## Abstract

Wind energy has proven to be one of the most promising resources to meet the challenges of rising clean energy demand and mitigate environmental pollution. The global new installation of wind turbines in 2022 was 77.6 GW, bringing the total installed capacity to 906 GW, documenting an astounding 9% growth in just one year (Lee and Zhao, 2023, Global Wind Report, GWEC. Global Wind Energy Council). Sizeable research continues to focus on improving wind energy conversion, safety, and capacity. However, funding allocations and research have not matched this sustained market growth observed over the last few decades. This is particularly the case for small-size wind turbines. We define small-scale wind turbines as those with an output power of 40 kW or less that can nonetheless be interconnected to provide larger power output. Thus, the paper focuses on small-scale horizontal-axis wind turbines (HAWT) with emphasis on current technology trends including data gathering, aerodynamic performance analysis of airfoils and rotors, as well as computational approaches. The paper also highlights the challenges associated with small-scale HAWTs thereby conjecturing about future research directions on the subject. The literature review suggests that small-scale HAWT wind turbines are suitable for harnessing energy in communities with limited resources where grid-supplied power is out of reach. The power coefficient of these turbines ranges from 0.2 to 0.45 which shows that it could greatly benefit from research, built on targeting these modest performance scales by using efficient airfoils, mixed airfoils, optimizing the blade geometry, shrouding the wind turbine rotor, using maximum power tracking control, etc. This review paper is an attempt to prioritize and layout strategies toward evaluating and enhancing the aerodynamic performance of small-scale HAWTs.

## 1 Introduction

In recent years [1], renewable energy conversion technologies have expanded due to the rise in the price of fossil fuels which comes at an ever greater upfront cost, continued energy supply which is stressed by industrialization and population growth, and the impact of hydrocarbons on the environment [2,3]. The growth is exhibited despite challenges with power production and quality, often unfavorable resource location, and cost issues [4]. The scholarly work is abundant on the integration of renewable energy systems (i.e., solar, wind, hydro, geothermal, and biomass) and their connections with modern power systems and technology assets to overcome the aforementioned challenges [5,6]. The abundant scholarly works are often scattered, randomly addressing niche areas without compartmentalized and prioritized research areas and trends that impact continued success in harnessing wind energy.

This paper is an effort to cast more light on current technology trends of horizontal-axis wind turbines (HAWTs) including data gathering, power coefficient analysis of airfoils and rotors, and computational techniques, and highlights the challenges to thereby define strategic research directions for the future.

### 1.1 Classification.

Wind turbines are commonly classified based on the axis of rotation, i.e., vertical axis and horizontal axis. Vertical-axis wind turbines (VAWT) have a vertical rotor axis almost perpendicular to the wind direction, Fig. 1. They can receive wind from any direction. They also do not require a yawing mechanism. The generator and gearbox are located on the ground, and their operating height is relatively low, making maintenance much easier. However, their poor starting characteristics and the lower efficiency of the turbines have restricted advances in VAWTs. Horizontal-axis wind turbines (HAWT) on the other hand operate with a horizontal rotor axis. These turbines may self-start and have a yaw mechanism, unlike VAWTs. Because these turbines are so reliant on wind direction, they are often operated at greater altitudes than the VAWT [7]. They produce more energy and have higher system efficiency because they have a relatively lower cut-in wind velocity and higher power coefficient. There is also more control over the angle of attack, which can be improved by variable blade pitching, leading to better system performance in varying wind conditions. HAWT is regarded as a reliable and widely used design [8]. Their popularity stems mainly from their superior efficiency compared to VAWTs. While the power coefficient of an efficient VAWT is typically under 40%, the maximum power coefficient of a modern HAWT has been observed to reach the 50% range [9].

Wind turbines can be further classified as micro, small, medium, large, and ultra-large wind turbines according to the size of the rotor [10,11]. The rotor diameter and power capacity are less than 3 meters and 50 W–2 kW in the case of micro wind turbines, from 3 to 12 m and 2–40 kW in the case of small wind turbines, 12–45 m and 40–999 kW in the case of medium wind turbines, 46–150 m and 1–10 MW in the case of large wind turbines and more than 150 meters and more than 10 MW in the case of ultra-large wind turbines.

### 1.2 Number of Blades.

The number of rotor blades in an HAWT varies depending on the application for which they are used and the wind regimes in which they are expected to work. Based on the number of blades, HAWT rotors can be classified as single-bladed, two-bladed, three-bladed, and multi-bladed. The most common commercial configurations of HAWT are the two- and three-bladed turbines. Two and three-bladed turbines can operate either upwind or downwind. The upwind configuration is the most commonly used because it produces less noise and reduces rotor fatigue (e.g., in blade, tower, nacelle) [12].

### 1.3 Justification.

In recent years, a significant amount of research has focused on large wind turbines. However, these turbines are not suitable to provide energy for off-grid and stand-alone applications. They are also not designed to operate efficiently at low wind conditions. Besides, their installation and maintenance costs are higher. Small wind turbines can overcome these limitations. Furthermore, small wind turbines can be installed at a residential scale [13] which offers the advantage of reducing transmission and distribution losses in energy supply to the consumers. They also have a lesser visual impact and produce less noise when compared to their large-scale counterparts. These wind turbines can provide a reliable source of electricity if they are appropriately sized and used under ideal conditions. They have the potential to become a socioeconomically viable source of energy for the majority of developing countries. Even in affluent countries, small-scale turbines can be a convenient power source in remote places where grid power is unavailable. However, the cost of producing small-scale wind turbines is critical to the future growth of the industry. Therefore, optimizing the efficiency, cost, and weight of small wind turbines is necessary for this promising technology to live up to its potential.

Many scholars have produced research papers providing an overview of wind turbines including their design and development. A few cases in point are recent advances in the future emerging technologies in the wind power sector from a European perspective [14], advancements in wind energy conversion systems for low-wind urban environments [15], small-scale wind turbines [16], diffuser-augmented horizontal-axis turbines [17], the evaluation of wind resources and wind turbine for urban application [18], the numerical modeling of turbulent flow [19], state of the art of wind tunnel tests for wind turbines [20], aerodynamic developments on small horizontal-axis wind turbine blade [21], aerodynamic shape optimization methods [22], and performance optimization techniques applied to wind turbines [23]. However, the aerodynamic performance of recently developed airfoils, issues surrounding the chord length, attack angles, computation approaches, and trends in innovation to improve small-scale horizontal-axis wind turbines are not well addressed.

Thus, this paper focuses on the trends in the innovation of small-scale HAWT technologies and the recent improvements in their aerodynamic performance, thereby addressing a lagging nexus in wind turbine research. This review was carried out by analyzing research papers from high-impact and reputable journals published in the last ten years to ensure that the previous years’ advances in research have been captured based on the collection, filter, and selection methods to search for relevant literature for this work. A total of 156 papers were reviewed out of 248 selected. As depicted in Fig. 2, the topic is researchable and it needs further investigation to reach its maturity stage. This paper gives a broad understanding of the state of the art in the development of small-scale wind turbines along with their aerodynamic performance.

Although the governing equations are independent of wind turbine blade sizes or geometries, the design and manufacturing, hence practical applications very much depend on the size and type of the wind turbine. For example, very large wind turbines are now restricted to offshore sites because of challenges in transportation and handling, and offshore turbines are exclusively HAWT types. It is hard to imagine some of the innovative technologies such as morphing blades on large blades, whereas they may be suitable for small- or medium-sized turbines. Hence, there is a need to review advances and trends in small-scale wind turbine technologies apart from the large blades.

## 2 Overview and Trends in Innovation of Wind Power Technologies

With established benefits of meeting the challenge of rising energy demand, with notable growth across the globe as a greenhouse gas (GHG) reduction strategy, and its availability in most regions of the world enabling a scale-up, the world can expect continued growth of wind energy conversion for decades to come, [25–27]. Indeed, the installed capacity of wind turbines has increased from year to year; 2022 saw global new wind power installations surpass 77.6 GW, bringing total installed capacity to 906 GW, a growth of 9% compared to 2021 as shown in Fig. 3. Among these, China leads global onshore and offshore wind development sharing 40% and 49% of the total installed capacity of onshore (841.9 GW) and offshore (64.3 GW) wind farms, respectively, Fig. 4 [1].

