Incremental Conductance (IC) technique is a cheap, and easy algorithm to implement for Maximum Power Point Tracking (MPPT). However, the IC technique usually takes time and suffers some delay to approach the MPP if the voltage is not near to the MPP or when subjected to rapid change in irradiance. In this paper, IC technique was implemented and compared to the modified Incremental Conductance technique (MIC) under various environmental conditions such as standard test conditions (STC) and partial shading conditions. Also the MIC method was compared to a Fuzzy Logic Controller (FLC) MPPT technique in order to evaluate the best and the accurate controller for the MPPT for different weather conditions. Results show that using FLC and MIC techniques are efficient for MPPT and may increase the PV system stability.

The increasing adoption of wind turbines as a key component of renewable energy generation necessitates the development of efficient and reliable maintenance strategies to ensure their optimal performance and safety. Among the most critical aspects of turbine maintenance is detecting and classifying defects in wind turbine blades, which are constantly exposed to extreme environmental conditions. Defects such as cracks, delamination, erosion, and icing not only compromise the structural integrity of blades but also significantly reduce their aerodynamic efficiency and energy production capabilities. While effective, traditional inspection methods are labor-intensive and time-consuming, prompting the exploration of automated solutions. This review paper examines the state-of-the-art deep learning methodologies applied to defect detection in wind turbine blades. Key advancements are highlighted, including the integration of Convolutional Neural Networks (CNNs), Variational Autoencoders (VAEs), and Generative Adversarial Networks (GANs) for image-based detection and anomaly identification. Additionally, transfer learning and attention mechanisms have been instrumental in enhancing the precision and speed of defect detection, enabling real-time applications. Notable approaches like YOLO (You Only Look Once) and its variants have shown exceptional performance in detecting defects with varying scales and complexities, leveraging innovations such as feature pyramid networks and efficient loss functions. Furthermore, this review discusses the role of advanced data acquisition techniques, such as drone-based imaging, thermographic analysis, and LiDAR (Light Detection and Ranging), in generating high-resolution and multi-spectral data for improved detection accuracy. Insights are provided into challenges such as class imbalance, limited labeled datasets, and environmental noise, alongside emerging solutions like semi-supervised learning and data augmentation. The paper concludes by emphasizing the transformative potential of deep learning in achieving automated, accurate, and efficient defect detection in wind turbine blades. Addressing current limitations through interdisciplinary research and innovative algorithms will pave the way for sustainable and cost-effective wind energy systems, ultimately contributing to the global transition toward renewable energy.

This paper presents an exploration of the potential of hybrid renewable energy systems (HRESs), combining floating solar photovoltaics (FPV), wind turbines, and vanadium redox flow (VRF) battery energy storage systems (BESSs) to expedite the transition from conventional to renewable energy for the mining sector in South Africa. The feasibility study assesses how to enhance the overall efficiency and minimize greenhouse gas emissions from an economic standpoint by using the Hybrid Optimization of Multiple Energy Resources (HOMER) grid software version 1.11.1 and PVsyst version 7.4. Furthermore, the BESS Covariance Matrix Adaptation Evolution Strategy (CMA-ES) dispatch algorithm is proposed to make the most of the battery storage capacity and capability, aligning it with the dynamic energy demand and supply patterns of an HRES. The proposed HRES includes a highly efficient SFPV with a performance ratio of 0.855 and an annual energy production of 15,835 MWh; a wind turbine (WT) operating for 2977 h annually, achieving a 25% wind penetration rate; and a dynamic VRF-BESS with a 15,439 kWh life throughput and a 3 s dispatch response time. This HRES has a CapEx of R172 million, a 23.5% Internal Rate of Return (IRR), and an investment payback period of 4.9 years. It offers a low Levelized Cost of Energy (LCoE) at 4.27 R/kWh, a competitive Blended Cost of Energy (BCoE) at 1.91 R/kWh, and a positive net present cost (NPC), making it economically advantageous without external subsidies. Moreover, it annually reduces CO₂ emissions by 1,715,468 kg, SO₂ emissions by 7437 kg, and NOx emissions by 3637 kg, contributing to a significant environmental benefit.

Amidst South Africa’s persistent energy crisis, this study explores the viability of microgrid systems as a pivotal solution to enhance energy reliability and sustainability. By scrutinizing factors like resource availability, grid connectivity, demand dynamics, regulatory frameworks, and financial implications, a comprehensive techno-economic analysis reveals the substantial potential of microgrids. These systems not only augment energy access and fortify grid resilience but also facilitate the seamless integration of renewable energy sources, aligning with global sustainability goals. Economic evaluations underscore favorable returns on investment, emphasizing the long-term cost efficiencies and broader societal benefits. The microgrid showcases strong financial performance with an Internal Rate of Return (IRR) of 23.1%, competitive Blended Cost of Energy (BCoE) at $0.073/kWh, Levelized Cost of Energy (LCoE) at $0.186/kWh lower than Eskom grid tariffs, and a rapid payback time of 5.5 years. The proposed microgrid has the potential to save 1,135,949.737 tons of carbon dioxide (CO₂) over its 30-year lifecycle.