Demand for energy is growing across many countries in the world. In the case of wind energy, the use of renewable energy technologies is growing because of things like rising population and changing energy needs. Wealth may play a part, but it’s more important to look at bigger picture factors like energy demand, technological progress, and policy frameworks that encourage the use of wind energy. The global wind energy industry has witnessed remarkable growth over the years. In 2022, it was the third-best year ever for new wind energy capacity, with 78 GW added globally. The total installed global capacity reached 906 GW, representing a 9% year-on-year growth. In 2023, it is projected to be the very first to exceed 100 GW of new capacity added globally, with a forecasted year-on-year growth of 15%. 680 GW of new capacity is expected in the next five years (2023–2027), averaging 136 GW per year. However, trends documented in the Global Wind Report 2022 from the Global Wind Energy Council (GWEC) indicate that growth must quadruple by the end of the decade if the world is to stay on course for a 1.5°C pathway and net zero by 2050 [5].

From 1800 to 2022, there has been a significant and rapid growth in global energy consumption. Initially, traditional biomass was the primary source of energy until the early 20th century. The Industrial Revolution heralded the ascendancy of coal as a primary energy source, which was subsequently succeeded by the prominence of oil and natural gas in the mid-20th century [2]. Over the past few decades, there has been a substantial increase in the use of renewable energy sources such as wind, solar, and contemporary biofuels. Additionally, nuclear power has consistently played a role in the global energy mix, indicating a broader range of energy options.

Energy demand and consumption in India have experienced significant growth over the past few decades due to factors such as population growth, urbanization, industrialization, and economic development. The country’s energy sector is characterized by a diverse mix of energy sources, including fossil fuels (coal, oil, and natural gas) and renewables (solar, wind, hydroelectric, and biomass). Coal has historically been the dominant source of energy in India, primarily used for electricity generation. Oil and natural gas also play significant roles, particularly in transportation and industry. The demand for electricity in India has been growing steadily. This has put pressure on the country’s power infrastructure, leading to efforts to improve energy efficiency and expand electricity generation capacity. The government’s commitment to increasing access to electricity, improving energy efficiency, and reducing reliance on fossil fuels is reflected in initiatives, including the promotion of renewable energy. Wind energy holds immense potential for India’s sustainable development, contributing to environmental protection, economic growth, energy access, and energy security. Wind energy diversifies India’s energy mix, reducing reliance on conventional fossil fuels like coal and natural gas. This diversification contributes to a more balanced and sustainable energy portfolio. India has vast wind resources, especially in regions like Gujarat, Rajasthan, Tamil Nadu, and Maharashtra, making it one of the most suitable countries for wind energy development. Wind energy is renewable and clean, contributing to India’s efforts to reduce greenhouse gas emissions and combat climate change. It aligns with the country’s commitments under international agreements like the Paris Agreement. The cost of wind energy has become increasingly competitive with conventional sources of energy, especially as technology advances and economies of scale are achieved. This makes wind energy an attractive option for meeting India’s growing electricity demand. Wind energy can play a crucial role in electrifying remote and off-grid areas where extending conventional grid infrastructure is economically unviable. Off-grid wind power systems can provide reliable electricity access to remote communities.

As per the World Energy Outlook 2019 by the International Energy Agency, coal continues to be the primary source of worldwide energy consumption, accounting for 45% of the total. Petroleum and other liquids constitute 25% of the total, while biomass and garbage make up 20%. Natural gas accounts for 6% of the total contribution, whereas hydroelectricity, nuclear power, and other renewable sources each for 1%. This distribution highlights the ongoing worldwide dependence on fossil fuels, specifically coal and petroleum [2].

By the end of 2021, the country had the fourth-largest wind power capacity globally, totaling 40 GW, which was a 4% increase from the 38.62 GW generation in 2020. India is seeking to increase its green energy portfolio by utilizing the untapped coastal wind energy potential along its 7600 km shoreline. There has been a growing emphasis on offshore activities in recent years. The Ministry of Renewable Energy has established a goal of installing 30 gigawatts of offshore wind power by the year 2030. The Indian Wind Atlas indicates that the potential capacity for wind energy installation is approximately 102.79 GW at 80 m above ground level and 302.25 GW at 100 m above ground level. India’s offshore wind power business is in its early stages, with a potential capacity of approximately 60 GW. The coastal wind energy operational capacity grew from 25,088 MW in 2015 to 41.93 GW in 2022, as reported by IRENA. The trend is anticipated to rise in the upcoming years, contributing to the market’s expansion. The market is anticipated to be driven by the rising use of onshore wind energy, fueled by the expanding domestic wind sector.