The quality of the wind, the height of the tower (hub height), the rotor diameter, and the management of operation and maintenance are all factors that influence the performance of wind turbines. They can generate power at wind speeds ranging from 3 m/s to 25 m/s. The common range of electricity generation is usually between 11 and 25 m/s [28]. As shown in Fig. 5, the power capacity and size of horizontal-axis wind turbines have increased consistently over the years.

The innovation of wind turbine technology has been varying in size, shape, and location over the years. Wind turbines come in a variety of blade shapes, but horizontal-axis turbines with three blades are the most prevalent for electricity generation, in both onshore and offshore scenarios [29].

Different types of horizontal-axis wind turbines are introduced for rural electrification and mitigating climate change [30–38] as shown in Fig. 6. Among these, (a) the onshore wind turbine is the earliest and most commonly used type of wind turbine. Furthermore, (b) offshore wind turbines, (c) highway-integrated wind turbines, (d) concentrated wind turbines, (e) cross-axis wind turbines, (f) multirotor wind turbines, and (g) bladeless wind turbines were introduced later.

## 3 Wind Energy Data Analysis Statistical Models for Wind Data Analysis

Statistical analysis can be used to estimate the energy output and evaluate the wind energy potential of a certain site. There may be no requirement for data analysis in terms of probability distributions and statistical techniques if time-series measurement if data are available at the appropriate location and height. When deemed necessary, to determine statistical distributions for mapping wind regimes, various probability functions such as the Weibull and Rayleigh distributions can be fitted to the field data to accurately establish wind regimes [39,40].

In the Weibull distribution, the variation of wind velocity is characterized by the probability density function (PDF) and cumulative distribution function (CDF). The probability density function *f*(*V*)) indicates the fraction of time (or probability) for which the wind is at a given velocity V. Cumulative distribution function describes the probability of the wind velocity, *V*, that is less than or equal to the wind velocity. Therefore, the cumulative distribution function, *F*(*V*) can be calculated by integrating the probability density function, *f*(*V*).

*f*is the probability of occurrence,

*V*is the wind speed in m/s,

*A*is the Weibull scale factor in m/s, and

*K*is the Weibull shape factor.

*V*

_{m}is the mean wind speed (m/s).

*K*approximated to 2. Thus, the PDF and CDF can be determined using Eqs. (4) and (5)

*f*is the frequency of occurrence,

*V*is the wind speed in m/s, and

*V*

_{m}is the mean wind speed (m/s).

### 3.1 Extrapolation of Wind Speed.

*V*

_{2}and

*V*

_{1}are the wind speeds at a height

*h*

_{2}and

*h*

_{1}in m/s, respectively, and

*α*is the wind shear exponent.

The measured wind speed can be extrapolated horizontally using the relative speedup relation.

*U*

_{2}and

*U*

_{1}are the wind speeds at the same height above ground level at the top of the hill and over the terrain upstream of the hill, respectively.

## 4 Aerodynamic Performance Evaluation of Airfoils

An airfoil of a turbine blade represents a wing-shaped structure designed to produce a favorable lift-to-drag ratio, (LDR), $ClCd$, similar to an aircraft wing. Thus, in order to get the closest possible ideal aerodynamic performance, airfoils with a high gliding LDR, low roughness sensitivity, and low noise character should be chosen. This is particularly important at the blade tips of HAWTs, where the drag coefficient is often lower than the lift coefficient. The airfoil LDR has a significant impact on output power [49–51]. The effects of airfoil type and blade size on the aerodynamic performance of three-bladed small-scale horizontal-axis wind turbines were investigated by several authors including Yossri et al. [52]. Computational fluid dynamics (CFD) method was used to evaluate the aerodynamic performance of NACA0012, NACA4412, NACA0015, and NACA4415 airfoils with three rotor diameter sizes of 0.5 m, 0.75 m, and 1 m at a free stream velocity of 4 m/s. The tip-speed ratios (TSRs) were 1.31, 1.96, and 2.62, respectively. The findings showed that the NACA 4412 airfoil yields the highest LDR of 26 as depicted in Fig. 7 and a power density of 2.465, 5.935, and 11.011 W/m^{2} for a 0.5 m, 0.75 m, and 1 m diameter wind turbine, respectively. Another study conducted by Mostafa et al. [53] revealed that the SG6043 wind turbine blade has better aerodynamic performance, producing 2.5% more power than the NACA4412 blade under the same operating conditions.

Suresh et al. [54] evaluated the aerodynamic performance of ten airfoils (i.e., Aquila, BW-3, E387, FX63-137, NACA 0012, NASA LS-043, RG-5, S1223, SD7080, and SG6043) to examine the suitability of low Reynolds (Re) number airfoils for the design of a 1.8-m, 2 -kW small-scale wind turbine at low wind conditions. The performance of these airfoils was analyzed at a different angle of attack (i.e., 0–20 deg) with an increment of 2 deg and a constant Re of 81,712 using Q-blade software. The blade was designed at a TSR of 6, using SD7080 airfoil based on blade element momentum (BEM) theory. A Numerical simulation was carried out to evaluate the LDR and power coefficient of the airfoils. The result revealed that the SG6043 airfoil had a maximum LDR of 56.40 at an 8-deg angle of attack. However, among these airfoils, the SD7080 airfoil was selected for the design of a wind turbine blade that can operate at low wind speed conditions due to its soft stall behavior in the 4- to 9-deg angle of attack (AOA).

Conversely, six novel airfoils (named NAF3929, NAF4420, NAF4423, NAF4923, NAF4924, and NAF5024) have been proposed by Chaudhry and Prakash [55] for the design of micro horizontal-axis wind turbine operating at low wind speed. Matlab and Xfoil programs were used to design, investigate, and optimize their aerodynamic performance at a Re of 100,000, AOA varying from 0–20 deg, and TSR of 3–10. Moreover, the effect of blade shape, solidity, rotor size, rotor configuration, wind speed, and blade pitch angle were investigated experimentally by the wind tunnel. The results showed the maximum LDR of 69 was obtained by the NAF4924 airfoil at 5 deg and the NAF4923 airfoil at 6 deg AOA. It was also seen that the aerodynamic performance of NAF-Series airfoils was better than SG6043 and NACA4415 airfoils at a low Re and NAF 4923 airfoil was the best candidate airfoil among others.

Similarly, Yeboah et al. [56] studied the aerodynamic performance of three EYO-Series low Re airfoils (Re = 100,000–500,000) for small wind turbine blades. The results indicated that when Re increased, all three airfoils aerodynamic performance was improved. At Re = 500,000, EYO7-8 produced the greatest LDR of 170 at a 4 deg AOA, but EYO9-8 showed a steepest stall angle of 15 deg for all Re values except Re = 200,000, indicating progressive stall behavior. These airfoils performed well in the drag bucket, showing favorable lift-drag characteristics. In summary, the study reveals that the EYO-Series airfoils provide steady lift performance with little variations, making them desirable for low Re applications in small wind turbine blade design.

Based on the surveyed literature, it was concluded that Xfoil and CFD were mostly used for predicting the performance of airfoils. It was observed that among the different types of airfoils, EYO-series, S-series, NAF-series, and SG-series airfoils had a better aerodynamic performance than others, as presented in Table 1.

## 5 Aerodynamic Performance Evaluation of a Wind Turbine Blade

The earlier section presents the aerodynamic performance of airfoils tailored to HAWT. Below we present the aerodynamic performance of a wind turbine blade, which also can be evaluated using analytical, computational, and experimental methods.

### 5.1 Analytical Methods.

The analytical method involves solving the performance parameters of a wind turbine blade using mathematical approaches after determining the aerodynamic forces applied to a wind turbine blade. The forces are calculated by resolving the velocity triangles of the turbine blade. The design and performance prediction of wind turbine blades has long been done using the BEM theory, first proposed by Glauert in 1935.

