Levels Of Solar Radiation Research Articles

Designing a 10 MW peak solar power plant using a system advisor model (SAM software). Case study: Somalia, Mogadishu Region

Globally, there has been a sharp increase in energy needs due to industrialization and technological advancement. The wisest course of action is to switch from traditional energy sources to renewable energy sources, or ecologically friendly types of energy. Solar energy is one of the most abundant and widely accessible renewable energy sources. Photovoltaic (PV) systems using solar energy to generate electricity are weather-dependent. With the data available in the System Advisory Model (SAM), the Mogadishu region of Somalia can produce about 10 MW peak solar PV system design, which will be helpful to reach the country's target of total installed solar energy capacity by 2025. The SAM was used in this paper to design (system technical design and financial analysis) the small, medium, and large PV systems for the different countries. A 6% annual growth in producing capacity is the ultimate objective of the Somali National Development Plan (NDP). The nation's production capacity increased from 115 MW to 344 MW between June 2021 and June 2015. The target of the NDP is to raise generating capacity to 1043 MW between 2022 and 2027. It is intended to raise the electrification rate to 75% from its present 36% level. 41 MW of solar and 1 MW of wind power are Somalia's total installed capacity for renewable energy (RE). There are 3,000 hours of sunshine each year in the nation, and daily solar radiation levels range from 5 to 7 kWh/m2. Somalia has 41 MW of installed solar capacity and uses it nationwide. A solar photovoltaic system in Somalia attained a performance ratio of 70.8%. By 2030, the UN wants to run all of its operations with 80 percent renewable energy.

Techno-Economic Evaluation of a Residential Photovoltaic System: A Case Study From the Residential Complex Kass Suwailem in Hilla, Iraq

Recently, the construction of residential complexes has spread significantly in all cities of Iraq. Hence, incorporating renewable energy into residential buildings provides multiple advantages as an essential component of the global sustainability initiative. In this study, a techno-economic analysis of a photovoltaic (PV) system has been evaluated in residential buildings of Kass Suwailem I and II, Iraq-Hilla, as a case study. The evaluation involved the technical and economic feasibility of implementing a solar power generation system in this residential setting. Furthermore, it assessed the costs, benefits, and financial viability of installing and operating a PV system for electricity generation. The optimal size of the PV system based on the energy consumption, local solar irradiation levels, and electricity demand patterns for each apartment unit has been evaluated. For each apartment unit, the total PV system cost is assumed to be $12,470 consisting of a 14.7 kW array, 8-battery bank of 230 Ah each, controller, and 10 kW inverter. Life-Cycle Cost analysis has been employed to determine the comprehensive cost of the PV system, encompassing all expenditures accrued throughout its operational lifespan. The calculation of the installed PV system involves annual energy rationalization, along with determining the amount of cost reduction and comparing the system with a generator or any other source that uses fossil fuels.

Enhancing economic and performance effectiveness of a geo-solar system using multi aspect artificial neural network

Geo-solar systems, comprising ground-source heat pumps (GSHP) and solar thermal collectors, offer promising solutions for sustainable space heating and hot water provision in residential buildings. However, optimizing these systems for maximum energy efficiency and cost-effectiveness remains a challenge. In this context, this study presents a comprehensive approach to enhancing the performance and cost-effectiveness of geo-solar systems. The proposed methodology integrates artificial neural network (ANN) techniques and multi-aspect optimization to optimize system parameters. The detailed of building mathematical model incorporating energy and exergy equations, thermodynamic, and economic relations for all system components. The neural network model inputs to the include five neurons represents Geothermal temperature, Solar radiation levels, System pressure levels, Ambient temperature, and Operational time, and the model outputs represent Energy efficiency and Exergy destruction rate. Through the training of ANN using simulation data and the application of a multi-objective genetic algorithm, the system is optimized for energy efficiency, cost, and environmental impact simultaneously. The results of analysis show that the exergy efficiency of system is 22.02 % and turbine generates 1963 kW. Changes in P10, ranging from 2000 to 3400 kPa leads to the exergy destruction reduction from 8350 kW to 8000 kW, while the other parameters exhibit an increasing trend.