As a result of increasing affluence and adapting to new ways of life, India has become the 3rd biggest global consumer of energy [6]. India will soon be the most populous nation on earth. India will need to add a power system the size of the European Union to its existing infrastructure in order to satisfy the rise in demand for electricity that is expected to occur over the course of the next two decades [7].

As of April 2022, the installed renewable energy capacity in India was 158.12 GW, which represents 39.43% of the country’s total installed power capacity. When compared to the previous fiscal year’s first eight months, the addition of 8.2 GW of capacity for renewable energy sources occurred during the first eight months of the FY22 fiscal year. Solar energy’s installed capacity has expanded by a factor of 18 since March 2014, when it stood at 2.63 gigawatts (GW), to its projected level of 49.3 GW by the end of 2021. When compared to the 1.73 GW of solar power capacity that was built in FY21, India’s addition of 7.4 GW of solar power capacity in FY22 through December 2021 is a 335% increase. It is anticipated that 392,000 off-grid solar goods would be sold in India during the first half of the year 2021, suggesting a rapid increase in the utilisation of this renewable energy source. The amount of energy that was generated from renewable sources (other than hydro) in May 2022 was 19.31 billion units (BU), which is an increase from the 17.87 billion units (BU) that were generated in May 2021. In the period from April 2021 to March 2022, renewable energy sources generated 170.91 billion units (BU), which is an increase from the previous year’s production of 147.24 billion units (BU). When it comes to the overall wind power capacity built, the nation holds the fifth spot on the global list. When compared to the 1.73 GW of solar power capacity that was built in FY21, India’s addition of 7.4 GW of solar power capacity in FY22 through December 2021 is a 335% increase [8].

The domestic wind power business in India has shown steady expansion over the past few years, making it the leader in India’s wind energy industry. The growth of the wind sector has resulted in the development of a thriving ecosystem, the capacity to successfully operate projects, and a yearly manufacturing base of around 10,000 MW. By the end of March 2021, the country had the fourth highest amount of installed wind capacity in the world, totaling 39.25 gigawatts (GW). During the year of 2020–2021, it generated around 60.149 billion units [9].

Another way to classify wind turbines is by the type of aerodynamic force they generate, either lift or drag, at the blade’s surface. As the name implies, lift-type machines exert a force perpendicular to the direction that the wind is blowing, whereas drag-based Wind Turbines (WTs) use aerodynamics to push in the same direction as the air flow. Unlike HAWTs and Darrieus turbines, which rely purely on lift forces, Savonius turbines and other innovative types of VAWT are based on the drag forces. The drag-based turbines are simple in design but perform poorly overall. However, lift-based turbines capture more of the wind’s potential energy per square foot of swept area [15], despite their complexity. Table 1 summarizes of the research work on various blade profiles used and parameters for small scale wind turbine.

There are several types of wind turbines that can create different amounts of energy based on the diameter of their rotors. Classification of HAWT based on rotor diameter and power rating were given in Table 2. Segmentations of wind turbines are used to categorise them, although there are no agreed-upon boundaries for these types of turbines. Small wind turbines typically have a rotor diameter of 0.5–10 metres with a power output of 50 watts to 15 kilowatts. They are used for low-power applications (battery charging, domestic consumption, cottages, etc.). Commercial applications typically require the design, installation, and connection to the grid of wind turbines with capacity of 15 kW or above (rotor diameters of 10 m or higher). A smaller wind turbine’s COE per unit of swept area is much more than that of a larger wind turbine.

The turbine is located inside of a duct that has been specially constructed to look like an annular wing, with the suction side of the wing pointing inward. Ducted turbines, in comparison to turbines without ducts, are able to sustain lower pressure levels than the ambient air pressure. This leads to enhanced airflow and higher energy generation capabilities [33]. The current design of wind turbines makes use of a mechanism known as passive yawing, and it has been recommended that the shroud be self-stabilizing in order to lessen the stress that is placed on the control system [34]. The fundamental advantage of the shrouded wind turbine is its great efficiency, but there are several other reasons why it beats horizontal axis wind turbines of comparable diameter. It is not necessary to have a yaw control system while using the shroud for yaw angles that fall within the stall range, which is around +30 degrees. The enclosure that is established by the shroud helps to reduce the losses that occur at the tip of the turbine, and it also helps to eliminate the negative effects that are caused by the tower wake on the turbine. Because of the flow field that exists within the shroud, it is possible to use an axial flow turbine, which ultimately results in increased energy production. In addition, the cost of the tower for a shrouded wind turbine is considerably less than the cost of a normal horizontal-axis wind turbine [33].