This method is based on a combination of blade element theory and momentum theory. The BEM approach is based on the assumptions of steady, inviscid, incompressible flow without radial and circumferential dependency and is derived from the principles of conservation of mass and momentum [58]. It solves the aerodynamic forces acting on the blade along with different aerodynamic parameters.

The main aerodynamic parameters and forces affecting the blade are presented in Fig. 8.

*dF*

_{n}) and tangential force (

*dF*

_{t}) acting on the blade can be calculated from axial momentum and angular momentum as follows [60]:

*U*,

*ρ*, and Ω are the free stream velocity, air density, and angular speed.

*U*

_{rel}is the relative wind speed

*C*

_{p}as

*C*

_{T}

*ρ*,

*A*, and

*V*are the air density, rotor area, and wind velocity, respectively.

*C*

_{p,r}) and thrust coefficient (

*C*

_{t,r}) can be calculated as follows:

*a*and

*a*′ are respectively the axial and tangential induction factors,

*C*

_{l}is the lift coefficient of the selected airfoil,

*ϕ*is the flow angle,

*λ*

_{r}is the local TSR for each blade element which can be calculated from the design TSR

*λ*, blade length

*R*, and the local radius

*r*using Eq. (15),

*C*is the chord length, and

*σ*is the local solidity determined using Eq. (16).

*F*, is introduced by Prandtl to reflect the lift force reduction due to airflow around the blade tip

*ϕ*is the inlet flow angle,

*α*is the AOA,

*β*is the blade pitch angle,

*a*is the axial induction factor,

*a*′ is the angular induction factor,

*λ*

_{r}and

*λ*

_{d}are the local and design TSR,

*C*is the chord length,

*r*is the sectional blade radius,

*B*is the number of blades,

*n*is the number of blade sections,

*C*

_{ld}is the design lift coefficient,

*C*

_{dd}is the design drag coefficient,

*V*

_{d}is the design flow velocity,

*V*

_{w}is the local angular speed,

*V*

_{rel}is the local resultant air velocity,

*C*

_{n}is the normal force coefficient of a blade, $m\u02d9b$ is the mass flow through the bare wind turbine, and $m\u02d9d$ is the mass flow through the ducted wind turbine.

Although BEM has been applied to the design and performance prediction of wind turbine blades, the theory fails to give an accurate performance in the region where stalls arise at a low TSR and high AOA. Furthermore, because of the complexity brought on by the creation of wake on the blade surface, realistic data at high angles in these locations are either not readily accessible or shouldn't be relied upon too much [61]. Due to the limitations of BEM theory, several approaches that are helpful in wind turbine blade design and performance prediction have been developed by researchers. Some scholars considered the tip loss correction factor, whereas others did not take it into account when they evaluated the chord length, twist angle distribution, and the power coefficient of the rotor. For an ideal rotor, the performance of the wind turbine has been commonly predicted without considering the wake, but different corrective measures such as the Prandtl tip loss correction factor [62] and other tip loss correction models (i.e., The Tip Loss Correction of Glauert, The Tip Loss Correction of Wilson and Lissaman, The Tip Loss Correction of de Vries, Shen Tip Loss Correction Model, Sun, etc.) have been introduced to modify the BEM theory model [63]. Also, Sun et al. [64] improved the blade element momentum theory by considering the influence of pressure drop due to wake rotation (WR) and the effect of radial velocity at the rotor disc by combining Glauert’s tip correction and Shen's tip correction models. Moreover, Zhenye and Buhl’s empirical corrections, skewed wake correction, and 3D correction of Snel et al. [65] are important to make the BEM method applicable in wind turbine design [66,67]. Tip loss correction considers the impact created by vortices that are shed from the blade tips into the wake. The vortex shed near the hub is considered by hub loss correction. The turbulent wake phenomena are accounted for by Glauert and Buhl's empirical correction. While Snel et al.’s 3D's correction accounts for the lift augmentation brought on by rotation, skewed wake correction takes into consideration the influence made by wind turbines when operating at yaw angles. Many researchers have designed and analyzed the horizontal-axis wind turbine by considering the above corrections individually. However, all corrections must be credited during the analysis to get an accurate and reliable result. So, it needs further modification of the existing BEM method model by considering all the listed corrections.

Some of the approaches that were employed for the design and performance analysis of a horizontal-axis wind turbine are depicted in Table 1.

Table 2 also shows that most of the scholars used different numerical approaches for the design and performance evaluation of a HAWT, and research on comparative analysis of these approaches is limited [82]. Besides, small-scale horizontal-axis wind turbine (SSHAWT) blade designs have been conducted using computational and experimental techniques [83] which are discussed below.

No. | Flow angle, $\varphi (deg)$ and sectional flow angle, ϕ_{i} | Chord length, C (m) | References |
---|---|---|---|

1 | $\varphi i=90\u221223tan\u221211\lambda r$, $\varphi =tan\u22121(\lambda r(1+a\u2032)1\u2212a)$ | $C=8\pi rcos\varphi 3B\lambda r$ | [68] |

2 | $\varphi i=tan\u22121(23\lambda r)$, $\varphi =tan\u22121(1\u2212a\lambda r(1+a\u2032))$ | $C=8\pi rBCld(sin\varphi 3\lambda r)$, without WR | [59,69] |

3 | $\varphi i=23tan\u221211\lambda r$, $\varphi =tan\u22121(\lambda r(1+a\u2032)1\u2212a)$ | $C=8\pi rBCld(1\u2212cos\varphi )$, with WR | [59,70] |

4 | $\varphi i=90\u221223tan\u221211\lambda r$, $\varphi =tan\u22121(1\u2212a\lambda r(1\u2212a\u2032))$ | $C=8\pi rBCld(sin\varphi 3\lambda r)$, without WR | [71] |

5 | $\varphi =tan\u22121(1\u2212a\lambda r(1+a\u2032))$ | $C=8\pi raF(1\u2212aFsin2\varphi )BCld(1\u2212a)2cos\varphi $ | [72] |

6 | $\varphi i=23tan\u22121(23\lambda r)$ | $C=16\pi rBCldsin2(13tan\u221211\lambda r)$, with WR | [73] |

7 | $\varphi i=23tan\u22121(R\lambda r)$ | $C=16\pi rBCldsin2(23tan\u22121R\lambda r)$ | [74] |

8 | $\varphi =23tan\u22121(23\lambda r)$ | $C=16\pi r9BCld(Vd\lambda Vrel),Vrel=Vw2+Vd2$ | [75] |

9 | $\varphi =23tan\u22121(23\lambda r)$ | $C=8\pi rBCld(1\u2212cos\varphi )$ | [76] |

10 | $\varphi i=23tan\u22121(1\lambda r)$ | $C=16\pi R29BCld\lambda 2r$, with WR | [77] |

11 | $\varphi i=tan\u22121(23\lambda r)$ | $C=8arsin2\varphi (1\u2212a)BCn$, $Cn=Cldcos\varphi \u2212Cddsin\varphi $ | [78] |

12 | $ai=0or13$, $ai\u2032=0,\varphi =tan\u22121(1\u2212a\lambda r(1+a\u2032))$ | $C=8\pi rBCld(1\u2212cos\varphi )$ | [59,78] |

13 | $\varphi i=23tan\u22121(1\lambda r)$ | $C=8\pi rFsin\varphi (cos\varphi \u2212\lambda rsin\varphi )BCld(sin\varphi \u2212\lambda rcos\varphi )$ | |

$F=2\pi cos\u22121exp(\u2212B(R\u2212r)2rsin\varphi )$ | [79] | ||

14 | $\varphi i=23tan\u22121(1\lambda r)$ | $C=8\pi rFsin\varphi (1\u2212\lambda rtan\varphi )BCld(tan\varphi +\lambda r)$ | [80] |

15 | $\varphi =23tan\u22121(1\lambda d(rR))$ | $C=8\pi rBnCld(1+cos\varphi )$ | [81] |

