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Research Article
Parameters Estimation and Curves Analysis for Faults Evaluation of a Degraded Photovoltaic Module
Issue:
Volume 13, Issue 4, December 2024
Pages:
67-81
Received:
1 October 2024
Accepted:
28 October 2024
Published:
18 November 2024
Abstract: In the present work, a fault evaluation method for photovoltaic arrays based on fault parameters identification and curves analysis is proposed for diagnosing the state of photovoltaic generators. An overview of the components, the modelling of the photovoltaic generator and the meaning of the parameters is established for relating parameters to photovoltaic components and environmental conditions. The analysis and investigation of the relationship between the maximum power points and the parameters variations are performed. Investigation on how degradations and failure on photovoltaic systems can affect parameters, is established. In this context, the methodology for diagnosing and monitoring defects based on photovoltaic estimated parameters is developed; the optimization technique maximum likelihood, is used for extracting health and faults parameters from the measured curves of the photovoltaic array. From residual vectors, the parameters which vary more are the series resistance, the shunt resistor, and the current of photon. The maximum power also changes and decreases from its reference value. The validation results prove deviations on parameters, which means that there are degradations and failures on the ARCO Solar M75 array after 20 years of outdoors operation. So, at the end of this analysis, it is recommended to act on the PV system through junction box, cell edges, wiring, busbars, and connectors.
Abstract: In the present work, a fault evaluation method for photovoltaic arrays based on fault parameters identification and curves analysis is proposed for diagnosing the state of photovoltaic generators. An overview of the components, the modelling of the photovoltaic generator and the meaning of the parameters is established for relating parameters to ph...
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Research Article
Physicochemical Characterization and Production of Biodiesel from Cottonseed Oil and Waste Cooking Oil
Issue:
Volume 13, Issue 4, December 2024
Pages:
82-89
Received:
26 September 2024
Accepted:
21 October 2024
Published:
22 November 2024
Abstract: Biodiesel is an eco-friendly, alternative diesel fuel prepared from domestic renewable resources i.e. mainly produced from vegetable oils and animal fats. It is a renewable energy source that appears to be the perfect answer to the world's energy needs, especially those of Ethiopia. The aim of present study was to evaluate the physicochemical characterization and production of biodiesel from cottonseed oil and waste cooking oil. Transesterification of edible and non-edible oil with methanol in the presence of strong acid or base catalysts is the standard process for creating biodiesel. The percent yield of cottonseed crude oil was found to be 62.98 % upon the extraction from the cotton seeds. After food residues and sediments were removed using chemical coagulation with 2% Al2SO4 as a coagulating agent and gravitational sedimentation, approximately 90.24 percent of the oil was recovered. The physicochemical parameters of oils and its biodiesel were performed and the experimental results such as moisture content (0.32% and 0.27%), specific gravity (0.86-0.9258), viscosity (4.1-65mm2/sec), saponification value (56.1-182.3 mg/g), Iodine value (51.74-120.53 mgI2/g), Acid value (0.30-0.50 mg/g), free fatty acid content ¬¬¬(0.23-1.9%), cetane number (74.6-137.56) and higher heat values (40.87-48.94 MJ/kg) are good agreement with ASTM standards. In conclusion, the result of recent study confirmed that the cottonseed oil and waste cooking oil derived biodiesel is an alternative renewable biofuel for petro-diesel with an eco-friendly.
Abstract: Biodiesel is an eco-friendly, alternative diesel fuel prepared from domestic renewable resources i.e. mainly produced from vegetable oils and animal fats. It is a renewable energy source that appears to be the perfect answer to the world's energy needs, especially those of Ethiopia. The aim of present study was to evaluate the physicochemical chara...
