Search Results (88)

The continuous degradation of mangrove ecosystems, considered among the most vulnerable worldwide, reveals multiple threats driven by human activities and climate change. In the Peruvian context, particularly in the Tumbes Mangrove ecosystem, these pressures are intensified by the absence of integrated spatial and educational infrastructures capable of supporting conservation efforts while engaging local communities. In response, this research proposes a Sustainable Interpretation Center for Conservation and Environmental Education in Ecologically Sensitive Areas of the Tumbes Mangrove, Peru. The methodology includes climate data analysis, identification of local flora and fauna, and site topography characterization, supported by digital tools such as Google Earth, AutoCAD 2025, Revit 2025, and 3D Sun Path. The results are reflected in an architectural proposal that incorporates sustainable materials compatible with sensitive ecosystems, including eco-friendly structural solutions based on algarrobo timber, together with resilient strategies addressing climatic variability, such as lightweight structures, elevated platforms, and passive environmental solutions that minimize impact on the mangrove. Furthermore, the proposal integrates a photovoltaic energy system consisting of 12 solar panels with a unit capacity of 450 W, providing a total installed capacity of 5.4 kWp, complemented by a 48 V LiFePO₄ battery storage system designed to ensure energy autonomy during periods of low solar availability. In conclusion, the proposal adheres to principles of sustainability and energy efficiency and aligns with the Sustainable Development Goals (SDGs) 7, 8, 12, 14, and 15, reinforcing the use of clean energy, responsible tourism, sustainable resource management, and the conservation of marine and terrestrial ecosystems. Full article

Solar panel power plants are typically established in regions with maximum solar irradiation, yet these conditions result in heavy dust accumulation on the panels causing significant performance degradation and reduced power output. The paper addresses this issue via an image-based dust detection solution powered by deep learning, particularly convolutional neural networks (CNNs). Most of such solutions use state-of-the-art CNNs either as backbones/features extractors, or propose custom models built upon them. Given such a reliance, future research requires a comprehensive benchmarking of CNN models to identify the ones that achieve superior performance on classifying clean vs. dusty solar panels both with respect to accuracy and efficiency. To this end, we evaluate 100 CNN models that belong to 16 families for image-based dust detection on solar panels, where the pre-trained models of these CNN architectures are used to encode solar panel images. Upon these image encodings, we then train and test a linear support vector machine (SVM) to determine the best-performing models in terms of classification accuracy and training time. The use of such a simple classifier ensures a fair comparison where the encodings do not benefit from the classifier itself and their performance reflects each CNN’s ability to capture the underlying image features. Experiments were conducted on a publicly available dust detection dataset, using stratified shuffle-split with 70–30, 80–20, and 90–10 splits, repeated 10 times. convnext_xxlarge and resnetv2_152 achieved the best classification rates of above 90%, with resnetv2_152 offering superior efficiency that is also supported by features analysis such as tSNE and UMAP, and explainableAI (XAI) such as LIME visualization. To prove their generalization capability, we tested the image encodings of resnetv2_152 on an unseen real-world image dataset captured via a drone camera, which achieved a remarkable accuracy of 96%. Consequently, our findings guide the selection of optimal CNN backbones for future image-based dust detection systems. Full article

In recent decades, accelerated urban growth in Arequipa has led to the loss of more than 40% of riparian vegetation and increased ecological fragmentation in the Chili River valley. This transformation has degraded water quality and limited equitable access to green and public spaces. Therefore, this research aims to design a Green Corridor along the Chili River as an ecosystem-based strategy to enhance social connectivity and ecological resilience in Arequipa, Peru. The methodology combined an extensive literature review, a comparative analysis of international case studies, and a territorial diagnosis supported by geospatial and climatic data. The process is supported by digital tools such as Google Earth Pro 2025, AutoCAD 2024, SketchUp Pro 2023, and solar simulations with Ladybug-Grasshopper, complemented by data from SENAMHI, SINIA, and the Solar Atlas of Peru. The results propose a resilient green corridor integrating passive and active sustainability strategies, including 40 photovoltaic panels, 44 solar luminaires, biodigesters producing between 90 and 150 kWh per month, and phytotechnologies capable of absorbing 75,225 kg of CO₂ annually, based on WHO conversion factors adapted to high-altitude conditions. The proposal employs eco-efficient materials such as reforested eucalyptus wood and volcanic sillar, creating recreational and productive spaces that promote social cohesion and circular economy. In conclusion, this study demonstrates the potential of ecosystem-based design to regenerate arid urban riverbanks, harmonizing environmental sustainability, social inclusion, and cultural identity. Thus, the Chili River corridor is consolidated as a replicable model of green-blue infrastructure for Andean cities, aligned with Sustainable Development Goals 6, 7, 11, 12, 13, and 15. Full article