No. | Flow angle, $\varphi (deg)$ and sectional flow angle, ϕ_{i} | Chord length, C (m) | References |
---|---|---|---|

1 | $\varphi i=90\u221223tan\u221211\lambda r$, $\varphi =tan\u22121(\lambda r(1+a\u2032)1\u2212a)$ | $C=8\pi rcos\varphi 3B\lambda r$ | [68] |

2 | $\varphi i=tan\u22121(23\lambda r)$, $\varphi =tan\u22121(1\u2212a\lambda r(1+a\u2032))$ | $C=8\pi rBCld(sin\varphi 3\lambda r)$, without WR | [59,69] |

3 | $\varphi i=23tan\u221211\lambda r$, $\varphi =tan\u22121(\lambda r(1+a\u2032)1\u2212a)$ | $C=8\pi rBCld(1\u2212cos\varphi )$, with WR | [59,70] |

4 | $\varphi i=90\u221223tan\u221211\lambda r$, $\varphi =tan\u22121(1\u2212a\lambda r(1\u2212a\u2032))$ | $C=8\pi rBCld(sin\varphi 3\lambda r)$, without WR | [71] |

5 | $\varphi =tan\u22121(1\u2212a\lambda r(1+a\u2032))$ | $C=8\pi raF(1\u2212aFsin2\varphi )BCld(1\u2212a)2cos\varphi $ | [72] |

6 | $\varphi i=23tan\u22121(23\lambda r)$ | $C=16\pi rBCldsin2(13tan\u221211\lambda r)$, with WR | [73] |

7 | $\varphi i=23tan\u22121(R\lambda r)$ | $C=16\pi rBCldsin2(23tan\u22121R\lambda r)$ | [74] |

8 | $\varphi =23tan\u22121(23\lambda r)$ | $C=16\pi r9BCld(Vd\lambda Vrel),Vrel=Vw2+Vd2$ | [75] |

9 | $\varphi =23tan\u22121(23\lambda r)$ | $C=8\pi rBCld(1\u2212cos\varphi )$ | [76] |

10 | $\varphi i=23tan\u22121(1\lambda r)$ | $C=16\pi R29BCld\lambda 2r$, with WR | [77] |

11 | $\varphi i=tan\u22121(23\lambda r)$ | $C=8arsin2\varphi (1\u2212a)BCn$, $Cn=Cldcos\varphi \u2212Cddsin\varphi $ | [78] |

12 | $ai=0or13$, $ai\u2032=0,\varphi =tan\u22121(1\u2212a\lambda r(1+a\u2032))$ | $C=8\pi rBCld(1\u2212cos\varphi )$ | [59,78] |

13 | $\varphi i=23tan\u22121(1\lambda r)$ | $C=8\pi rFsin\varphi (cos\varphi \u2212\lambda rsin\varphi )BCld(sin\varphi \u2212\lambda rcos\varphi )$ | |

$F=2\pi cos\u22121exp(\u2212B(R\u2212r)2rsin\varphi )$ | [79] | ||

14 | $\varphi i=23tan\u22121(1\lambda r)$ | $C=8\pi rFsin\varphi (1\u2212\lambda rtan\varphi )BCld(tan\varphi +\lambda r)$ | [80] |

15 | $\varphi =23tan\u22121(1\lambda d(rR))$ | $C=8\pi rBnCld(1+cos\varphi )$ | [81] |

### 5.2 Computational Fluid Dynamics Methods.

The computational method involves the use of numerical tools and algorithms to calculate the fluid flow over wind turbines employing such tools as ANSYS Fluent, ANSYS CFX, SOLIDWORKS, WindSim, Q-blade, ASHE, COMSOL Multiphysics, openfoam, etc. When comparing the three approaches, i.e., analytical, computational, and experimental methods, CFD seems to be the most commonly used tool to determine the performance characteristics of wind turbine blades [84–86]. CFD uses different turbulence models such as Spalart–Allmaras, S–A model, and Realizable and shear stress transport (SST) to visualize the flow over an airfoil and wind turbine blade [87].

Younoussi and Etaouil [88] used only CFD to investigate the aerodynamic properties of a newly built three-bladed HAWT. The Ampair300 wind turbine's blade geometry was created utilizing an upgraded BEM technique. To solve the steady-state 3D Reynolds-averaged Navier–Stokes (RANS) equations, the SST transition turbulence model was used. After that, the operating conditions of the two turbines were compared by monitoring the pressure coefficient, pressure contours, and velocity vectors at five different radial points. The impacts of the TSR on turbine efficiency and flow behavior on the blade and in the near wake were investigated. The results showed that at a TSR of 4, a maximum power coefficient of 0.22 was attained at a wind speed of 8 m/s, indicating a reduction in separated flow zones as TSR increased. Lowering the pitch angle decreased blade efficiency at low TSRs but increased it at high TSRs, achieving a 0.6 power coefficient at a pitch angle of 0 deg and TSR 7. At this wind speed and TSRs greater than 4, the newly developed turbine with a 0 pitch outperformed the Ampair300.

On the other hand, Monteiro et al. [89] predicted the performance of a 1.2-m horizontal-axis wind turbine at different tip speed ratios (λ) and wind speeds of 3.0 m/s, 3.7 m/s, 4.4 m/s, 5.5 m/s, 7.2 m/s, and 7.7 m/s using WT_Perf and BEM codes. The experiment was carried out in a 2.2-m open test chamber, or closed-circuit wind tunnel to verify the numerical results. It was discovered that the maximum power coefficient (*Cp*) varied dramatically with wind speed, with *Cp* equal to 0.32 at 3.0 m/s and 0.40 at 7.7 m/s. The maximum power coefficient's associated TSR was found to vary inversely with wind speed, from *λ* = 6.5 at 3.0 m/s to *λ* = 4.8 at 7.7 m/s. Similarly, performance analysis and design of a three-blade 5-kW small horizontal-axis wind turbine were conducted by [71] using BEM theory. A matlab code was developed to design and optimize the turbine. SG6043 and NACA 4412 airfoils were used for designing the blade at 6 and 9 m/s. A comparative study on the performance of the two designed blades was conducted by determining the coefficient of power and power output. The result revealed that the SG6043 wind turbine blade produced 5.125-kW power with a power coefficient of 0.51651 at a wind speed of 6 m/s and a blade length of 4.455 m. Whereas NACA 4412 blade produces 5-kW power with a power coefficient of 0.50659 at the same operating condition. For the same power output, the blade length of the blades was reduced to 2.424 m at 9 m/s. Generally, it was seen that the SG6043 wind turbine blade has better aerodynamic performance, and it produced 2.5% more power than the NACA4412 blade under the same operating conditions.

### 5.3 Experimental Methods.

The experimental approach involves physical testing and measurements in controlled conditions, wind tunnel tests, and in real-world conditions, field tests to validate the analytical and computational results and evaluate the performance of wind turbines. These methods are the most accurate, but they can also be the most expensive and time-consuming [90]. The performance characteristics of a wind turbine at various operating conditions can be evaluated by using different apparatuses such as a wind tunnel test, particle image velocimetry, smoke flow visualization, laser sheet visualization, hot-wire anemometer, etc. [91–93]. Many experimental researches were carried out to evaluate the performance of small wind turbines.

For example, Jackson and Amano [94] conducted an experimental study and simulation of a SHAWT. In their experimental work, hot-wire anemometer was utilized to measure the velocity profiles at different wake locations behind the turbine. Moreover, the rigid body motion (RBM) CFD simulations were done to predict the velocity profiles at wake locations matching the experimental work and showed better agreement with the experiment results. Treuren [95] used a wind tunnel test to evaluate the performance of small wind turbines (SWTs) in low Re circumstances. The author emphasized how understanding the system performance through wind tunnel experiments would allow the designer to scale the results more properly to massive wind turbines.

Bastankhah and Porté-Agel [96] also experimented to study the interaction of a 15-cm horizontal-axis miniature wind turbine with an incoming boundary layer flow. Similarly, Le et al. [97] used a wind tunnel test to evaluate the aerodynamic performance of a novel dual-stage counter-rotating SWT at various wind speeds and verify the CFD model.