In comparison to the performance of the naked turbine, the installation of a simple conical diffuser increased the micro wind turbine’s coefficient of performance by around 60%. The coefficient of performance increased by 63% after the addition of a conical diffuser shroud for the nozzle. The ideal tip speed ratio for the diffuser and nozzle-diffuser advancements was determined to be 33% greater when compared to the bare turbine. The study revealed that the nozzle-diffuser enhancement yielded a minimal increase compared to the diffuser enhancement. The difference between the two was only 1.7% in favor of the turbine using diffuser augmentation [35].

Numerous research was undertaken to optimise the design parameters in order to increase performance, and a number of methods for estimating the aerodynamic characteristics of wind turbines were published. The aerodynamic performance of wind turbine is largely influenced by blade profile, the different types of blade profiles used for the study are as shown in Fig. 1. On the aerodynamic performance of tiny turbines, additional characteristics, such as rotor solidity and blade number, have also been explored [36,37].

Wind tunnel experiments and the Lattice Boltzmann Method (LBM) were used to examine the performance and interaction of two-stage rotors in micro–Counter Rotating Wind Turbines (CRWTs) [22]. The counter-rotating method improves the wind power coefficient of HAWT, whereas micro CRWT has two schemes that achieve the same result. National Renewable Energy Laboratory (NREL) Phase VI’s numerical study on the effect of pitch angle on HAWT performance led to the following conclusions. The Shear-Stress Transport (SST) k-model predicts thrust and power well. Most of the time, the SST turbulence model is used to simulate turbulent flow phenomena in Computational Fluid Dynamics (CFD). It takes the best parts of two well-known turbulence models and puts them together: the k-ω (k-omega) model near the walls and the k-ε (k-epsilon) model in the flow regions on the outside. The SST model can accurately represent both near-wall and far-from-wall turbulent flow behavior thanks to this hybrid approach. This makes it useful for many situations, such as those with complex geometries and boundary layer flows. Because micro turbines are smaller and have lower Reynolds numbers than utility-scale turbines, they often have different flow characteristics, such as being more sensitive to turbulence and boundary layer effects. For a given wind speed, there is an optimal pitch angle that maximizes turbine power. Wind speed determines the optimal angle. As air velocity increases, momentum transfer increases, increasing thrust. Increased pitch angle reduces blade frontal area and drag, reducing thrust [38,39].

The capacity of a wind turbine to convert wind energy into electricity is its efficiency. Using the National Advisory Committee for Aeronautics (NACAs) data and Q_blade airfoil modelling, the efficiency and power of new airfoils were studied in the past [39]. According to NREL, the choice of airfoil should maximise lift. The National Aeronautics and Space Administration preferred lift-based airfoils over lift-to-drag ratio. Three high-lift airfoils are being modelled in Q_blade. A NACA 23012 5-digit airfoil is utilised in power generation [40]. According to Q_blade, the addition of turbine blades boosts power. New airfoils and an optimised pitch were used to test this notion. The end product of the project is a portable turbine with four sets of blades capable of producing 10 watts of power in various conditions. The modular hub and blades make wind blade replacement simple. The 5-blade model cut 3.35 m/s. Reduced power increases efficiency. The 7-blade type provides the highest power between 3 and 4 m per second. Six-blade designs operate best over 5 m/s. The Alpha model generates 10 watts in winds greater than 10 m per second and is advised for safety [40].

Ronit et al. developed a low Reynolds number airfoil for use in tiny horizontal axis wind turbines to improve start-up and operational efficiency in low wind speeds. Tests were conducted on the upgraded airfoil (AF300) in an open circuit wind tunnel at Reynolds numbers of 38,000, 75,000, 128,000, and 205,000. Pressure variations were collected throughout the airfoil’s surface, and lift and drag forces were quantified using a dynamometer at various angles of attack, α. Particle Image Velocimetry (PIV) and smoke flow visualization were employed to analyze the airflow around the airfoil. Maximum lift coefficients of 1.72, 1.81, and 1.86 were achieved at Reynolds numbers of 75,000, 128,000, and 205,000, respectively, with a stall angle of 140 degrees. The lift coefficient rose from 0.41 to 1.05 at Re = 38,000 within the α range of 0–180, with no stalling observed [21].

The results from particle image velocimetry and smoke flow visualization showed that the flow stayed fully attached to the airfoil surface from Re as low as 56,000 at an angle of attack of 80 and maintained a fully attached flow up to 140 angle of attack for Re as low as 75,000.

