Wind turbine blades perform the most important function in the wind energy conversion process. It plays the most vital role of absorbing the kinetic energy of the wind, and converting it to mechanical energy before it is transformed into electrical energy by generators. In this work, National Advisory Committee for Aeronautics (NACA) 4412 and SG6043 airfoils were selected to design a small horizontal axis variable speed wind turbine blade for harvesting efficient energy from low wind speed areas. Due to the low wind profile of the targeted area, a blade of one-meter radius was considered in this study. To attain the set objectives of fast starting time and generate more torque and power at low wind speeds, optimization was carryout by varying Reynolds numbers (Re) on tip speed ratios (TSR) values of 4, 5, and 6. The blade element momentum (BEM) method was developed in MATLAB programming code to iteratively find the best twist and chord distributions along the one-meter blade length for each Re and tip speed ratio (TSR) value. To further enhance the blade performance, the twist and chord distributions were transferred to Q-blade software, where simulations of the power coefficients (Cp) were performed and further optimized by varying the angles of attack. The highest power coefficients values of 0.42, 0.43, and 0.44 were recorded with NACA 4412 rotor blades, and 0.43, 0.44, and 0.45 with SG6043 rotor blades. At the Re of 3.0 × 10⁵, the blades were able to harvest maximum power of 144.73 watts (W), 159.69 W, and 201.04 W with the NACA 4412 and 213.15 W, 226.44 W, 245.09 W with the SG6043 at the TSR of 4, 5, and 6 respectively. The lowest cut-in speed of 1.80 m/s and 1.70 m/s were achieved with NACA 4412 and SG6043 airfoils at TSR 4. At a low wind speed of 4 m/s, the blades were able to harness an efficient power of 79.3. W and 80.10 W with both rotor blades at the TSR 4 and 6 accordingly.

As wind energy is widely available, an increasing number of individuals, especially in off-grid rural areas, are adopting it as a dependable and sustainable energy source. The energy of the wind is harvested through a device known as a wind energy harvesting system (WEHS). These systems convert the kinetic energy of wind into electrical energy using wind turbines (WT) and electrical generators. However, the output power of a wind turbine is affected by various factors, such as wind speed, wind direction, and generator design. In order to optimize the performance of a WEHS, it is important to track the maximum power point (MPP) of the system. Various methods of tracking the MPP of the WEHS have been proposed by several research articles, which include traditional techniques such as direct power control (DPC) and indirect power control (IPC). These traditional methods in the standalone form are characterized by some drawbacks which render the method ineffective. The hybrid techniques comprising two different maximum power point tracking (MPPT) algorithms were further proposed to eliminate the shortages. Furtherly, Artificial Intelligence (AI)-based MPPT algorithms were proposed for the WEHS as either standalone or integrated with the traditional MPPT methods. Therefore, this research focused on the review of the AI-based MPPT and their performances as applied to WEHS. Traditional MPPT methods that are studied in the previous articles were discussed briefly. In addition, AI-based MPPT and different hybrid methods were also discussed in detail. Our study highlights the effectiveness of AI-based MPPT techniques in WEHS using an artificial neural network (ANN), fuzzy logic controller (FLC), and particle swarm optimization (PSO). These techniques were applied either as standalone methods or in various hybrid combinations, resulting in a significant increase in the system’s power extraction performance. Our findings suggest that utilizing AI-based MPPT techniques can improve the efficiency and overall performance of WEHS, providing a promising solution for enhancing renewable energy systems.

This study presents the design optimization of a WTB for low-WS applications using a combined BEMT–PSO framework implemented in QBlade and MATLAB with experimental validation. To address the aerodynamic limitations of mSWTs operating under low-Re conditions, the SG6043 airfoil was selected due to its superior lift-to-drag characteristics in laminar flow. An optimized low-TSR mSWTB is developed with refined C and θ distributions to enhance Cp and reduce Vin. The first-stage MPOB-optimized WTB achieved a Cp-max of 0.445 with a Vin of 2.0 m/s. In the second stage, the incorporation of root-curvature modification using RC-CPOB further improved the Cp-max to 0.468, corresponding to a 4.92% enhancement in aerodynamic performance, while reducing the Vin to 1.8m/s. Simulations using site real WS demonstrated strong low-WS performance of the TSR4-based blade across the study sites, yielding AEGs of 81.19kWh and 108.48kWh at Karakit and Mersing, respectively. At the higher-wind Rompin site, the TSR-5-based blade achieved the highest AEG of 160.98kWh, compared with 155.35kWh for the TSR-4 design. Experimental evaluation showed strong agreement between simulated and prototype Pe, with a maximum deviation of only 7.60%. Furthermore, maximum AEG deviations of only 8.81% were recorded across the sites. Compared with the selected CBLB, the prototype achieved up to 7.8% and 13.65% improvements in Pe and AEG, respectively. The proposed framework confirms the aerodynamic effectiveness of RC-CPOB-based optimization in enhancing mSWEH under low-wind conditions, while demonstrating the suitability of FDM for rapid prototyping and providing a validated pathway for QBlade-based BEMT aerodynamic design of mSWTBs.