Variable Speed Wind Research Articles

Task-Driven Path Planning for Unmanned Aerial Vehicle-Based Bridge Inspection in Wind Fields

Using unmanned aerial vehicles (UAVs) for bridge inspection is becoming increasingly popular due to its ability to improve efficiency and ensure the safety of monitoring personnel. Compared to traditional manual monitoring methods, UAV inspections are a safer and more efficient alternative. This paper examines the impact of meteorological conditions on UAV-based bridge monitoring during specific tasks, with the aim of enhancing the safety of the UAV’s costly components. The wake vortex behind a bridge structure can vary over time due to airflow, which can have a direct impact on the safety of UAV flights. To assess this impact, numerical analysis is conducted based on monitoring requirements specific to different tasks, taking into account wind speed, wind direction, and air temperature. In order to optimize UAV trajectory, it is important to consider the wake vortex intensity and its associated influence region, which can pose a potential danger to UAV flight. Additionally, the analysis should take into account the aerodynamic effects of different types of bridge columns on the wake vortex. An optimization algorithm was utilized to optimize the trajectory of a UAV during bridge inspections within the safe region affected by wind fields. This resulted in the determination of an effective and safe flight path. The study reveals that varying wind speeds have an impact on the safe flight zone of UAVs, even if they are below the operational requirements. Therefore, when monitoring bridges using UAVs, it is important to take into account the influence of meteorological conditions. Furthermore, it was observed that the flight path of UAVs during square cylinder column monitoring is longer and more time-consuming than round cylinder column monitoring. Determining an effective UAV inspection path is crucial for completing bridge monitoring tasks in windy conditions, establishing bridge inspection standards, and developing the Intelligent Bridge Inspection System (IBIS).

An improved typhoon monitoring model based on precipitable water vapor and pressure

The potential of monitoring the movement of typhoons using the precipitable water vapor (PWV) has been confirmed. However, monitoring the movement of typhoon is focused on PWV, making it difficult to describe the movement of a typhoon in detail minutely and resulting in insufficient accuracy. Hence, based on PWV and meteorological data, we propose an improved typhoon monitoring mode. First, the European Centre for Medium-Range Weather Forecasts Reanalysis 5-derived PWV (ERA5-PWV) and the Global Navigation Satellite System-derived PWV (GNSS-PWV) were compared with the reference radiosonde PWV (RS-PWV). Then, using the PWV and atmospheric parameters derived from ERA5, we discussed the anomalous variations of PWV, pressure (P), precipitation, and wind speed during different typhoons. Finally, we compiled a list of critical factors related to typhoon movement, PWV and P. We developed an improved multi-factor typhoon monitoring mode (IMTM) with different models (i.e., IMTM-I and IMTM-II) in different cases with a higher density of GNSS observation or only Numerical Weather Prediction (NWP) data. The IMTM was evaluated through the reference movement speeds of HATO and Mangkhut from the China Meteorological Observatory Typhoon Network (CMOTN). The results show that the root mean square (RMS) of the IMTM-I is 1.26 km/h based on ERA5-P and ERA5-PWV, and the absolute bias values are mostly within 2 km/h. Compared with the models considering the single factor ERA5-P/ERA5-PWV, the RMS of the IMTM-I is improved by 26.3% and 38.5%, respectively. The IMTM-II model manifests a residual of only 0.35 km/h. Compared with the single-factor model based on GNSS-PWV/P, the residual of the IMTM-II model is reduced by 90.8% and 84.1%, respectively. These results propose that the typhoon movement monitoring approach combining PWV and P has evident advantages over the single-factor model and is expected to supplement traditional typhoon monitoring.

Research on the Characteristics of Urban Building Cluster Wind Field Based on UAV Wind Measurement

An innovative approach for measuring wind fields in urban building clusters using Unmanned Aerial Vehicles (UAVs) is presented. This method captures the distribution of wind fields within clusters. The results indicate that building architecture has a significant influence on wind flow characteristics at 15 m and 25 m height levels. Particularly, areas adjacent to the buildings and the wake section exhibit notable variations in wind speed and turbulence intensity compared to the incoming flow. The regions most affected include the areas flanking the buildings on either side and the intermediate section of the wake. The flow separation and convergence of incoming wind from the windward sides of the buildings notably amplify the wind load, resulting in a significant shift in wind speed and turbulence intensity within pedestrian pathways. The use of UAVs for wind measurements enables a flexible and efficient assessment of urban wind fields. These findings pave the way for further research into wind field measurements in urban architecture and a better understanding of the interference effects of buildings.