The typical lifespan of a modern wind turbine is estimated to be twenty years, and the comparable number of rotations is somewhere between 108 and 109. The choice of material and the shape that it takes are the two most important aspects of the design of a rotor blade. The material that is chosen needs to be able to withstand wear and tear while also being relatively lightweight [41–43].

In the study, a computerized model was suggested for determining the amount of ice load that would be placed on wind turbine wing portions that were 80% of their span. Calculating the torque coefficient for four clear and iced rotors at different Angles of Attack (α) allows one to analyse how the buildup of ice affects the efficiency of a HAWT by measuring the influence of lower lift and greater drag brought on by the formation of ice. A rotor blade with a radius of 40 m is used in the calculation of the angle of relative wind; thus, rotor blades with a radius of 32 m are used in the calculation of the torque coefficient. In addition to that, a Tip Speed Ratio (TSR) of 6 and a wind speed of 15.3 m/s are presumed [44].

Stereoscopic Particle Image Velocimetry (PIV) was used to analyze the near-end transition-wake of a 0.6 m horizontal-axis wind turbine model [45]. The tip speed ratio impacts the structures and mixing qualities of the wake flow. Analysis of the structure of vortices reveals that wake instability relies on rotor tip speed ratio. Experimental work performed on small scale horizontal axis wind turbines presented in Table 3. As evidenced by unconditioned and phase-lock average velocity fields, wake instability influences flow characteristics [45].

The research shows that a powerful root vortex that is released from the maximum chord results in an outboard radial motion at the root region and a similar inboard motion at the maximum chord area (at r/R = 0.3). Both of these movements can happen at the identical radial point in relation to the turbine radius (r/R). This process results in a rise in axial velocity [55]. The effect of yaw angle on blade power with respect to different rpm speeds. Abu Dhabi’s accumulated wind hours are used to compare micro wind turbine performance and economics. The study says ATT’s payback is 1.83 years. ATT micro wind turbines could replace commercial wind turbines. Using the same swept area produces more energy, according to the study [56]. Salih N. et al. introduced an innovative design of a micro wind turbine optimized for low wind speed areas including the Arabian Peninsula, Jordan Desert, and United Arab Emirates (UAE). The experimental validation confirms the accuracy of the theoretical design’s performance analysis, demonstrating a high level of agreement between the two results. The theoretical power coefficient is 0.38, while the experimental power coefficient is 0.34 [22]. A prototype is created using a 3D printer to validate the simulation. The ATT prototype blades are tested in an open-air environment to achieve more realistic results. A methodology for designing ATT blades that incorporates Reynolds Number as a design parameter has been created and applied. The operational Reynolds number is taken into account together with the Blade Element Momentum (BEM) theory specifications since the lift and drag coefficients of the airfoils depend on it. The Reynolds number significantly impacts the performance of turbine blades. Reynolds number sensitivity research indicates that the ATT turbine blade airfoil SG6043 outperforms the NACA 4412 and SG6042 airfoils, as illustrated in Fig. 2. The Reynolds number is determined by the average chord length of the blade, while maintaining a constant wind speed of 5 m/s as the primary goal for designing micro turbine blades. The Reynolds number is estimated relatively high at first as 120000, and after three iterations for NACA 4412 and SG6042, it settles down to about 25000. Nonetheless, Airfoil SG6043 continues to oscillate in proximity to Reynolds number 25000.

It has been shown that neuro fuzzy control is a very reliable and secure method for managing established boundaries, and it can operate with steady-state, sinusoidal, and random wind profiles. In light of the fact that it was difficult to construct either a linear or a nonlinear plant, a database was utilised in order to fit the control. The control unit showed some encouraging capabilities, including being able to deter overload and reduce the amount of time required to overcome inertia. By preventing unnecessary wear and tear caused by low wind speed, an automatic timer that calculates the mean wind speed could be of great use. Training for the controller can be done with the neuro fuzzy tool, or it can be done manually if necessary. By providing the data set following the definition of the control rules, this tool made the process of designing the rules much simpler [57].

Simple passive controllers can be found on very small horizontal-axis wind turbines. These systems comprise tilt-up structures, gravity-operated furling tails, side-vanes, and spring-loaded tails. Microturbines that have a flexible tail that shifts as well as an off-center pivot offer a greater controlling ability and a more straightforward construction. This strategy is non-invasive, low-risk, dependable, and easy to execute [58].

