The unprecedented growth of renewable energy infrastructure poses both technical and humanistic issues, especially about the reliability of the system, efficiency, and adaptability of the workforce. This literature review investigates the role of artificial intelligence (AI) in optimizing renewable energy systems (RES) through fault detection and predictive maintenance (PdM) from an industrial psychology perspective to understand human-technology interactions. The research reflects current studies on AI-powered monitoring devices, anomaly detection methods, and PdM models, and their usefulness in minimizing unexpected downtime, extending equipment life, and optimizing overall equipment performance. At the same time, the review highlights how industrial-psychological influences, including employee attitudes, cognitive preparation, and behavioral adaptation, can determine the successful adoption and use of AI-enabled maintenance solutions. It has been shown that the benefits of AI technologies, development of trust, and reduction of resistance to technological change are highly dependent on workforce engagement, training, and participation in decision-making. The review also establishes the major organizational, social, and economic consequences of incorporating AI into human-centric responses, indicating that the best opportunities are expected to arise from matching technical advances with human behavioral concerns. Lastly, existing research gaps are addressed, including empirical research on the long-term relationship between humans and AI, cross-cultural workforce strategies, and scalable implementation plans for various renewable energy infrastructure. Altogether, this review has demonstrated the prospective changes that can be made to the possibility of stabilizing the renewable energy infrastructure through the combination of PdM, AI-based fault detection, and industry-oriented psychological insights, providing a holistic framework to improve the stability, sustainability, and human-machine interactions of the renewable energy infrastructure.
Parabolic dish solar collectors (PDSCs) offer exceptional potential for high-temperature solar-energy conversion owing to their point-focus configuration, high concentration capability, and compatibility with advanced thermodynamic and thermal energy storage (TES) technologies. However, their broader deployment remains constrained by coupled optical, thermal, mechanical, materials, and operational challenges that are often addressed independently in the existing literature. This review provides a critical and system-level assessment of recent advances in PDSCs for high-temperature applications, integrating developments in concentrator geometry, optical performance, solar tracking, receiver technologies, thermal management, power conversion, and TES. Particular attention is given to cavity, tubular, volumetric, and phase-change-material-integrated receivers, together with Stirling engines, thermoelectric generators (TEGs), and hybrid conversion architectures. The review further evaluates the role of advanced materials, selective coatings, intelligent tracking, real-time monitoring, numerical modeling, and experimental validation in reducing optical and thermal losses and improving system reliability. Emerging pathways involving hybrid renewable systems, industrial process heat, decentralized energy supply, and solar-driven hydrogen production are critically examined. The analysis identifies material degradation, thermal losses, high capital cost, tracking accuracy, storage limitations, and scalability as major barriers to commercialization. A system-integration perspective is therefore proposed in which optical optimization, receiver design, TES, intelligent control, and power conversion are co-optimized rather than treated as isolated components. Future research should prioritize validated multi-physics modeling, advanced high-temperature materials, intelligent control, scalable thermal storage, techno-economic optimization, and long-duration field demonstration to accelerate the transition of PDSCs toward reliable, dispatchable, and sustainable high-temperature energy systems.
This study presents an integrated strategy for both energy conversion and control in a grid-connected photovoltaic (PV) system. The goal is to ensure smooth coordination between power extraction, direct-current (DC) link voltage regulation, grid synchronization, and control of power exchange. The system configuration includes a PV array, a Boost converter controlled by a hybrid maximum power point tracking (MPPT) algorithm combining Perturb and Observe (P&O) with the Whale Optimization Algorithm (WOA), DC-link stage, and a three-phase inverter. Grid synchronization is achieved using a synchronous reference frame phase-locked loop (SRF-PLL), while active and reactive power are managed through indirect vector control in the synchronous dq reference frame. The model was developed and tested in MATLAB/Simulink 2022b under two conditions: a steady-state scenario at an irradiance of 1000 W/m² and a temperature of 25 °C, and a dynamic scenario with varying irradiance while maintaining constant temperature. In steady-state operation, the PV system delivers around 100 kW, the DC-link voltage is stabilized near 600 V, and the grid voltages remain balanced with peak values close to ±340 V. The injected currents also show good quality with low distortion. When subjected to changes in irradiance, the system responds effectively, with the extracted power closely tracking the variations. At the same time, the DC-link voltage remains stable, the three-phase voltage balance is preserved, and the quality of the injected current is maintained. Overall, the results confirm that the proposed method offers a reliable and well-coordinated solution for integrating energy conversion and control in grid-connected PV systems, delivering strong performance in both steady-state and dynamic operating conditions.
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