Uncertainties and discrepancies in the representation of recent storm surges in a non-tidal semi-enclosed basin: a hindcast ensemble for the Baltic Sea

Abstract. Extreme sea level events, such as storm surges, pose a threat to coastlines around the globe. Many tide gauges have been measuring the sea level and recording these extreme events for decades, some for over a century. The data from these gauges often serve as the basis for evaluating the extreme sea level statistics, which are used to extrapolate sea levels that serve as design values for coastal protection. Hydrodynamic models often have difficulty in correctly reproducing extreme sea levels and, consequently, extreme sea level statistics and trends. In this study, we generate a 13-member hindcast ensemble for the non-tidal Baltic Sea from 1979 to 2018 using the coastal ocean model GETM (General Estuarine Transport Model). In order to cope with mean biases in maximum water levels in the simulations, we include both simulations with and those without wind-speed adjustments in the ensemble. We evaluate the uncertainties in the extreme value statistics and recent trends of annual maximum sea levels. Although the ensemble mean shows good agreement with observations regarding return levels and trends, we still find large variability and uncertainty within the ensemble (95 % confidence levels up to 60 cm for the 30-year return level). We argue that biases and uncertainties in the atmospheric reanalyses, e.g. variability in the representation of storms, translate directly into uncertainty within the ensemble. The translation of the variability of the 99th percentile wind speeds into the sea level elevation is in the order of the variability of the ensemble spread of the modelled maximum sea levels. Our results emphasise that 13 members are insufficient and that regionally large ensembles should be created to minimise uncertainties. This should improve the ability of the models to correctly reproduce the underlying extreme value statistics and thus provide robust estimates of climate change-induced changes in the future.

A model reference adaptive control framework for floating offshore wind turbines with collective and individual blade pitch strategy

The utilization of floating offshore wind turbines is regarded as a promising solution for offshore renewables, wherein blade pitch control plays a crucial role in both operation and maintenance. Among the various blade pitch control methods, strategies that involve more states pertaining to the control objectives demonstrate superior tracking performance. However, these approaches necessitate additional linearization and parameter tuning efforts, particularly when addressing varying operating conditions in real-world unsteady environments. In this study, a decoupled model reference adaptive control framework with inner control laws for ideal responses for both collective and individual blade pitch is employed, in the context of floating offshore wind turbine control problems during operation beyond rated conditions. According to the simulations conducted on a benchmark wind turbine simulator under a series of turbulent wind speeds, the proposed integrated controller achieved reductions in power fluctuation and damage equivalent load in blades compared to corresponding baseline controllers, at the expense of acceptable deterioration in tower base loads and blade actuator activities. The proposed scheme demonstrates its adaptability in serving the entire operating range, mitigating the need for parameter tuning due to variations in wind speed, and exhibits potential to involve more control purposes.

Exploring Meteorological Conditions and Microscale Temperature Inversions above the Great Barrier Reef through Drone-Based Measurements