France’s Georges Darrieus is credited with developing the first aerodynamic vertical-axis wind turbine, which was initially patented in 1931. Its operating principle relies on the fact that the speed of its blades is a multiple of wind speed. This results in an apparent wind blowing from behind the turbine all through the entire cycle, with minimal fluctuation in the angle of attack. Summary of wind turbine configurations with capacity and different features were been presented in Table 4. When combined to the actual wind, this headwind produces the perceived wind. If the angle of attack of this observed wind on the blade is larger than zero [59], the lift force contains a forward component that propels the turbine.

As mentioned earlier, small-scale wind turbines can efficiently fulfill the energy requirements of homes. However, the placement of these turbines is also vital in order to achieve maximum efficiency. Small-scale wind turbines have become a practical option for fulfilling the energy requirements of residential properties. Nevertheless, the strategic placement of these turbines is crucial in order to attain optimal efficiency. Multiple experiments have been carried out to determine the optimal locations for the placement of wind turbines. Topographical characteristics, such as elevations, depressions, and plant life, can greatly impact the movement of air currents. Multiple experiments have been conducted to determine the optimal location for placing wind turbines [97]. The topography, barriers, and adjacent constructions in the vicinity can affect the direction and intensity of wind currents, leading to turbulent conditions that may affect the efficiency of small wind turbines. Optimization techniques are employed to ascertain the optimal arrangement and design of turbines for a specific location. This entails employing mathematical modeling, computer simulations, and iterative design processes to determine the most advantageous placement of turbines in relation to one another and nearby obstacles. In order to tackle these challenges, optimization techniques are utilized to ascertain the most efficient arrangement and setup of turbines for a specific location [98]. This procedure frequently entails the utilization of advanced mathematical modeling, computer simulations, and iterative design processes. By employing these techniques, engineers and researchers can examine wind flow patterns, turbulence impacts, and wake interactions to determine the most advantageous placement of turbines in relation to one another and nearby obstacles. This article provides a comprehensive account of the planning, building, and overseeing of El Alumbre’s inaugural wind energy project, aimed at providing electricity to rural areas in Peru. The project is designed to generate power on a community-wide scale.

Wind turbine noise limits widespread wind energy adoption. Assuming that mechanical noise is well-controlled, aerodynamic noise from the blades dominating current large wind turbines. Aerodynamic noise is caused by airflow and airfoil noise. Low-frequency noise from upwind turbines is unimportant (i.e., with the rotor upstream of the tower). The leading edge of the blade and upstream turbulence generate inflow noise. On a three-bladed, 58-m-diameter wind turbine, acoustic field measurements were undertaken to identify noise sources and determine if trailing edge noise was predominant. Blade roughness was evaluated using untreated, cleaned, and scuffed blades. A broad horizontal microphone array assessed the rotor plane and blades for noise. The results of the array indicate that nearly all noise (emitted to the ground) is generated during blade fall. Convective amplification and trailing edge noise result in an asymmetric source pattern. The blades’ loudness was proportional to the flow speed. The slashed blade was more audible. The narrowband noise spectrum of a blade revealed that trailing edge bluntness noise was unimportant. According to tests [99], trailing edge noise dominates wind turbine noise.

The issue of wind turbine noise is a substantial concern that can have an effect on the widespread acceptance of wind energy. Although wind turbines generally have effective measures to minimize mechanical noise, the main source of noise in modern large wind turbines is aerodynamic noise, specifically from the blades. Aerodynamic noise is primarily caused by the interactions between airflow and airfoils. This study compares and contrasts the findings of the estimated annual energy production and cost-benefit evaluations conducted using actual wind speed data from various locations in the United Kingdom. The evaluations were performed under both steady state and dynamic conditions. The obtained results were derived from actual wind speed measurements. Therefore, to evaluate the performance in a stable condition, the wind power input is determined by employing Weibull distributions, with the user having the flexibility to choose the shape factors and mean wind speeds. The study utilized acoustic field measurements with a wide horizontal microphone array to evaluate noise levels at the rotor plane and on the blades. The evaluation of blade roughness involved comparing untreated, cleaned, and scuffed blades to determine the effect it has on noise production. The payback period for micro-wind turbines (micro-WTs) with rated powers of 1.5 kW or less is the most extended, and it may surpass the initially intended lifespan of these turbines. These micro-wind turbines make up 84% of all small and micro-wind turbines that have been installed in urban areas of the United Kingdom. The results unveiled various significant revelations regarding wind turbine noise. Initially, it was noted that the predominant amount of noise produced towards the ground happens when the blades descend, which is commonly referred to as blade fall. This indicates that phenomena such as convective amplification and trailing edge noise have a substantial impact on the production of noise. According to a study, micro-wind turbines with a maximum power output of 1.5 kW or less account for 84% of all small-scale wind turbines installed in urban areas of the United Kingdom [100].