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The present article focuses on a cradle-to-grave life cycle assessment (LCA) of the most widely adopted solar photovoltaic power generation technologies, viz., mono-crystalline silicon (mono-Si), multi-crystalline silicon (multi-Si), amorphous silicon (a-Si) and cadmium telluride (CdTe) energy technologies, based on ReCiPe life cycle impact assessment method. LCA is the most powerful environmental impact assessment tool from a product perspective and ReCiPe is one of the most advanced LCA methodologies with the broadest set of mid-point impact categories. More importantly, ReCiPe combines the strengths of both mid-point-based life cycle impact assessment approach of CML-IA, and end-point-based approach of Eco-indicator 99 methods. Accordingly, the LCA results of all four solar PV technologies have been evaluated and compared based on 18 mid-point impact indicators (viz., climate change, ozone depletion, terrestrial acidification, freshwater eutrophication, marine eutrophication, human toxicity, photochemical oxidant formation, particulate matter formation, terrestrial ecotoxicity, freshwater ecotoxicity, marine ecotoxicity, ionising radiation, agricultural land occupation, urban land occupation, natural land transformation, water depletion, metal depletion and fossil depletion), 3 end-point/damage indicators (viz., human health, ecosystems and cost increases in resource extraction) and a unified single score. The overall study has been conducted based on hierarchist perspective and according to the relevant ISO standards. Final results show that the CdTe thin-film solar plant carries the least environmental life cycle impact within the four PV technologies, sequentially followed by multi-Si, a-Si and mono-Si technology.

The paper represents the experimental implementation of a Drone-based solar panel inspection using YOLOv8-based object detection with Ultraviolet sensing for automated and enhanced defect inspection. A quadcopter platform was equipped with imaging sensors that was used to capture aerial images of the solar panels. The dataset used consists of 1500 annotated images categorized into clean panels, surface cracks, dust accumulation and thermal defects. The YOLOv8 model was fine-tuned using the dataset which had an input of 500x500 resolution for training a series of 100 epochs. Transfer learning enabled object localization and classification and RGB-based detection, for effective detection and identification of abnormal surfaces, discharge related anomalies where identified successfully that cannot be identified through standard imaging. Experimental validation also demonstrated reliable detection across all categories of defects and the results validated the completion of the project using lightweight deep learning model for object detection with multiple sensor UAV platform for cost-effective inspection, making a cost-efficient and automated solar farm monitoring possible and much more efficient than traditional methods.

The installation of solar plants everywhere in the world increases year by year. Automated diagnostic methods are needed to inspect the solar plants and to identify anomalies within these photovoltaic panels. The inspection is usually carried out by unmanned aerial vehicles (UAVs) using thermal imaging sensors. The first step in the whole process is to detect the solar panels in those images. However, standard image processing techniques fail in case of low-contrast images or images with complex backgrounds. Moreover, the shades of power lines or structures similar to solar panels impede the automated detection process. In this research, two self-developed methods are compared for the detection of panels in this context, one based on classical techniques and another one based on deep learning, both with a common post-processing step. The first method is based on edge detection and classification, in contrast to the second method is based on training a region based convolutional neural networks to identify a panel. The first method corrects for the low contrast of the thermal image using several preprocessing techniques. Subsequently, edge detection, segmentation and segment classification are applied. The latter is done using a support vector machine trained with an optimized texture descriptor vector. The second method is based on deep learning trained with images that have been subjected to three different pre-processing operations. The postprocessing use the detected panels to infer the location of panels that were not detected. This step selects contours from detected panels based on the panel area and the angle of rotation. Then new panels are determined by the extrapolation of these contours. The panels in 100 random images taken from eleven UAV flights over three solar plants are labeled and used to evaluate the detection methods. The metrics for the new method based on classical techniques reaches a precision of 0.997, a recall of 0.970 and a F1 score of 0.983. The metrics for the method of deep learning reaches a precision of 0.996, a recall of 0.981 and a F1 score of 0.989. The two panel detection methods are highly effective in the presence of complex backgrounds.