Understanding the atmospheric conditions in remote areas contributes to assessing local weather phenomena. Obtaining vertical profiles of the atmosphere in isolated locations can introduce significant challenges for the deployment and maintenance of equipment, as well as regulatory obstacles. Here, we assessed the potential of consumer drones equipped with lightweight atmospheric sensors to collect vertical meteorological profiles off One Tree Island (Great Barrier Reef), located approximately 85 km off the east coast of Australia. We used a DJI Matrice 300 drone with two InterMet Systems iMet-XQ2 UAV sensors, capturing data on atmospheric pressure, temperature, relative humidity, and wind up to an altitude of 1500 m. These flights were conducted three times per day (9 a.m., 12 noon, and 3 p.m.) and compared against ground-based weather sensors. Over the Austral summer/autumn, we completed 72 flights, obtaining 24 complete sets of daily measurements of atmospheric characteristics over the entire vertical profile. On average, the atmospheric temperature and dewpoint temperature were significantly influenced by the time of sampling, and also varied among days. The mean daily temperature and dewpoint temperature reached their peaks at 3 p.m., with the temperature gradually rising from its morning low. The mean dewpoint temperature obtained its lowest point around noon. We also observed wind speed variations, but changes in patterns throughout the day were much less consistent. The drone-mounted atmospheric sensors exhibited a consistent warm bias in temperature compared to the reference weather station. Relative humidity showed greater variability with no clear bias pattern, indicating potential limitations in the humidity sensor’s performance. Microscale temperature inversions were prevalent around 1000 m, peaking around noon and present in approximately 27% of the profiles. Overall, the drone-based vertical profiles helped characterise atmospheric dynamics around One Tree Island Reef and demonstrated the utility of consumer drones in providing cost-effective meteorological information in remote, environmentally sensitive areas.

Maximum Power Point Tracking Based on Wind Speed Prediction

A control method for MPPT (maximum power point tracking) based on wind speed prediction is proposed to address the issue of slow attainment of optimal speed values by wind turbines in variable wind speed conditions in the wind power system. Initially, the wind speed variation value is accurately estimated using the SSA-BP neural network based on historical wind speed data. Subsequently, the MPP (maximum power point) is calculated considering the optimal blade-tip speed ratio, thereby enhancing the tracking speed of the system. Simulation results demonstrate that this method enables real-time estimation of wind speed variation, improves wind energy utilization rate, and maximizes the utilization of wind energy.

Synchronous Generator Feeding DC Grid Having Brushless Excitation System With Induction Machine Operating in Deep Plugging Mode

This paper proposes a novel excitation control for a recently proposed synchronous generator (SG) entitled Brushless Induction Excited Synchronous Generator (BINSYG) for wind energy applications. In BINSYG, one SG and another Induction machine (IM) are embedded in the same machine frame to make the overall system brushless. Here, IM acts as the excitation machine and regulates the dc excitation for SG by rectifying the rotor-induced currents through a rotating rectifier. SG feeds power to dc grid via a two-level six-switch Voltage Source Rectifier that is operated at unity power factor at the SG terminal. The excitation developed by IM is regulated using a new frequency modulation control strategy for a two-level Voltage Source Inverter connected to the stator of IM. The excitation machine helps operate SG at variable wind speeds with reduced flux distortions. The proposed system is speed/position sensorless and controlled without using any voltage or current sensors on the IM side. The system with its proposed control algorithm is modeled in MATLAB/Simulink and experimental verification for a small-scale laboratory prototype BINSYG feeding power to a 220 V dc grid are presented to validate the effectiveness of the controller.

A two-stage frequency response method for DFIGs under variable wind speeds

A two-stage frequency response method for DFIGs under variable wind speeds

Investigating the Impacts of Tropospheric Parameters on Received Signal Strength of the Mobile Communication System

Abstract Radio communication systems are crucial for information transmission, but they face challenges like interference and fading, which significantly impact network efficiency. Understanding propagation-related issues is crucial for optimizing networks. This study examined the influence of tropospheric parameters on the Received Signal Strength (RSS) of the Globacom communication system in Rivers State. Four tropospheric variables (air temperature, relative humidity, atmospheric pressure, and wind speed) are collected from the River State University of Science and Technology (RSUST) Environmental and Climatic Observatory Program. The results of the correlation analysis indicated a direct linear correlation between air temperature and RSS. In contrast, wind speed displayed an indirect relationship, while the remaining parameters demonstrated an inverse linear relationship. Multiple linear regression (MLR) and multiple linear perceptron (MLP) models were developed to accurately predict the impacts of tropospheric parameters on RSS. A supervised-learning three-layered MLP feed-forward neural network was employed, consisting of 10 distinct MLP NN models. The Levenberg-Marquardt (LM) learning algorithm was used in training and validating the MLP NN models. The MLP 4-40-1 model showed an exceptional understanding of the non-linear relationship between the tropospheric parameters and RSS. The MLR model exhibited a weaker correlation coefficient of 0.2164, while the MLP demonstrated a stronger correlation coefficient of 0.7942. Additionally, the MLP 4-40-1 model outperformed the MLR model in terms of Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE), achieving an accuracy of 0.2480 RMSE and 12.9963% MAPE, while the MLR model had 1.6880 RMSE and 27.3787% MAPE. These findings highlight the efficiency of the MLP 4-40-1 model in estimating the exact relationship between the tropospheric parameters and the signal strength of the mobile network in Rivers State. The study provides valuable insights into optimizing network design and emphasizes the significance of including tropospheric parameters in enhancing mobile communication performance.