Józwik et al. [98] conducted experiments to forecast and validate the performance of the improved SWT blade shape. They emphasized the link between BEM numerical tools and small-scale tests, which allowed for an efficient iterative path of prototype creation. The significance of simulating real-world wind conditions in wind tunnels, as well as the implications of their results for SWTs, was also explored.

Hsiao et al. [99] tested the performance of three various HAWTs (i.e., optimum blade shape, untapered and optimum twist blade, and untapered and untwisted blade) in a wind tunnel and performed a numerical simulation. The experimental result showed that the highest power coefficient of 0.428 was obtained in the first two turbines. The observed power coefficients of the three blades were accurately recreated in CFD simulations using the k-SST turbulence model. This implies that CFD can properly forecast the performance of HAWT blades. Plaza et al. [100] also compared the findings of BEM theory, CFD, and observations to perform an aerodynamic study of the MEXICO wind turbine rotor. At low wind velocities, the results show that BEM computations beat CFD estimations. They fail, however, at greater velocities due to separated flow conditions. Blade tip loss and 3D effects contribute to calculation mistakes, particularly in the BEM theory. Additionally, the performance of a three-bladed wind turbine at a wind speed of 7, 10, 15, 20, and 25 m/s was evaluated by Dewangan et al. [101] using experimental approaches and CFD. In the CFD analysis, Spalart Allmaras's single equation model was utilized to solve the turbulent flow problems. The result showed that 14-kW power can be produced by the turbine at a wind speed of 25 m/s and the computational results were matched with the experimental works.

Additionally, a summary of literature reviews on the design and performance analysis of wind turbines is presented in Table 3.

Airfoil used | Rotor diameter, m | Number of blades | λ_{Design} | Method | C_{p}, max | Authors |
---|---|---|---|---|---|---|

AFN2016 | 2.28 m | 3 | 7 | Experiment and CFD | 0.24 | [102] |

NACAxx10 | 0.3 | 8 | 0.1 | Euler's turbomachinery theorem and experiment | 0.31 | [103] |

SG6042 | 3 | 3 | ||||

NACA0012 | 1.2 m | 2 | 10 | Nonlinear vortex lattice method (NVLM) | [104] | |

S826 | 0.894 m | 3 | 6 | 0.45 | ||

SG6043 | 7.25 m | 3 | 7.5 | BEM | 0.47 | [105] |

NACA 65415 | 2 m | 3 | 8 | CFD | 0.47 | [106] |

NACA 64421 | 7 | 0.54 |

Airfoil used | Rotor diameter, m | Number of blades | λ_{Design} | Method | C_{p}, max | Authors |
---|---|---|---|---|---|---|

AFN2016 | 2.28 m | 3 | 7 | Experiment and CFD | 0.24 | [102] |

NACAxx10 | 0.3 | 8 | 0.1 | Euler's turbomachinery theorem and experiment | 0.31 | [103] |

SG6042 | 3 | 3 | ||||

NACA0012 | 1.2 m | 2 | 10 | Nonlinear vortex lattice method (NVLM) | [104] | |

S826 | 0.894 m | 3 | 6 | 0.45 | ||

SG6043 | 7.25 m | 3 | 7.5 | BEM | 0.47 | [105] |

NACA 65415 | 2 m | 3 | 8 | CFD | 0.47 | [106] |

NACA 64421 | 7 | 0.54 |

Based on the surveyed literature, it was concluded that BEM, CFD, and wind tunnel tests were employed for predicting the performance of a wind turbine. Also, among numerical models, the improved BEM model gave reliable results. It was also noted that high-performance airfoils were used to maximize the power output of the wind turbines.

## 6 Techniques for Performance Improvements of a HAWT

The performance of any machine is maximum when it is operated at optimum conditions. Similarly, wind turbines can produce maximum power when they are operated at optimum rotational speed for a specified wind speed. Moreover, the performance of the wind turbine can be affected by the number of wind turbine blades, blade shape, wind speed, blade pitch angle, and control mechanism.

### 6.1 The Number of Blades.

The number of blades is one of the factors that impact how well wind turbines perform. Wang and Chen [107] used CFD to numerically examine the impact of blade numbers 2, 4, 6, and 8 on a 1.4 m ducted wind turbine's performance at an inflow speed of 12 m/s. The *k*–*e* turbulence model was used for the numerical calculations. It was observed that adding more blades results in a higher starting torque and a slower cut-in speed. But, more blades result in more obstruction and slower blade entrance velocity, which reduces the power coefficient of the rotor. In contrast, the study conducted by [108,109] found that increasing the blade number enhances the aerodynamic performance of the wind turbine while Shintake [110] notes that the performance of a horizontal-axis wind turbine blade increased with an increase of the blade number from 1 to 3 and decreased with an increase of the blade number from 3 to 5.

The effect of blade number on the aerodynamic performance of a 2-m small-scale horizontal-axis wind turbine was investigated experimentally and numerically by Eltayesh et al. [111]. The study was analyzed by installing an experimental setup of wind turbine rotors (ANEMOS 455 model) with three-, five-, and six-bladed wind turbines at a constant pitch angle, different velocities (i.e., 6, 7, and 8 m/s), and tip speed ratios, in the closed-circuit, open test section wind tunnel. The turbine rotational speed was measured and monitored using an optical sensor model XUB5APBNL2. The current was measured using MCR-S-1-5 current transducers, while voltage was obtained using LV 25-P/Sp5 transducers. The study also used ANSYS Fluent to study the effect of blade number on the power and thrust coefficient of the selected turbines. The numerical calculation was performed using the SST *k*–*ω* turbulence. The Moving Reference Frame (MRF) strategy was used to model the rotating wind turbine. The findings showed that compared to the five-bladed and six-bladed wind turbines, the performance of a three-bladed wind turbine increased by 2% and 4%, respectively. Also, there was a good agreement between the calculated and measured values. Similarly, in the studies by Refs. [112–114], it was reported that a three-bladed horizontal-axis wind turbine rotor had a better power coefficient than the single-bladed and two-bladed wind turbines.

Generally, the literature showed that many researchers have studied the performance of single-bladed, two-bladed, three-bladed, four-bladed, five-bladed, and multi-bladed wind turbines. The consensus is that a three-bladed wind turbine has a better power coefficient and is the most suitable for electric power generation.

### 6.2 Optimizing Blade Geometry.

Another strategy that is used to improve the performance of a wind turbine is optimization of the turbine blade geometry. Many researchers have been actively working on enhancing the aerodynamic performance of the wind turbine by optimizing the wind turbine blade. For example, Lee et al. [115] twisted an SD8000 airfoil-based wind turbine blade to examine the aerodynamic performance of the twisted and baseline blade using experimental and numerical approaches. The experimental and numerical results depicted that the power coefficient of the blade was enhanced by more than 50% after being twisted. It was also noted that the numerical results had a better agreement with the experimental results. Similarly, Thangavelu et al. [116] twisted a wind turbine blade as shown in Fig. 9, to evaluate its aeroelastic performance at different yaw angles (i.e., 0 deg, 10 deg, 30 deg, and 60 deg) and a wind speed of 10 m/s using ANSYS Fluent and finite element analysis. NACA4412-43 airfoil was selected for designing the blade. The blade was starting to sweep at a 14.55-m distance from the root, and it sweeps to 2.8 m in the *y*-direction at the blade tip. The findings revealed that the maximum and minimum power production difference between unswept and swept blades is 8% and 6% at a yaw angle of 60 deg and 10 deg, respectively. Also, the total maximum and minimum deformation difference between unswept and swept blades was found to be 17% and 0% at a yaw angle of 60 deg and 30 deg, respectively. Under yaw conditions, swept blades have greater aeroelastic performance than unswept blades in terms of higher rotor power and lesser deformation.