This paper reviews recent progress in fault detection, reliability analysis, and predictive maintenance methods for grid-connected solar photovoltaic (PV) systems. With the rising adoption of solar power globally, maintaining system reliability and performance is vital for a sustainable energy supply. Common faults discussed include panel degradation, electrical issues, inverter failures, and grid disturbances, all of which affect system efficiency and safety. While traditional diagnostics like thermal imaging and V-I curve analysis offer valuable insights, they mostly detect issues reactively. New approaches using Artificial Intelligence (AI), Machine Learning (ML), and Internet of Things (IoT) enable real-time monitoring and predictive diagnostics, significantly enhancing accuracy and reliability. This study represents the introduction of a consolidated decision framework and taxonomy that systematically integrates and evaluates the fault types, symptoms, signals, diagnostics, and field-readiness across both plant types and voltage levels. Moreover, this study provides quantitative benchmarks of performance metrics, energy losses, and diagnostic accuracies of 95% confidence intervals. Adopting these advanced techniques promotes proactive management, reducing operational risks and downtime, thus reinforcing the resilience and sustainability of solar power infrastructure. Full article

Accumulation of dust on PV panels is a big challenge, especially in dry and semi-arid environments like Morocco, where the number of dust particles in the atmosphere diminishes the efficiency of solar panels severely. The review analyzes 30 recent studies, which provide insight into performance degradation by dust, as well as the search for solutions that mitigate this effect. Results show that dust reduced solar panel efficiency by between 10% and 40% based on environmental conditions, including dust density, composition, and length of exposure. Many technological approaches have been provided for the problem, including autonomous cleaning systems and advanced coatings, yet economic and scalability barriers are still in existence. Also, using AI in predictive maintenance provides good opportunities to optimize solar panel cleaning schedules to enhance energy production. This review concludes with the observation that, going forward, more research on long-term solutions and the development of sustainable and cost-effective cleaning technologies is urgently needed in order to better exploit solar energy in dusty environments. Full article

This paper presents a performance evaluation of a 140 kW solar array installed on the rooftop of the Mountain Line Transit Authority (MLTA) building in Morgantown, West Virginia (WV), USA, covering the period from 2013 to 2024. The grid-connected photovoltaic (PV) system consists of 572 polycrystalline PV modules, each rated at 245 watts. The study examines key performance parameters, including annual electricity production, average daily and annual capacity utilization hours (CUH), current array efficiency, and performance degradation. Monthly ambient temperature and global tilted irradiance (GTI) data were obtained from the NASA POWER website. During the assessment, observations were made regarding the tilt angles of the panels and corrosion of metal parts. From 2013 to 2024, the total electricity production was 1588 MWh, with an average annual output of 132 MWh. Over this 12-year period, the CO₂ emissions reduction attributed to the solar array is estimated at 1,413,497 kg, or approximately 117,791 kg/year, compared to emissions from coal-fired power plants in WV. The average daily CUH was found to be 2.93 h, while the current PV array efficiency in April 2024 was 10.70%, with a maximum efficiency of 14.30% observed at 2:00 PM. Additionally, an analysis of annual average performance degradation indicated a 2.28% decline from 2013 to 2016, followed by a much lower degradation of 0.17% from 2017 to 2023, as electricity production data were unavailable for most summer months of 2024. Full article

To elucidate the fatigue damage evolution of solar road panels under long-term loading and enhance their structural durability, this study develops a particle-based discrete element model and simulates fatigue responses under different structural configurations and loading rates. A strength degradation index was established by introducing peak stress and terminal stress, enabling quantitative evaluation of strength deterioration. Combined with fracture evolution, the dominant mesoscopic damage mechanisms were revealed. The results indicate that structural configuration strongly influences fatigue performance, with square panels showing the best resistance due to geometric symmetry and stable boundary constraints. Loading rate regulates damage evolution: lower rates promote structural coordination but may delay cumulative failure, while higher rates suppress overall deformation yet increase localized fracture risk. Based on these findings, a nonlinear predictive model of the strength degradation rate was constructed (R² = 0.935), offering reliable support for structural life prediction and design optimization. Finally, fatigue-resistant design strategies are proposed, including optimal structural configuration, controlled loading rates, bonding enhancement, and integration of online monitoring—providing both theoretical and technical guidance for high-performance, long-lifespan solar road systems. Full article