Mosquito swarms shear harden

Recently Cavagna et al. (Sci Rep 13(1): 8745, 2023) documented the swarming behaviors of laboratory-based Anopheles gambiae mosquitoes. Here key observations from this 3D-video tracking study are reproduced by a minimally structured (maximum entropy) stochastic trajectory model. The modelling shows that in contrast with midge swarms which are a form of collective behavior, unperturbed mosquito swarms are more like collections of individuals that independently circulate around a fixed location. The modelling predicts the observed response Anopheles gambiae mosquitoes in wild swarms to varying wind speeds (Butail et al. in J Med Entomol 50(3): 552–559, 2013). It is shown that this response can be attributed to shear hardening. This is because mosquitoes are found to be attracted to the centre of the swarm by an effective force that increases with increasing flight speed. Mosquitoes can therefore better resist the influence of environmental disturbances by increasing their flight speeds. This contrasts with other emergent mechanical-like properties of swarming which arise accidentally without a change in an individual’s behavior. The new results add to the growing realization that perturbations can drive swarms into more robust states.

The Mathematical Modeling and Performance of Sky Radiative Coolers

Sky radiative cooling is a kind of passive cooling technology that uses the “atmospheric window” to emit the object’s own heat to the low temperature of outer space; this technology has low energy consumption, no pollution, and other useful characteristics, so in recent years it has attracted widespread attention. The cooling effect of the sky radiative cooler is mainly affected by the constantly changing outdoor ambient temperature. In addition, the structure of the radiative cooler itself also means that its radiative cooling power undergoes obvious changes. Here, we utilized COMSOL simulation software to establish a numerical heat transfer model for radiative cooling, aimed at investigating the influencing factors on the sky radiative cooler and methods to enhance the structure of the radiative cooling. This study discusses outdoor ambient wind speed, the inlet flow rate of the cooler, installation angle of the cooler, and different cooler structures. Based on simulation results, it is observed that, for varying wind speeds, when the ambient radiation temperature is higher than the surface temperature of the cooler, a larger ambient wind speed leads to a poorer refrigeration effect. The maximum temperature difference in surface temperature at wind speeds of 0 m/s and 4 m/s is 0.59 °C. When the ambient temperature is lower than the surface temperature of the cooler, a smaller wind speed results in a greater net refrigeration power. The maximum temperature difference in this scenario is 0.32 °C. The net refrigeration power of the radiative cooler increases with an increase in water flow rate. As the water flow rate increases from 0 L/min to 5 L/min, the net refrigeration power increases from 25 W/m2 to 200 W/m2 and gradually stabilizes. Considering the radiative impact of the cooler on the surrounding environment, as the installation angle increases from 0° to 90°, the surface temperature of the cooler first increases and then decreases, reaching its highest temperature of 29.26 °C at 45°. The surface temperature of the cooler varies with the thickness of the air sandwich, increasing from 1 cm to 12 cm, and then decreasing. The lowest temperature of 23.4 °C is achieved at a thickness of 8 cm. The increase in the fin structure on the surface of the radiative cooler leads to a decrease in its refrigeration performance, and the difference between the inlet and outlet temperatures of the radiative cooler with a flat plate structure is always greater than that of the finned plate, and the difference in the average radiance is 23.52 W/m2. Finally, the energy-saving effect of the sky radiative cooling composite system is analyzed. Taking a typical small office building as an example, an energy consumption analysis model is set up, and the energy consumption of the composite system is simulated in four cities with different climates, using EnergyPlus software (version 8.6); the system’s power consumption is the largest in hot and humid climates. Compared with the traditional vapor-compression refrigeration system, the composite system reduces air conditioning power consumption by 25.7%, 32.5%, 37.1%, and 44.8% in Guangzhou, Shanghai, Jinan, and Shenyang, respectively. The main innovations of this paper include analyzing and studying the influence of the tilt angle change of the radiative plate on the refrigeration performance of the cooler and the relationship between the surrounding buildings, adding air sandwiches and ribs to the radiative cooler to analyze the influence of convective heat transfer on the refrigeration effect, which plays a guiding role in the design and research of the sky radiative cooler.