This paper presents a comprehensive review of solar panel performance degradation in both industrial and residential sectors. Drawing on a wide range of academic studies, the paper systematically analyses the key factors affecting the performance of photovoltaic (PV) systems to provide in‐depth understanding of degradation mechanisms along with effective countermeasures. These factors include the selection and properties of the materials used in PV panel manufacturing, changes in environmental conditions, the inherent degradation rate of materials and user behaviour. The paper aims to comprehensively reveal the mechanisms by which environmental and human factors contribute to PV panel performance degradation, assess their impact on the operational efficiency of the power systems and explore feasible adaptive solutions to mitigate or restore PV system performance. The paper also incorporates a technical framework aligned with the IEC 61850 standard and provides constructive recommendations for enhancing the efficiency and reliability of renewable power systems. The paper holds substantial theoretical and practical significance. At a macro level, it contributes to reducing the overall cost of PV energy production while minimising investment in equipment maintenance and human resources. At a micro level, it enhances the utilisation efficiency and basic performance of PV systems. The recommendations of this paper not only support the sustainable growth of the renewable energy industry but also facilitate the synergistic expansion of the upstream and downstream industrial chain, fostering new employment opportunities and business potential. For individual users, businesses and the public sector, the paper provides a robust scientific foundation for developing future energy strategies with practical insights to advance global sustainable development goals. Promote key factors affecting the performance of photovoltaic (PV) systems. Explore feasible adaptive solutions to mitigate or restore the performance degradation of PV systems. Integrates a technical framework compliant with the IEC 61850 standard.

Maximizing output from renewable solar panels requires higher efficiency. Conventionally, such optimization techniques—MPPT (Maximum Power Point Tracking) along with heuristic algorithms—suffer significantly from slow adaptability and track sub optimality under dynamic environments. This article proposes a numerical modeling framework from hybrid AI models, combining physics-informed neural networks and RL for real-time optimization of orientation in solar panels. The methodology uses numerical modeling for precise energy transformation analysis, and deep learning-based optimization dynamically adjusts the angles of panels to maximize power output. A self-learning adaptive neural network is developed to improve tracking accuracy based on real-time irradiance and temperature variations. Moreover, an Edge AI architecture is introduced to make low-latency decisions with reduced dependency on cloud computation, thus improving the efficiency of the system. Besides, an advanced hybrid model based on CNN-LSTM is applied to solar energy forecasting for predictive control of the maximum energy yield. Experimental validation was performed using UTL 335W and 330W PV modules, where real-time data acquisition was followed by AI-driven optimization. Results show an increase in energy yield by 10–15% compared to traditional MPPT systems, while computations are performed 40–50% faster using AI-based numerical modeling. The proposed approach achieves 25% lower forecasting error (RMSE/MAE) and 30% reduced power consumption through Edge AI implementation. This study sets up a new paradigm for AI-integrated solar optimization, which ensures real-time adaptability and enhanced performance in practical deployment. The findings advance the intelligent solar tracking and set a new benchmark for AI-driven renewable energy management.

The optimization of floating bifacial solar panels (FBS PV) in tropical freshwater systems is explored by employing response surface methodology (RSM) and central composite design (CCD). Previous studies have yet to explore the long-term durability, environmental impact, economic viability, and performance of FBS PV systems under various climatic conditions. This study addresses this gap by focusing on panel height, water depth, and tilt angle to improve performance. The quadratic model reveals significant non-linear relationships impacting FBS PV power generation with freshwater cooling. Our models demonstrate high explanatory power, with R-squared values of 0.9831 for output power and 0.9900 for Bi-Facial gain. Experimental validation using conventional white surface (CWS) and proposed freshwater surface (PFS) indicates notable improvements in power generation, achieving a 4.34 to 4.86% gain in bifacial efficiency across various irradiation levels. Under 950 W/m 2 irradiation, freshwater cooling achieves a 3.19% higher bifacial gain compared to CWS cooling. Panel temperature analysis shows consistent reductions with freshwater cooling, ranging from 1.43 to 2.72 °C, enhancing overall efficiency and longevity. This research highlights the potential of freshwater cooling in optimizing bifacial solar systems, offering actionable insights for sustainable energy solutions in tropical regions.