In the last decades, small satellites such as CubeSats and PocketQubes have become popular platforms for scientific and applied missions in low Earth orbit (LEO). However, prolonged exposure to atomic oxygen, ultraviolet radiation, and thermal cycling in LEO leads to gradual degradation of onboard solar panels, reducing mission lifetime and performance. This study addresses the need to quantify and compare the degradation behavior of different solar cell technologies and protective coatings used in nanosatellites and pico-satellites. The aim is to evaluate the in-orbit performance of monocrystalline silicon (Si), gallium arsenide (GaAs), triple-junction (TJ) structures, and copper indium gallium selenide (CIGS) cells under varying orbital and satellite parameters. Telemetry data from recent small satellite missions launched after 2020, combined with numerical modeling in GNU Octave, were used to assess degradation trends. The models were validated using empirical mission data, and statistical goodness-of-fit metrics (RMSE, R²) were applied to evaluate linear and exponential degradation patterns. Results show that TJ cells exhibit the highest resistance to LEO-induced degradation, while Si-based panels experience more pronounced power loss, especially in orbits below 500 km. Furthermore, smaller satellites (<10 kg) display higher degradation rates due to lower thermal inertia and limited shielding. These findings provide practical guidance for the selection of solar cell technologies, anti-degradation coatings, and protective strategies for long-duration CubeSat missions in diverse LEO environments. Full article

The corrosion within photovoltaic (PV) systems has become a critical challenge to address, significantly affecting the efficiency of solar-to-electric energy conversion, longevity, and economic viability. This review provides a comprehensive analysis of electrochemical corrosion mechanisms affecting solar panels and environmental factors that accelerate material degradation, including (i) humidity, (ii) temperature fluctuations, (iii) ultraviolet radiation, and (iv) exposure to saline environments, leading to reduced performance and premature failures. The role of encapsulation materials, solder interconnections, and conductive coatings in the corrosion formation process is examined. Various electrochemical and surface characterization techniques provide insights into material degradation and corrosion mechanisms within panels. Essential parameters are presented and discussed, including materials used, geographical location of analysis, environmental considerations, and corrosion characterization techniques, to enhance the assessment of solar panels. This review emphasizes the importance of corrosion management for sustainable PV systems and proposes future research directions for developing more durable materials and advanced coatings. Full article

The performance degradation of photovoltaic (PV) systems, comprising solar panels and DC-DC converters, is often caused by various anomalies related to manufacturing defects, operational conditions, or environmental factors. These faults significantly reduce energy output, preventing the system from reaching its nominal power and expected production levels. Given the demonstrated impact of such faults on PV system efficiency, an effective diagnostic method is essential for proactive maintenance and optimal performance. This paper presents a fault detection algorithm based on a Mamdani-type fuzzy logic approach. The proposed method utilizes three key inputs—panel current, panel voltage, and converter voltage—to assess system health. By computing the distortion ratios of these electrical parameters and processing them through a fuzzy logic controller, the algorithm accurately identifies fault conditions. Simulation results validate the effectiveness of this approach, demonstrating its capability to detect and classify 12 distinct faults in both the PV array and the DC-DC converter. The study highlights the potential of fuzzy logic-based diagnostics in enhancing the reliability and maintenance of photovoltaic systems. Full article

The development of the construction industry in Hong Kong and the UK has long played a vital role in economic development, advanced or otherwise, but has also brought formidable environmental challenges, particularly in terms of the huge volume of waste generated. This review paper puts under scrutiny the environmental management practices and green materials and technologies adoption in the construction industries of two developed regions, Hong Kong and the UK, the main objective being to compare their approaches to construction waste management and assess the level to which they have adopted sustainable practices. This review recognizes construction waste as a major contributor to environmental degradation and indicates the on-site waste reduction according to waste hierarchy as adopted by both regions. Major findings are that effective environmental management practices, such as resource optimization, waste minimization, and pollution prevention, are also enforced through legislation and fiscal policies. The use of eco-concrete, plastic wood, and recycled steel, together with high-tech roofs and solar panels, shows a move toward sustainable and energy-saving building that is taking root more and more. This paper highlights the need for policies and innovation in promoting sustainable building. Future studies should look into the green techs’ long-term performance, cross-area policy spread, and how digital tools help maximize waste and create sustainably. Full article