Analysis of heat transfer characteristics of sandwich cylindrical shell with an aerogel-metal-rubber composite core

Metal-rubber sandwich cylindrical shell displays a broad range of potential applications in the field of thermal insulation due to its advantages of lightweight, high-temperature stability, and superior high-temperature thermal insulation properties. To optimize the thermal insulation performance of a sandwich cylindrical shell with a metal-rubber core, this study proposes the idea of filling the pores of the metal-rubber with aerogel, thereby forming a composite structure of aerogel and metal-rubber (AMR). Through finite element analysis, the law of internal temperature changes in the AMR composite structure and the distribution laws of the velocity field, viscosity field, and pressure field of the sandwich cylindrical shell under varying wind speeds are investigated. These findings are further verified through heat transfer and forced convection tests. The results reveal that the thermal conductivity of the AMR composite structure at the same density is lower than that of the metal-rubber, and the thermal insulation performance of the AMR composite structure is directly proportional to its density. Meanwhile, through the analysis of forced convection heat transfer in the cylindrical shell, it is found that wind speed has a direct relationship with the pressure field, velocity field, and viscosity field. In addition, there is a relatively high viscosity and a low flow rate near the wall of the cylindrical shell, with a higher flow rate at the axis of the cylindrical shell. At the same wind speed, the temperature rise of the sandwich cylindrical shell shows a positive correlation with an increase in density, and the maximum stable temperature also increases correspondingly. Moreover, at the same density, the thermal conductivity demonstrates a negative correlation with increasing temperature.

Fuzzy Logic Energy Management Based on Wind Turbine for Standalone Application

Wind turbines (WT) offer valuable environmentally friendly energy solutions, particularly for electricity generation, owing to their renewable nature. These renewable energy systems (RESs) present an indispensable solution for sites that are not associated with the electrical grid. However, their intermittent power generation, which can be more or less than that required by the load, due to variable wind patterns, poses a challenge. To overcome these weaknesses, the integration of robustly controlled storage systems is a good proposal as it enhances the stability of standalone applications in terms of voltages and powers. In this paper, an intelligent supervisory power control system is developed to manage the different components of the renewable energy generator (REG). A fuzzy logic proportional–integral PI controller is also introduced, in the permanent magnet synchronous generator (PMSG) and the batteries, to better regulate energy, compared to a conventional PI controller. The main objective of this study is to introduce a new control strategy to satisfy the energy requirement of the load. The configuration of the suggested control methodology consists of a fuzzy logic supervisor (FLS) elaborated to manage energy flows, ensuring equilibrium between production and consumption amidst weather and load changes. The presented control method was applied to an autonomous wind battery station, showcasing its ability to maintain energy supply, minimize fluctuations, and enhance stability. Simulation results, executed in MATLAB/Simulink, confirm the effectiveness of the proposed system under various scenarios of wind speed variability and storage system state of charge.

Spatial contrasts in mid-Holocene dune activities in deserts of northern China: Results from PMIP4 simulations in comparison with geological data