Solar energy development is a significant driver of land‐use change worldwide, and desert ecosystems are particularly well suited to energy production because of their high insolation rates. Deserts are also characterized by uncertain rainfall, high species endemism, and distinct landforms that vary in geophysical properties. Weather and physical features that differ across landforms interact with shade and water runoff regimes imposed by solar panels, creating novel microhabitats that influence biotic communities. Endemic species may be particularly affected because they often have limited distributions, narrow climatic envelopes, or specialized life histories. We used experimental panels to simulate the effects of solar development on microhabitats and annual plant communities present on gravelly bajada and caliche pan habitat, two common habitat types in California's Mojave Desert. We evaluated soils and microclimatic conditions and measured community response under panels and in the open for seven years (2012–2018). We found that differences in site characteristics and weather affected the ecological impact of panels on the annual plant community. Panel shade tended to increase species richness on the more stressful caliche pan habitat, and this effect was strongest in dry years. Shade effects on diversity and abundance also tended to be positive or neutral on caliche pan habitat. On gravelly bajada habitat, panel shade did not significantly affect richness or diversity and tended to decrease plant abundance. Panel runoff rarely affected richness or diversity on either habitat type, but effects on abundance tended to be negative—suggesting that panel rain shadows were more important than runoff from low‐volume rain events. These results demonstrate that the ecological consequences of solar development can vary over space and time, and suggest that a nuanced approach will be needed to predict impacts across desert landforms differing in physical characteristics.

This study examines the evolution and advancement of solar cleaning robot technology, focusing on the development trajectory from the initial prototype to the most recent iteration. The context is set within the Middle East and North Africa (MENA) region, where the adoption of solar power initiatives is rapidly expanding. The need for efficient solar panel maintenance solutions is discussed, driven by dust and soiling accumulation challenges. The Sultanate of Oman’s ambitious renewable energy targets underscore the importance of innovative technologies like the Autonomous Solar Cleaning Robot (ASCR) in maintaining optimal photovoltaic (PV) system performance. It discusses the iterative design, functionality, and operational efficiency improvements, addressing challenges such as inaccurate brush contact and mechanical failures. The ASCR performance was tested through multiple testing and experimental conditions and was demonstrated by evaluating the solar system’s performance utilizing real-time data from irradiance, temperature, and dust sensors. The analysis performed revealed strong correlations between irradiance sensors and solar power production, with up to 100% correlation for both groups of panels. Additionally, a 90% correlation was found between the temperature readings and the power output data. The impact of dust and soiling on the solar system’s efficiency was also analyzed in this study, and a 30% energy reduction was recorded due to dust and soiling accumulation, which increasingly affects energy outcomes. Alongside the ASCR integration on the solar system, a DustIQ sensor was also integrated into the system to quantify the soiling ratios and identify the optimal cleaning interval, which, as a result, maximizes the ASCR efficiency. The findings from this study suggest that in order to ensure optimal solar panel efficiency is have an automated system responsive to real-time data generation, which helps manage dust and soiling accumulation and minimize downtime, thus ensuring more reliable renewable energy integration in the Oman region.

PV system utilize the photovoltaic effect to generate electricity directly from the energy brought by the sunrays. However, the normal ground installation of PV panel is prone to several effect reducing the power output and efficiency, such as the overheated panel surface that can lead to malfunction of a cell. The alternative for ground installation is installing the PV panel on the surface of a water body such as river or lake, and called Floating Solar Panel. This paper presents the pilot project for application of floating solar panel in Palembang. This setting is also functioning as passive or natural cooling for the panel and increase the power output. The pilot project was conducted on August 2-8, 2019 by comparing two 100 Wp Poly-crystalline PV panels. The passive cooling of floating solar panel can reduce surface temperature by 2oC compared to ground installation. The experiment shows the most effective time to harvest the power from the sun is from 11.00 AM to 02.00 PM. The average output power generated by Floating Solar Panels is 51.6 Watts, compared to 42.9 Watt Ground Solar panels.

The buildup of dust on solar panels has significantly reduced the operational lifespan and mission performance of exploration rovers, and traditional dust removal techniques have proven inadequate for the Martian environment. The present study proposes a novel method for removing dust from the solar panels of Mars exploration rovers using Chladni patterns, addressing the persistent issue of efficiency loss due to Martian dust accumulation. To overcome these challenges, the proposed method leveraged Chladni patterns, generated by specific frequencies, to effectively clear dust from the panels. We conducted experiments that identified optimal frequencies, frequency sequences, and plate shapes for dust removal, demonstrating the method’s effectiveness. In conclusion, our approach not only enhances the efficiency of solar panels but also has the potential to improve the overall performance and longevity of Mars exploration missions.