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Review Article
Research Frontiers in Ecological Restoration and Carbon Sequestration in Mining Areas: A Visual Analysis Using VOSviewer
Yulong Wang,
Long Zhang,
Guoyan Zhu,
Chen Song,
Longgang Zhang,
Wei Chang,
Kun Li
,
Xiaohui Wang*
Issue:
Volume 13, Issue 4, December 2024
Pages:
90-99
Received:
29 October 2024
Accepted:
11 November 2024
Published:
28 November 2024
Abstract: The functioning and progress of modern industrial systems are deeply reliant on mineral resources. While mining offers substantial economic and social gains, it also imposes notable environmental impacts. In the context of global climate change, sustainable mining and ecological restoration in mined areas are increasingly connected to carbon sequestration efforts. Enhancing carbon sink capacity in ecological restoration processes is crucial for achieving carbon neutrality. This study aims to review the current research landscape, identify key research areas, and explore future trends in this field. Relevant literature from the Web of Science was selected, key information extracted, and co-occurrence networks were mapped and analyzed using VOSviewer. Covering publications from 2000 to the present, the analysis spans 84 countries and regions, 1,184 institutions, 3,757 authors, and 858 papers. The main research areas include: (1) strategies for ecological and vegetative restoration of mining areas; (2) carbon sequestration processes in vegetation and soil in mining areas; (3) mechanisms for soil health restoration in mining areas; (4) the role of plants and microbes in pollution remediation; (5) importance of water resource management and wetland restoration in mining areas; and (6) ecological succession and biomass accumulation in mining area rehabilitation. This study highlights major contributors, countries, and institutions, elucidates research hotspots, and outlines directions for future development. By systematically summarizing research trends and hotspots in ecological restoration and carbon sequestration in mining areas, this work provides a valuable reference for researchers seeking to navigate and advance this dynamic field.
Abstract: The functioning and progress of modern industrial systems are deeply reliant on mineral resources. While mining offers substantial economic and social gains, it also imposes notable environmental impacts. In the context of global climate change, sustainable mining and ecological restoration in mined areas are increasingly connected to carbon seques...
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Research Article
Optimizing EV Charging Infrastructure: A Data-Driven Approach to Predicting Power Demand and Analyzing Geographic Disparities
Archita Ruby Arun*
Issue:
Volume 13, Issue 4, December 2024
Pages:
100-108
Received:
30 October 2024
Accepted:
14 November 2024
Published:
25 December 2024
DOI:
10.11648/j.ijrse.20241304.14
Downloads:
Views:
Abstract: This paper presents a data-driven approach to optimizing electric vehicle (EV) charging infrastructure using a stacked ensemble learning model, which predicts power demand (kWh) per session to address challenges like long wait times, geographic disparities, and uneven resource allocation. Leveraging data from 85 EV drivers across 105 charging stations, the study identifies critical factors influencing station performance, such as session duration, time of day, and regional demand. Extensive preprocessing steps, including cyclical encoding of time-related variables, one-hot encoding of categorical features, and standardization of numerical variables, ensured the dataset was properly prepared for machine learning analysis. The stacked ensemble model combines Random Forest, XGBoost, and Neural Network models, effectively capturing both linear and non-linear relationships in the data. The results highlight significant urban-rural disparities in charging infrastructure. Urban stations exhibit higher and more consistent demand, whereas rural areas show sporadic and limited usage, underscoring the need for targeted infrastructure investment in underserved regions. Temporal patterns further reveal peak charging demand during business hours at workplace stations, emphasizing the potential for dynamic optimization of station placement and operational capacity based on usage trends. The model achieved a low Mean Squared Error (MSE) on training data (0.1577 kWh), but a higher MSE on test data (1.7875 kWh) indicates overfitting, suggesting the need for further refinement. Despite this limitation, the model offers valuable insights into optimizing EV charging networks, enabling policymakers and developers to improve infrastructure planning and reduce geographic inequities. Future work will focus on expanding the dataset to include residential and public charging scenarios, incorporating additional variables like weather and traffic patterns, and refining model architecture to improve generalization. This study contributes to building equitable and efficient EV charging networks, supporting the growing adoption of sustainable transportation.
Abstract: This paper presents a data-driven approach to optimizing electric vehicle (EV) charging infrastructure using a stacked ensemble learning model, which predicts power demand (kWh) per session to address challenges like long wait times, geographic disparities, and uneven resource allocation. Leveraging data from 85 EV drivers across 105 charging stati...
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