In some interesting scenarios, [117] some exotic geometries were investigated. One such case is the lotus-inspired horizontal-axis wind turbine blade that mimics the aerodynamic structure of the Nelumbo Nucifera flower (sacred lotus) as depicted in Fig. 10. The model of the blade was generated with a length of 1.5 m, 0.334 m in width, and binding angle of 20 deg between the tip and the middle of the blade, and a curvature of 60 deg between the leading edge and the trailing edge. The performance of the blade was examined at 12 m/s using ANSYS 2020 R1. Models of the lotus-inspired NACA2412 blades were fabricated using a 3D printer and tested in a lab by connecting them to a 9V-DC generator. According to the results obtained, it was observed that the performance of the blade can be increased by applying the structure design of the lotus flower and modifying the blade shape. Moreover, the experimental results showed that the performance of the lotus blade was improved by 31.7% compared to the NACA2412 airfoil.

Another strategy employed to improve the performance of a wind turbine is morphing the wind turbine blade. It involves the ability to dynamically adjust the shape, twist, or curvature of the blade in response to varying wind conditions. This helps to optimize the blade geometry in real-time, improving energy-capturing efficiency, reducing loads, enhancing aerodynamic performance, and enabling safe operation under various wind conditions. Several research articles focused on developing innovative morphing technologies for wind turbine blades. For example, Wang et al. [118] developed a simplified morphing blade to improve the power capture capacity of wind turbines. Similarly, the study by Refs. [119–125] also revealed that morphing a wind turbine blade is an innovative technology used for enhancing the efficiency of wind turbines over a wide range of wind speeds and angles of attack. In addition to improving the performance of wind turbines, morphing wind turbine blades is used to alleviate loads in wind turbine blades [126–128].

On the other hand, other authors attempted to modify the shape of the wind turbine blade by using mixed airfoils for the design of a wind turbine blade to improve its aerodynamic performance. For example, Refan et al. [129] used Wortmann FX 63 137 airfoils at the tip region and NACA 6515 at the root region of the blade for the design of the wind turbine. The power curve of the wind turbine was determined experimentally over a wide range of wind speeds. The aerodynamic performance curve showed that for low-speed tests, a maximum power of 470 W was obtained at 9 m/s and a high cut-in speed of the rotor at about 5 m/s.

Another study by Imane and Abdelouahed [130] also found that the turbine that was built from two profiles (i.e., NACA63421 and NACA63215 airfoils) had a better trust coefficient and torque than the uniform turbine (i.e., NACA63421). Similarly, Bhattacharjee et al. [131] improved the power coefficient of the turbine from 0.50 to 0.533 by constructing the blade with three profiles (i.e., Circular Foil 0.5, DU99W405LM, and NACA64618).

### 6.3 Shrouding the Wind Turbine Rotor.

Several studies have suggested that shrouding the wind turbine enhances the extracted power from the turbine by accelerating the wind velocity of the air that passes through the rotor plane [133–136]. As a result, the need for shrouded wind turbines has increased for rural and urban electrification. Careful attention to sizing the length-to-diameter ratio of a diffuser, nozzle length, exit area ratio, brim height, throat length, diffuser height, and nozzle height must be given while designing a shroud. The 2D geometry of the shrouded wind turbine is presented in Fig. 12.

Modern wind turbines do not operate at their maximum theoretical power coefficient (i.e., *C*_{p} = 0.593) due to wake losses, tip vortex losses, and other factors. To reduce such losses and to improve their performance, various innovative techniques are explored continually. One such approach is shrouding the turbine, which involves the addition of a protective cover around the rotor blades as shown in Fig. 13.

A shrouded duct with a brim on the outer exit edge has been creatively designed by Ohya et al. [137]. By purposefully causing flow separation, the linked flange eventually leads to a pressure drop below the diffuser brim. The area of low pressure immediately behind the turbine rotor increases the rate of mass flow and incoming velocity through the rotor plane. Then, many research studies were carried out to enhance the power production capacity and power coefficient of a ducted wind turbine.

For example, the performance of a 1.2-m diameter three-bladed shrouded wind turbine at low wind speed conditions was investigated experimentally by Setyawan et al. [139]. The test was carried out in a 3.1 m × 2.1 m × 2.5 m sized wind tunnel by varying the wind speed from 1 m/s to 5 m/s with an increment of 0.5 m/s. The study was also performed at 2 different diffuser geometries (i.e., without an inlet shroud of L/D = 0.25 and with an inlet shroud of L/D = 0.39 to analyze the effect of shroud geometry on the performance of wind turbines. NACA6412 airfoil was used to design a blade. The rotational speed of a rotor was measured by a tachometer whereas the electrical power was measured by a digital multimeter. The findings revealed that the diffuser without an inlet shroud increases the wind speed by up to 10% and electrical power by up to 20.5%. Whereas, the diffuser with an inlet shroud increases the wind speed by up to 13.3% and electrical power by up to 41.1%. However, the study did not include the effect of the length-to-diameter ratio and exit area ratio of the diffuser.

A theoretical analysis of a 0.7-m diameter shrouded horizontal-axis wind turbine was conducted by Khamlaj et al. [140]. The study was done by incorporating a converging-diverging nozzle into the system. Conservation of mass and momentum laws were applied to determine the optimum power production, power coefficient, and augmentation ratios. The result shows that the power coefficient of a shrouded wind turbine is three times greater than the maximum theoretical power coefficient of the bare turbine. The numerical results were validated by experimental and CFD results, and it was seen that there was good agreement between the results. Also, the efficiency of a nozzle, the efficiency of a diffuser, and the backup pressure coefficient had a significant impact on the performance of shrouded wind turbines.

Performance analysis of open and ducted wind turbines was done by Bontempo and Manna [141]. A new semi-analytical actuator disk model was applied to both cases to determine their aerodynamic performance. The study was conducted by varying the rotor load distribution. Whereas, Aloui et al. [142] analyzed the effect of flange height and flange height-to-diffuser inlet diameter ratio. The results obtained by Bontempo and Manna show that a properly ducted wind turbine can swallow a higher mass flow rate than an open turbine with the same rotor load. Whereas, Aloui et al. describe that the optimum extracted power of a shrouded wind turbine is achieved at a flange height to diffuser inlet diameter ratio of 0.1.

Mansour and Meskinkhoda [143] analyzed the flow field around the flanged diffuser and a number of design elements, such as the diffuser opening angle, flange height, and the curved inlet. It was discovered that the flange height and opening angle have a significant impact on the type of flow separation, whereas the curved inlet has increased velocity. Whereas, El-Zahaby et al. [144] studied the flow field over a diffuser-augmented wind turbine with a flange angle ranging from $\u221225degto25deg$ keeping other parameters constant, and it was shown that the optimum flange angle for accelerating flow at the diffuser entrance was 15 deg. Similarly, the study in Ref. [54] presented the effect of brim height (*h* = 0.1 D, 0.3 D, 0.5 D) and diffuser angle $(\alpha =0deg:2deg:30deg)$ on the performance of a 3 kW diffuser-augmented wind turbine. In their study, it was depicted that the maximum velocity in the rotor plane exists for the brim with $hD=0.3$ and $\alpha =6deg$.

Hashem et al. [145] employed high-fidelity CFD simulations to characterize the aerodynamic performance of a wind turbine by solving URANS equations with the aid of SST *k* − *ω* model. Computational analysis was done to determine how the geometry of the wind lens (i.e., cycloid type, circular type, and linear type) affects the performance of the wind turbine. Also, the effect of flange height-to-diameter ratio $(hD=0.05to0.2)$, length-to-diameter ratio $(LN+LDD=0.1to0.371)$ and area ratio $(AexitAthroat=1.119to1.555)$ of the cycloid type wind-lens shape on the flow field was analyzed at Re of 5 × 10^{5}, wind speed of 8 m/s, and a rotor-swept area of 0.785 m^{2}. The result revealed that the cycloid type wind lens had a maximum power coefficient (*C*_{p} = 0.92) than the circular type and linear type lenses and it boosts the power coefficient by 58% (from *C*_{p} = 0.39 to 0.92) when compared to bare wind turbines with the same rotor-swept area. Also, the power coefficient (*C*_{p}) was increased with the h/D and length-to-diameter ratio.