Solar photovoltaic (PV) panels are the best solution to reduce greenhouse gas emissions by fossil fuel combustion, with global capability now exceeding 714 GW due to rapid technological advances in solar panels (SPs). However, SPs’ efficiency and lifespan remain limited due to the absence of advanced fault-detection systems, and they are prone to short circuits (SC), open circuits (OC), and power degradation. Therefore, this large-scale production requires reliable, real-time fault diagnosis to maintain panel performance. However, traditional diagnostic methods implemented using MPPT, neural networks, or microcontroller-based systems often rely on complex computational algorithms and are not cost-effective. So, this paper proposes a diagnostic system composed of six functional blocks to address this issue. The proposed system was initially verified using an Intel DE-10 Lite FPGA board. Once its functionality was confirmed, an ASIC design was proposed for mass production, offering a significantly lower implementation cost and reduced hardware complexity than prior methods. Different circuit designs were developed for each of the six blocks. All designs were created using Cadence software and TSMC 180 nm technology files. The basic components used in these designs include PMOS transistors with 300 nm channel length and 2 µm width, NMOS transistors with 350 nm channel length and 2 µm width, as well as resistors and capacitors. Differential amplifiers with a gain of 40 dB were used for voltage and current sensing from the SP. The chip activation signal generator circuit was designed with an adjustable frequency and generated 120 MHz and 100 MHz signals in this work. The decision-making block, Logic Driver Circuit, was innovatively implemented using a reduced number of transistors. A custom memory block with a reset switch was also implemented to store the fault value detected at the SP. Finally, the proposed ASIC was implemented for fabrication, which is highly cost-effective in mass production and does not require complex computational stages. Full article

In this paper, we present the latest research results on the analysis of images taken during the condition assessment of solar cells and solar power plants. We aimed to summarize the most recent articles for 2024 and 2025. The annual volume of solar panels produced is expected to increase in the future. As imaging condition assessment technologies develop, the convolutional neural network models must follow this trend. In the field of real-time detection, CNN models will play an extremely important role because the faster any potential faults are identified, the quicker the response time during manufacturing and PV plant inspections. As part of CNN implementation in large PV power plants, IR and RGB imaging modes are very useful to detect failure sources. While IR imaging is useful in detecting heating from faults within PV panels or from nearby wiring, RGB imaging can detect mechanical defects such as broken glass planes, discolorations, and delamination. The implementation of these thus provides a higher chance of detecting solar panel damage and PV farms’ performance degradation or possible failure, resulting in a reduction in power generation interruptions. This will also allow faster and more efficient intervention and decision-making by operators in case of problems. Full article

The growing demand for renewable energy positions it as a cornerstone for climate change mitigation and greenhouse gas emissions reduction. Although renewable energy sources generate around 30% of global electricity, their production and deployment involve significant environmental challenges. This review analyzes renewable energy projects from a life cycle perspective, focusing on environmental impacts throughout the supply chain. Particular emphasis is placed on the energy-intensive nature of manufacturing phases, which account for 60% to 80% of total emissions. The extraction of critical raw materials such as neodymium, dysprosium, indium, tellurium, and silicon is associated with emission levels ranging from 0.02 to 0.09 kg of carbon dioxide equivalent per kilowatt-hour for rare earth elements, along with an estimated average land degradation of 0.2 hectares per megawatt installed. Furthermore, the production of solar-grade silicon for photovoltaic panels consumes approximately 293 kilowatt-hours of electricity per kilogram, significantly contributing to the overall environmental footprint. Through a comprehensive review of the existing literature, this study integrates life cycle assessment and sustainable supply chain management approaches to identify environmental hotspots, quantify emissions, and propose strategic improvements. The analysis provides a structured, systematized, and data-driven evaluation, highlighting the relevance of circular economy principles, advanced recycling technologies, and digital innovations to enhance sustainability, traceability, and resilience in renewable energy supply chains. This work offers actionable insights for decision-makers and policymakers to guide the low-carbon transition. Full article

In response to the stalling fault of the solar array drive assembly (SADA) in an in-orbit MEO satellite, an analysis and research on the energy balance algorithm are conducted. This is performed under the continuous changes in the light period, shadow period, and the incident angle of the solar panels. An output energy model of the solar panels is presented. It is proven that this model is a continuous function, and the optimal stalling angle for energy output is deduced. By simulating and calculating the energy output under different stalling angles and taking into account the on-orbit performance degradation of the solar cell array, the energy output curve within one orbital period is obtained, which provides support for the on-orbit operation and maintenance of the satellite. Moreover, on-orbit verification was carried out in the case of a stalling fault of the -Y-wing SADA of a certain MEO-orbiting satellite. Full article