. Dead termites will be detected by an ultrasonic sensor with a certain limit value then the relay cuts off electric current so that the light turns off and the tank door automatically opens to expel dead termites. The automatic control system on Fly-T is built using the NodeMCU ESP32 microcontroller to optimize the performance of sensors, relays, servo, and wifi connections in recording data to an IoT-based cloud database. The Fly-T is also controlled by command via a Telegram Bot equipped with solar panels and a windmill turbine generator. The results show that Fly-T can run automatically, easily, and save time efficiently, and is environmentally friendly.

Optimal Wind Power Plant Layout Using Ant Colony Optimization

The optimal layout of wind power plants is very important as the arrangement of wind turbine generators (WTGs) profoundly affects the overall energy output of the wind power plant. To address this important issue, this research investigates the best layout for WTGs in wind power plants with different terrain features across three locations in Thailand using the Ant Colony Optimization (ACO) algorithm. The objective functions of maximizing the net annual energy production (AEP) and minimizing the wake losses were used to achieve the optimal wind power plant layout. Using the MERRA-2 database, computational fluid dynamics (CFD) wind flow modeling was performed to create 10x10 km2 microscale wind resource maps of locations characterized by flat, semi-complex, and complex terrains to install wind power plants. The CFD wind flow modeling yielded wind speeds of 5.00 to 5.76, 4.21 to 8.90, and 3.10 to 4.45 m/s for the flat, semi-complex, and complex terrains, respectively, making them feasible for utility-scale wind power plants. WTGs of multiple blade diameters, ranging from 90 to 126 m, with a nominal capacity of 2.5 MW at 100 m above ground-level hub heights, were used in this study. The Gamesa G126-2.5MW WTG with a 126 m blade diameter produces the highest net AEP of 14.3, 76.1, and 38.9 GWh/yr for the three terrains. Hence, this WTG was used to perform an ACO-based optimization to improve the electricity production of the wind power plants. Such studies are important to improve the efficiency of wind power plants, thus extracting the maximum kinetic energy possible from the winds and improving the economic viability of wind power plants.

Wind power forecasting based on manifold learning and a double-layer SWLSTM model

With the increase of wind power installed capacity, wind energy is playing an increasingly important role in power grids. However, this also aggravates the volatility of a power grid. Accurate wind power prediction is particularly important for grid dispatching and grid connection modes. However, the existing forecasting methods mostly consider the meteorological environmental factors and historical power data and do not consider the impact of wind turbine generator parameters. Therefore, this study proposes a double-layer Shared Weight Long Short-Term Memory Network (SWLSTM) prediction model based on manifold learning considering meteorological environment factors, wind turbine generator parameters, and historical wind power. First, 17 indicators such as meteorological environment factors and wind turbine parameters are determined as influencing factors to construct high-dimensional data sets. Second, manifold learning is used to reduce the dimension of high-dimensional data sets and determine the best dimension reduction. The data after dimensionality reduction and historical wind power are used as the input of the prediction model. Finally, considering the influence of errors on the prediction results, a double-layer SWLSTM prediction model is constructed. The double-layer model is constructed simultaneously, and the final power prediction result is obtained by adding the prediction error of the lower model and the prediction wind power of the upper model. Moreover, a wind farm in Xinjiang is used to verify the effectiveness of the proposed prediction model. The results show that the prediction strategy and the model proposed in this paper have high prediction accuracy.

Design Strategies of Particle Dampers for Large-Scale Applications

Abstract Purpose Particle dampers are dynamic vibration absorbers that can be attached or inserted into a vibrating structure for broadband vibration attenuation. The particle damping technique is widely used across various industries for vibration attenuation because of its conceptual simplicity, cost-effectiveness, and suitability for harsh environments (Gagnon et al. in J Sound Vib, 2019. https://doi.org/10.1016/j.jsv.2019.114865; Lu et al. in Struct Control Health Monit, 2018. https://doi.org/10.1002/stc.2058). However, designing a particle damper for real-world applications is significantly challenging primarily due to the interaction among the numerous parameters that influence the damping effectiveness of a particle damper. Therefore, this contribution aims to experimentally investigate the particle dampers performance in the context of their designs. Methods We introduce three different design variants, namely thin-walled cavity (TWC), thin-walled cavity with additional sheets (TWC-AS), and ring cavity (RC). Different strategies are detailed and evaluated in the current paper. Following the comprehensive study of various design variants at the laboratory scale, several tests were conducted on a real-scale wind turbine generator, subjected to real-world loading conditions. Additionally, the effect of particle damper size and its location for the structure on vibration attenuation has been studied. Results Based on the experimental investigation, all these variants are effective in reducing the vibration amplitude of a structure. Furthermore, it has been found that for practical applications, particularly in the case of large-scale mechanical structures such as wind turbines, it is advisable to combine the most successful variants to design a particle damper. This approach can achieve significant vibration attenuation, and also minimize the additional mass of the granular material compared to a conventional particle damper. Conclusion The findings from our experimental studies offer valuable insight into the design of particle dampers for large-scale hollow mechanical structures.