Numerous studies have explored paleoenvironmental conditions in China's dune fields around the mid-Holocene (MH), ∼6000 calendar years ago, often based on individual paleosol-aeolian sand depositional sequences and local-scale landscape dynamics. So far, continent-scale modeling studies have only indirectly assessed dune activation and stabilization processes via changes in simulated precipitation, net precipitation (precipitation minus evaporation), and effective moisture (precipitation divided by evaporation). Here we conducted a comprehensive study using gridded data on potential evapotranspiration, precipitation, and surface wind velocity from 13 climate models participating in the Paleoclimate Modeling Intercomparison Project phases 4 (PMIP4) and compared the model results with geological records. Our evaluation of spatial patterns of dune activity in northern China around the MH reveals that the intensity of annual MH dune activity exhibited significant differences between the eastern and western parts of Chinese deserts compared to those of the preindustrial period (PI). Dune activity was significantly weaker in the eastern region, while in the western part, it was considerably stronger. The key drivers of this disparity were the distinct spatial variations in near-surface wind speed and effective moisture between the two regions. Compared with the optically stimulated luminescence ages of the paleosol-aeolian sand sedimentary sequences derived from a compilation of geological records from 88 sites across northern China, the model results suggest that the weakening of dune activity and the process of dune stabilization in eastern China around the MH were mainly attributed to a significant increase in effective moisture and a concurrent decrease in near-surface wind speed due to the intensified East Asian summer monsoon. Additionally, the weakening of westerlies in western China resulted in reduced effective moisture, contributing to increased sand availability for dune development and subsequent desert expansion.

Grid interface control of wind–solar generator in Hail region of Saudi Arabia using FOPI controller

This study investigates the performance of a wind–solar generator (WSG) in the Hail region of Kingdom of Saudi Arabia (KSA) with a fractional order PI controller (FOPI) applied to the grid connection line. The proposed hybrid generator consists of a wind turbine with a PMSM machine and a PV panel connected to a secondary distribution grid by a DC link, three-phase inverter, and an RL filter. The line currents injected into the grid and/or loads are controlled by calculating the output reference voltage of an inverter and by the reference active and reactive requested powers. For this purpose, control loops around the RL filter were developed with fractional-order proportional-integral (FOPI) controllers, and a comparison with classic PI controllers was made. The system was simulated using the MATLAB/Simulink software. The results showed that the generator satisfied the power demand under the climatic conditions of the Hail region. They also demonstrated the good performance of the proposed controllers, which had good tracking accuracy and robustness to variations in wind speed and power demanded by loads in a very short time. The FOPI controller exhibited faster dynamic response and less overshoot than the classic PI controller.

Optimal external opportunistic maintenance for wind turbines considering wind speed

ABSTRACT A novel optimal opportunistic maintenance strategy for wind turbines considering wind speed as a stochastic process is proposed in this paper. Under this strategy, periodic preventive maintenance is conducted to reduce the probability of downtime due to failure, whereas external opportunities are leveraged to reduce the cost of preventive maintenance. This combined maintenance approach can also reduce costs associated with the wind turbine lifecycle. The formulated expected costs per unit time considered two scenarios, i.e. regular operation and energy generation interruption, to obtain the optimal solution via a genetic algorithm. Furthermore, we illustrated the economic advantages of the proposed strategy by comparing various maintenance strategies, as well as a sensitivity analysis to determine the effects of the different parameters in the proposed model. Experimental results demonstrate that the proposed opportunistic maintenance strategy is more cost-effective for wind turbines than comparable methods, with an optimal solution that varies according to variations in wind speed.

Smoothing Intermittent Output Power in Grid-Connected Doubly Fed Induction Generator Wind Turbines with Li-Ion Batteries

Wind energy is an increasingly important renewable resource in today’s global energy landscape. However, it faces challenges due to the unpredictable nature of wind speeds, resulting in intermittent power generation. This intermittency can disrupt power grid stability when integrating doubly fed induction generators (DFIGs). To address this challenge, we propose integrating a Li-ion battery energy storage system (BESS) with the direct current (DC) link of grid-connected DFIGs to mitigate power fluctuations caused by variable wind speed conditions. Our approach entails meticulous battery modeling, sizing, and control methods, all tailored to match the required output power of DFIG wind turbines. To demonstrate how well our Li-ion battery solution works, we have developed a MATLAB/Simulink R2022a version model. This model enables us to compare situations with and without the Li-ion battery in various operating conditions, including steady-state and dynamic transient scenarios. We also designed a buck–boost bidirectional DC-DC converter controlled by a proportional integral controller for battery charging and discharging. The battery actively monitors the DC-link voltage of the DFIG wind turbine and dynamically adjusts its stored energy in response to the voltage level. Thus, DFIG wind turbines consistently generate 1.5 MW of active power, operating with a highly efficient power factor of 1.0, indicating there is no reactive power produced. Our simulation results confirm that Li-ion batteries effectively mitigate power fluctuations in grid-connected DFIG wind turbines. As a result, Li-ion batteries enhance grid power stability and quality by absorbing or releasing power to compensate for variations in wind energy production.