In addition to the above techniques, the aerodynamic performance of a wind turbine could be enhanced by optimizing the blade pitch angle, tip speed ratio, and rotor speed, and protecting the erosion of the blade [146–148]. For example, the effect of blade number, shape, shroud, rotor speed, and blade pitch angle on the performance of a wind turbine was presented [149]. Garrad Hassan (GH) bladed software was used to model wind turbines and analyze their performance. In this study, the effect of rotor speed at 1250, 1500, and 2000 rpm and blade pitch angle from 0 deg to 12 deg with an increment of 2 deg at various wind speeds have been examined to look at how the output power is adjusted and optimized. The findings showed that the power output of a wind turbine varies with the rotor speed. Hence, a suitable rotor speed control should be designed to track the maximum power coefficient under different requirements of the system. Also, a small variation of pitch angle affects the performance of a wind turbine along different TSR and wind speeds as shown in Fig. 14, and high blade pitch angles are recommended during high wind speed to protect the turbine from damage.

On the other hand, Wang [150] studied the impact of leading-edge erosion on wind turbine blades at 7, 10, 15, and 20 m/s incoming wind speeds using CFD. The study considered pitting erosion (*h* = 1 mm, *x* = 10% c) and three levels of delamination (*h*_{1} = 0.5 mm, *x* = 10% c, *h*_{2} = 1 mm, *x* = 10% c, and *h*_{3} = 3 mm, *x* = 10% c). The *SST k* − *ω* turbulence model was adopted throughout the study. The results showed that at a small inflow velocity, light erosion (pits erosion) can lead to a 2.95% power loss, and severe erosion (delamination) can cause a power loss of 13.69%. This value increases to 31.94% and 70.64% when the inflow velocity grows from 7 m/s to 20 m/s. The flow separation, tangential force coefficient, normal force coefficient, and wind turbine power production are all affected by the degree of leading-edge erosion. At 15 m/s, leading-edge erosion has the largest impact on wind turbine aerodynamics, with a maximum loss in power output of up to 73.26%. Additionally, scholars used various techniques to enhance the performance of wind turbines as summarized in Table 4.

Airfoil type | Method/technique | Main findings | Author |
---|---|---|---|

S809 | By vanquishing stall with active multi-air jet blowing | Torque increases about two times to the baseline blade | [151] |

S822 | Using an automated two-dimensional airfoil shape optimization procedure | Optimizing the blade shape enhances blade performance | [152] |

NACA23012C | By generating a passive jet from the pressure side to the suction side | The optimized geometry increases the CL/CD by 32.0% from the initial design without passive jet control | [153] |

— | Using Maximum Power Point Tracking (MPPT) Control based on perturb and observe (PO) algorithm method | The installation of the MPPT controller increased the output power by 135.62%, with an average power increase of 50.77% | [154] |

— | By augmenting the wind turbine with a diffuser | The power production was increased | [155] |

E216 | By curving the blade | The power coefficient increases up to 13% | [156] |

Airfoil type | Method/technique | Main findings | Author |
---|---|---|---|

S809 | By vanquishing stall with active multi-air jet blowing | Torque increases about two times to the baseline blade | [151] |

S822 | Using an automated two-dimensional airfoil shape optimization procedure | Optimizing the blade shape enhances blade performance | [152] |

NACA23012C | By generating a passive jet from the pressure side to the suction side | The optimized geometry increases the CL/CD by 32.0% from the initial design without passive jet control | [153] |

— | Using Maximum Power Point Tracking (MPPT) Control based on perturb and observe (PO) algorithm method | The installation of the MPPT controller increased the output power by 135.62%, with an average power increase of 50.77% | [154] |

— | By augmenting the wind turbine with a diffuser | The power production was increased | [155] |

E216 | By curving the blade | The power coefficient increases up to 13% | [156] |

In summary, the performance of the wind turbine blade is sensitive to the shape of the blade and airfoil, the rotor diameter, blade number, wind speed, Re, AOA, twisting angle, and control mechanism. It also revealed that the performance of the blade is enhanced by using thick airfoils at the root section, medium thick airfoils at the midsection, and thin airfoils at the tip section, avoiding leading-edge erosion, and optimizing its geometry based on the site data and introducing flap, slat, concentrator, jet, and maximum power tracking control. There are many attempts by researchers to improve the performance of a small wind turbine by using low Re airfoils that have a high gliding ratio for the design of wind turbine blades, but there is still a gap in selecting and using modern and proficient airfoils such as E59, E61, E63, etc., individually and combined with other airfoils.

## 7 Issues and Challenges of Small Wind Turbines

Small wind turbines are a novel technology that has great potential for meeting energy demands in a wide range of businesses, notably in rural areas, residential settings, and hybrid energy systems. These turbines, which generally have capacities ranging from 2 kW to 40 kW, offer a decentralized and sustainable energy source. Small wind turbines offer many advantages, and they also have many issues and challenges that must be overcome if their effectiveness, reliability, and universal acceptance are to be enhanced. This paper discusses the many challenges and issues that small wind turbines face and provides an in-depth overview of the current situation regarding this technology.

### 7.1 Turbulence and Site Selection.

One of the most difficult aspects of installing small wind power plants is finding a suitable location. In contrast to large-scale wind farms that benefit from careful site evaluations and meteorological studies, these turbines usually lack such detailed assessments. Site-specific variables such as wind speed, turbulence intensity, and wind direction, like in the case of large-size turbines, have a substantial impact on the efficiency of small wind turbines. As a result of these elements, the turbine's overall reliability may be compromised by decreased energy output and higher mechanical stress on the turbine's parts.

### 7.2 Scale and Performance.

Another challenge with small wind turbines is that their performance diminishes as their size decreases. Small wind turbines revolve at lesser speeds than bigger equivalents, which may result in reduced aerodynamic efficiency. The turbine's size and design constraints, particularly in terms of blade length, may make it more difficult to harvest wind energy. Furthermore, because small wind turbines frequently operate in lower Re regimes, their overall efficiency may be decreased as a result of higher aerodynamic losses. The performance of small wind turbines must be maximized by overcoming these scaling problems.

### 7.3 Noise and Vibration.

Small wind turbines raise serious issues concerning noise and vibration, especially when they are located in residential areas. Residents close by may experience disturbances and discomfort due to the mechanical noise produced by the turbine's gearbox and generator as well as the aerodynamic noise produced by the whirling blades. Additionally, the durability of the turbine may be impacted by structural fatigue brought on by vibrations generated by the operation of the turbine. Improved blade profiles, better gearbox arrangements, and improved vibration-dampening techniques are just a few of the advanced design strategies needed to address noise and vibration challenges.

### 7.4 Grid Integration and Power Quality.

Small wind turbines are routinely integrated into regional electrical grids or hybrid renewable energy systems, necessitating proper grid integration and power quality management. Wind energy offers challenges in sustaining a constant power supply and assuring compatibility with the current grid infrastructure due to its chaotic nature. Power conditioning systems, energy storage solutions, and cutting-edge control algorithms are necessary to effectively manage difficulties such as voltage variances, frequency deviations, and power quality concerns. Continuous grid connectivity is required for small wind generators to function reliably and effectively.

### 7.5 Maintenance.

Small wind turbine maintenance and servicing can be difficult, especially in rural areas or when placed in residential areas. It can be challenging and expensive to gain access to the turbine parts, such as the rotor, nacelle, and tower, which could cause delays in maintenance operations. Additionally, complicated electrical and control systems frequently used in small wind turbines necessitate specific knowledge and skills for troubleshooting and repairs. To reduce downtime and assure long-term performance, consistent maintenance practices, remote monitoring systems, and strong service networks must be developed.

### 7.6 Cost and Affordability.

Due to costs, small wind turbines confront significant barriers. These turbines lack the economies of scale that utility-scale wind farms like to do, making them more expensive per unit of electricity generated. The initial capital expense, which includes the purchase, installation, and support infrastructure for turbines, limits their widespread acceptability. To encourage the installation of small wind turbines, cost restrictions must be overcome by advances in manufacturing processes, enhanced market competitiveness, and government subsidies or support programs.