Generalized adaptive singular spectrum decomposition and its application in fault diagnosis of rotating machinery under varying speed

Rotating machinery fault signals often consist of multiple components with time varying frequencies under variable speed conditions. Spectral overlap exists among these components, making it difficult to independently separate the features of the components. Singular spectrum decomposition (SSD), a singular spectrum analysis-based signal decomposition method, has shown its great potential in suppressing background noise and extracting fault-related components in complex background noise environments. However, SSD is a frequency domain decomposition method with equivalent filtering characteristics, and it is susceptible to the mode mixing when processing signals with spectral overlap. Moreover, the choice of a key parameter in the iteration decomposition process of SSD, the embedding dimension, is determined using an empirical formula, which might cause suboptimal decomposition outcomes. To address these issues, this paper proposes a generalized adaptive singular spectrum decomposition (GASSD) method, which combines generalized demodulation with improved embedding dimension selection for SSD. GASSD incorporates SSD into the framework of adaptive generalized demodulation to separate specific frequency domain features. Firstly, for an effective generalized demodulation analysis, a region block synchronous ridge extraction method is proposed to accurately estimate the instantaneous frequency ridges from the time-frequency plane, which helps construct proper demodulation phase functions. Secondly, to achieve optimal analysis of SSD, a Gini moderation decomposition index is designed to improve the construction of the trajectory matrix by determining an appropriate embedding dimension. Finally, the reliability of the proposed method is demonstrated by analyzing wind turbine generator bearing fault signals and rotor rubbing fault signals.

Analisis Efektivitas Gas Turbine Generator pada PT Mitra Energi Batam

PT Mitra Energi Batam is one of the Gas Power Plants (PLTG) and regulates the electricity supply of Batam city with PT PLN-Batam. According to the tabulation of PT Mitra Energi Batam's production data from September 2022 to September 2023, the rolls-royce Gas Turbine Generator production engine has a downtime value of 805.60 hours. The purpose of the study is to find out the factors that cause high downtime and know how to minimize it. Using the proposed Overall Equipment Effectiveness method as a way to measure Losses due to production disruptions by taking six big loss and Fishbone diagrams to find the cause and effect of Losses that cause failure of machine or equipment operations. The results of the study of the highest OEE value in August 2023 were 98.54%, the lowest OEE value was in April 2023 which was 0%, the cause was severe damage to the power turbine blade which required engine replacement, but was constrained by the time of the engine spare delivery process. The six big Losses factor that contributed the most to the OEE value was idling minor stoppage with an average value of 8.83% and equipment failure Losses with an average value of 8.81%.

Fuel-constrained joint heat and power dynamic economic environmental dispatch

The economical use of available fuel for producing electricity has been a very important challenge for power companies due to the continuously declining supply of fossil fuels. FCJHPDEED (fuel-constrained joint heat and power dynamic economic environmental dispatch) and JHPDEED (joint heat and power dynamic economic environmental dispatch) with DSM (demand-side management) integrating solar PV plants, WTGs (wind turbine generators), and PHS (pumped hydro storage) plants have been presented. Using SPEA 2 (strength Pareto evolutionary algorithm 2) and NSGA-II (non-dominated sorting genetic algorithm-II), FCJHPDEED and JHPDEED have been solved. It is seen that the results obtained without fuel constraints are more optimal than the results obtained with fuel constraints. The joint heat and power dynamic economic dispatch cost obtained with fuel constraints is approximately 2.14% more than the cost obtained without fuel constraints and joint heat and power dynamic emission dispatch, and the emission obtained with fuel constraints is approximately 6.7% more than the emission obtained without fuel constraints.

Thermoelectric optimization of integrated energy system considering wind-photovoltaic uncertainty, two-stage power-to-gas and ladder-type carbon trading

Integrated energy system (IES) coupled with renewable energy generation and power-to-gas (P2G) technology provides an effective solution to alleviate the current urgent carbon peak demand. Therefore, This paper develops a novel IES low-carbon economic operation strategy incorporating market mechanism. Firstly, a 24-h joint wind turbine and photovoltaic (WT-PV) output scenario generation method is proposed based on the time-varying Copula function. Secondly, the introduction of carbon capture technology (CCT), electrolyzer (EL), methane reactor (MR) and hydrogen fuel cell (HFC) refines the operation process of p2g into two stages. The combined heat and power (CHP) and HFC variable power operation strategy with adjustable thermoelectric ratio is proposed. Finally, the ladder-type carbon trading mechanism is used to guide IES to control carbon emission. The results of case study show that 1) The time-varying Copula function is effective in improving the scenario accuracy and fitting the data closely to reality 2) The introduction of hydrogen improves the energy utilization efficiency while promoting the consumption of WT-PV. 3) The variable power operation reduces the total cost and carbon emission by 2.4 % and 3.14 %, respectively, compared with the constant thermoelectric ratio. 4) The proposed IES operates reliably and economically under reasonable carbon trading parameters.

Fault detection of a wind turbine generator bearing using interpretable machine learning

Introduction: During its operational lifetime, a wind turbine is subjected to a number of degradation mechanisms. If left unattended, the degradation of components will result in its suboptimal performance and eventual failure. Hence, to mitigate the risk of failures, it is imperative that the wind turbine be regularly monitored, inspected, and optimally maintained. Offshore wind turbines are normally inspected and maintained at fixed intervals (generally 6-month intervals) and the program (list of tasks) is prepared using experience or risk-reliability analysis, like Risk-based inspection (RBI) and Reliability-centered maintenance (RCM). This time-based maintenance program can be improved upon by incorporating results from condition monitoring involving data collection using sensors and fault detection using data analytics. In order to properly carry out condition assessment, it is important to assure quality & quantity of data and to use correct procedures for interpretation of data for fault detection. This paper discusses the work carried out to develop a machine learning based methodology for detecting faults in a wind turbine generator bearing. Explanation of the working of the machine learning model has also been discussed in detail.Methods: The methodology includes application of machine learning model using SCADA data for predicting operating temperature of a healthy bearing; and then comparing the predicted bearing temperature against the actual bearing temperature.Results: Consistent abnormal differences between predicted and actual temperatures may be attributed to the degradation and presence of a fault in the bearing.Discussion: This fault detection can then be used for rescheduling the maintenance tasks. The working of this methodology is discussed in detail using a case study.