Large scale study on influence of biopolymer to mitigate wind induced sand erosion with durability analysis

The desertification is a serious threat that affects the sustainable agriculture, ecosystem, and economic progress of society. There is an urgent need for an environment friendly, cost-effective, durable, and practical approach to mitigate wind induced erosion. The present study introduces a novel approach through large scale testing using biopolymer, including durability analysis. The research investigates the influence of biopolymer namely Acacia gum (AG) to mitigate wind induced sand erosion. For the experimental setup, rectangular trays (180 cm × 60 cm × 15 cm) containing 11 erosion pins (three rows along the length, in a staggered way at equal spacing) have been filled with desert sand. Solutions have been prepared using biopolymer concentrations (1%, 2%, and 3%) mixed with water. The prepared solution was sprayed onto specimens and the treated samples were then left for air drying (7 and 28 days curing period). The study has been performed on summer days with an average temperature of 40 °C. After biotreatment, the specimens were examined in a wind tunnel at variable wind speeds of 6, 10, and 15 m/s to measure sand loss rate (SLR) for different wind intervals (i.e., 1, 2, 3, 4, and 5 min). Surface strengths were assessed using a pocket penetrometer. To check the durability of biotreated sand samples, 28 days curing period samples, with all biopolymer percentages have gone through 5 wetting and drying cycles (WDC). After the last WDC, samples were left again for 28 days and then experienced all tests. The minimum and maximum SLR for desert sand ranged from 17 kg/m² at 6 m/s and 1 min duration to 123 kg/m² at 15 m/s and 5 min duration respectively. Instead biotreated sand samples presented negligible SLR. Surface strength increased with increasing biopolymer concentration and a curing period. The maximum surface strength was 334.47 kPa recorded at 3% concentration during the curing period of 28 days. Further analysis involving SEM, EDX, and PXRD, confirmed sand particle bonding. For surface strength, 3% biopolymer concentration consistently produced positive outcomes, even after undergoing 5 WDC.

Fuzzy assisted optimal tilt control approach for LFC of renewable dominated micro-grid: A step towards grid decarbonization

Grid decarbonization is very crucial for the energy transition. The massive penetration of renewables in conventional power networks is the essential step for decarbonization but at the cost of complex control architecture. Due to the stochastic behaviour of renewables, load frequency control (LFC) is a very tedious task. This paper addresses the LFC issue in a standalone micro-grid (S-μG) via wild horse optimizer (WHO) assisted intelligent fuzzy tilt integral derivative with filter – one plus integral (FTIDF-(1 + I)) controller. The proposed S-μG consists of a solar photovoltaic (PV), wind turbine (WT) generator (WTG), and diesel engine generator (DEG) as power generating units and a flywheel as the energy storage unit (ESU). A nonlinear WTG model is used to extract the power from varying wind speeds, and a variable step size incremental conductance (V-InC) maximum power point (MPP) tracking (MPPT) technique is considered to harvest the optimal power from PV. To replicate the practical scenarios, nonlinearities of the governor, turbine, and flywheel are also considered in this work. The proposed intelligent WHO-FTIDF-(1 + I) controller can handle huge uncertainties and nonlinearities of the considered μG. FTIDF-(1 + I) controller prominence has been established over TIDF and FTIDF controllers. The effectiveness of WHO has been demonstrated over particle swarm optimization (PSO) and grey wolf optimization (GWO). From the obtained results, it is perceived that the reduction in maximum frequency deviation Δf with the proposed controller as compared to WHO-TIDF, WHO-FTIDF, PSO-FTIDF-(1 + I), and GWO-FTIDF-(1 + I) are 99.75%, 97.71%, 94.87% and 33.34% respectively. Finally, detailed robustness analysis, stability evaluation and real-power system validation via the IEEE 39 bus system of the proposed controller are also performed.