### 7.7 Durability.

Another significant challenge is assuring the durability and robustness of small wind turbines. Wind turbine rotor blades, bearings, gearbox, and generator are all subject to climatic factors such as wind loads, temperature variations, and humidity. These factors may cause increased wear and strain, leading to premature failure and a shorter lifespan. Small wind turbines’ service life can be extended by designing strong parts, following proper maintenance practices, and employing long-lasting materials.

### 7.8 Environmental Impact and Visual Aesthetics.

Despite providing a clean source of electricity, the construction and operation of small wind turbines can have an influence on the environment and the surrounding region. Communities and environmental groups routinely express concerns over bird and bat crashes, including their impact on surrounding ecosystems and aesthetics. To address these concerns, comprehensive environmental impact evaluations and community interactions are required. Furthermore, advancements in turbine design, such as cutting-edge blade shapes and colors, can aid in lessening aesthetic effects and promote public acceptance.

## 8 Potential Future Research Directions

The performance of a wind turbine can be improved by optimizing the airfoil shape and selecting efficient airfoils for the design and modeling of a wind turbine blade. In this review, it was noticed that NACA series airfoils were commonly used for wind turbine blades; however, other families of airfoils have better aerodynamic performance than the NACA airfoils. Hence, the use of low Re and efficient airfoils such as E59, E61, E62, E63, E216, E379, E471, STCYR24, SOKOLOV, S4053, GM15, S1091, GOE370, SG6043, DAVISSM, and AH-7476 for wind turbine blade design can be further investigated to enhance the aerodynamic performance of the turbine.

Additionally, the design and optimization of wind turbines with different configurations were explored to improve their aerodynamic performance. This includes exploring different rotor configurations, rotor sizes, and blade shapes. Despite these attempts, the power coefficient of the wind turbine needs further improvement to maximize its power production. Hence, enhancing the aerodynamic performance of wind turbines will be a potential future research area.

Another issue that was considered during the design of wind turbines was the effect of external features around the turbines, such as buildings, terrain, and vegetation. Thus, examining the flow patterns around the wind turbine and optimizing the external features will be further investigated to improve the efficiency of the turbine.

Small-scale wind turbines have been costly, less reliable, and less efficient than large wind turbines, which limits their appeal to a wide range of users. So, there is a need for research into innovative methods to reduce the cost and enhance the reliability and efficiency of small-scale wind turbines.

The use of mixed airfoils, which combine the best features of different airfoils, can be also explored to improve the performance of wind turbines.

Moreover, these turbines are often exposed to harsh environments, such as rain, snow, and hurricanes. These can lead to premature tears and wear, which can decrease their performance and durability. Hence, it needs further investigation to enhance the life span and performance of small-scale wind turbines.

The BEM method is widely used for analyzing the performance of wind turbines. However, the theory fails to give a genuine performance in the region where stalls arise at a low tip speed ratio and high AOA. Due to the limitations of BEM theory, the extended BEM approaches were developed to improve the predicted performance of the BEM approach by considering the influence of pressure drop due to wake rotation and the effect of radial velocity at the rotor disc; the impact created by vortices that are shed from the blade tips into the wake; the vortex shed near the hub; the turbulent wake phenomena; the lift augmentation brought on by rotation; and the influence made by wind turbines when operating at yaw angles. The review result revealed that many researchers have designed and analyzed the horizontal-axis wind turbine by considering the above corrections individually. However, all corrections must be taken into account during the design and performance evaluation of a turbine to get an accurate and reliable result. So, modification of the existing BEM model by considering all the listed corrections and evaluating the predicting performance of these models can be further studied.

Several turbulence models have been used to solve fluid flow problems including visualizing the fluid flow over a wind turbine rotor. Turbulence models and other flow parameters can be improved to increase the accuracy of wind turbine performance predictions. This will make the results more reliable and useful for designing and optimizing wind turbines. Hence developing extended turbulence models that could have better agreement with the experimental results would be a potential research area.

Another future research area will be developing innovative testing techniques for observing the flow across the blade and improving the accuracy of testing procedures. This includes using advanced imaging techniques such as particle image velocimetry (PIV) and high-speed cameras.

Small wind turbines can be used either stand-alone or integrated with other renewable energy resources to create a more sustainable energy system. Thus, investigating hybrid small wind turbines integrated with other renewable energy sources such as solar power, hydropower, and batteries requires further research attention.

## 9 Conclusions

The purpose of this paper was to review the current trends in the innovation of wind power technologies, the aerodynamic performance of airfoils and wind turbine rotors, the chord length and inflow angle computation approaches of small-scale horizontal-axis wind turbines, the techniques employed for enhancing the aerodynamic performance of a horizontal-axis wind turbine, and the issues and challenges of small wind turbines. Based on the findings of this review, the following conclusions can be drawn.

Small-scale wind turbines have great potential for sustainable and off-grid energy generation, although they are vulnerable to several challenges such as efficiency, cost, noise, site selection, maintenance, and durability. Hence, many researchers are focusing on the mitigation of these challenges and the development of small wind turbines that can operate at low wind speed conditions. The aerodynamic performance of horizontal-axis wind turbines can be studied by analytical, numerical, and experimental approaches. The BEM method is a numerical approach combining 2D airfoil data for the rapid calculation of aerodynamic characteristics and performance of HAWT blades. However, due to the stalled and yawed rotor condition, the BEM method is not always reliable for simulating the aerodynamic load on the blade. Correction methods, such as Prandtl's tip loss factor, Spera's correction, and the Du-Selig stall delay model, enable the modification of 3D effects as well as improvements in prediction accuracy. The performance evaluation of wind turbines by the computational method is usually performed by the RANS approach, very often combined with an SST *k* − *ω* turbulence model due to its flexibility, robustness, and relatively short computational time.

The use of high-performance mixed airfoils, twisted blades, ducts, and maximum point tracking electric generators for horizontal-axis wind turbines increases the extracted power from wind. Generally, this review paper will be used by academicians and researchers who are interested in the design and performance analysis of wind turbines.

## Author Contribution Statement

The concept for creating a review study on current trends and innovations in enhancing the aerodynamic performance of small-scale, HAWTs came from Belayneh Yitayew Kassa. With the supervision of Aklilu Tesfamichael Baheta and Asfaw Beyene Abebe, he wrote the essay, which was then edited by the two coauthors.

## Acknowledgment

The authors want to acknowledge Arba Minch University and Addis Ababa Science and Technology University for their technical and financial support. The work presented in this paper was supported by the Sustainable Energy Center of Excellence, Addis Ababa Science and Technology University under award no. IGP 015/2023.

## Conflict of Interest

There are no conflicts of interest. This article does not include research in which human participants were involved. Informed consent is not applicable. This article does not include any research in which animal participants were involved.

## Data Availability Statement

The authors attest that all data for this study are included in the paper.

## Nomenclatures

*a*=axial induction factor

*n*=number of blade sections

*A*=rotor cross-sectional area

*B*=number of rotor blades

*C*=airfoil chord

*F*=tip loss correction factor

*P*=power

*T*=torque

*V*=wind speed

*a*′ =angular induction factor

*C*_{d}=drag coefficient

*C*_{l}=lift coefficient

*C*_{p}=power coefficient

*C*_{T}=thrust coefficient

- LDR =
lift-to-drag ratio

- Re =
Reynolds number

- WR =
wake ratio

*R*,*r*=blade radius, local blade radius

*α*,*AOA*=angle of attack

*β*=pitch angle

*γ*=relative airflow angle

*θ*=twist angle

*λ*,*TSR*=tip-speed ratio

*λ*_{r}=local tip-speed ratio

*λ*_{D}=design tip speed ratio

*μ*=dynamic viscosity

*v*=kinematic viscosity

*ρ*=air density

*σ*=chord solidity

- $\Phi $, $\Phi i$ =
flow angle, sectional flow angle

*ω*=angular velocity

## References

*—*Resources, Challenges and Applications

*Scientific Research Air Ministry—Reports and Memoranda No. 1111*: 41.