Dinamic Economic Dispatch (DED) Berbasis Mixed Integer Linear Programming (MILP) Dengan Mempertimbangkan Pembangkit EBT Pada Kelistrikan Pulau Sumbawa

In the Dynamic Economic Dispatch (DED) based on Mixed Integer Linear Programming (MILP) for the electricity of Sumbawa Island by considering RE (Renewable Energy) power plants, several aspects need to be considered, namely Variable Addition and Limitations: In the MILP model, the new variable must include the allocation of power from RE sources, such as solar power plants or geothermal power plants. Relevant technical and operational constraints should also be applied, such as capacity limits, characteristic curves and sustainability limits for renewable energy sources. The Sumbawa Island electricity system must consider the technical limitations related to RE Power plants. This includes capacity limitations, sustainability limitations (such as solar radiation levels or water discharge during the dry season), as well as the system's ability to handle variations in power generated by RE Power plants that depend on natural energy sources. One of the challenges in integrating RE Power plants is its variability. Renewable energy sources such as solar and hydropower tend to fluctuate in their power output depending on weather conditions. Therefore, the DED model must consider predictions or estimates of the power generated by RE Power plants to optimize accurate power allocation and minimize operational costs. The simulation results show that the Mixed Integer Linear Programming (MILP) method is able to provide a simple and economical solution to the DED problem. The change in operation using gas fuel and cofiring by 5% is a factor in the existence of minimal fixed power in the system. This thesis is a DED solution using Mixed Integer Linear Programming (MILP) by considering fuel changes as the main consideration in mathematical calculations to determine plant operation. With the change in PLTMG fuel type from B35 to gas, total generation costs decreased by 10.89%. With the change in PLTMG fuel type from B35 to gas and PLTU cofiring by 5%, total generation costs decreased by 10.94%.

Study of UV Index and Total Ozone Climatology Over Bhairahawa, Pokhara, and Lumle in Nepal

Recent studies on solar ultraviolet (UV) radiation have been pivotal in unveiling its detrimental impacts on humans and other living organisms on Earth. The Ultraviolet Index (UVI) is a crucial tool to ascertain the level of solar ultraviolet radiation at any given location, utilizing data from diverse sources. This study aims to examine the UV Index and Total Ozone Climatology over Bhairahawa (27.52°N, 83.43°E, 109m asl), Pokhara (28.22°N, 83.32°E, 850m asl), and Lumle (28.30°N, 83.80°E, 1740m asl), situated in the mid of Nepal, using OMI/Aura satellite data. The average minimum and maximum UV index recorded are 3.11±1.01 and 7.77±2.25 at Bhairahawa, 4.31±1.36 and 12.90±2.28 at Lumle, and 4.03±1.34 and 10.71±3.38 at Pokhara respectively. Additionally, the minimum Total Ozone Column (TOC) value is observed to be 251.4-288.5DU in December, with the maximum value reaching 291-285.3DU in April across all sites. This study concludes that the solar UV index increases with an increase in altitude, among other influential factors. The exploration of UVI and ozone data from the OMI/AURA satellite over specific locations in Nepal aims to enrich the existing pool of knowledge on solar ultraviolet radiation and its varying intensity, thereby contributing to the comprehension of its harmful implications on living entities.

Enhancing the solar water pumping efficiency through Beta MPPT method-controlled drive

This paper presents an innovative approach to achieve efficient solar water pumping through the integration of a Photovoltaic (PV) array and a Brushless Direct Current (BLDC) motor water pumping system. The system incorporates a Voltage Source Converter (VSC) with six switches, utilized to facilitate commutation. The inherent solar radiation is harnessed by the PV array, capitalizing on its renewable nature to generate electricity. By dynamically adjusting the switching states of the six VSC switches, the speed of the BLDC motor is modulated in response to the varying levels of available solar radiation. The BLDC motor's hall sensor signals play a crucial for determining the rotor's position and they are employed to generate precise commutation signals. The control strategy integrates the Incremental Conductance (INC) Maximum Power Point Tracking (MPPT) algorithm, which initially governs the commutation signals. To enhance adaptability to rapidly changing solar irradiation conditions, the control strategy dynamically updates the commutation signals using the innovative Beta MPPT algorithm. To assess the efficiency of the proposed control strategy, a comprehensive comparison between the INC and Beta MPPT algorithms is conducted using MATLAB Simulink. The performance of the BLDC motor under these algorithms was evaluated in terms of its ability to optimize energy extraction. The graphical analysis of these algorithms, considering the temporal aspect, substantiates the identification of the superior MPPT algorithm for BLDC motor control in solar water pumping applications. This study contributes to the advancement of solar water pumping systems by introducing a novel control approach that combines PV array utilization, VSC-based commutation, and a dual-step MPPT algorithm. The results demonstrate the effectiveness of the Beta MPPT algorithm by enabling the system to respond promptly to fluctuating solar irradiation conditions, thereby enhancing the overall efficiency of the solar water pumping process.

Wildfire susceptibility mapping by incorporating damage proxy maps, differenced normalized burn Ratio, and deep learning algorithms based on sentinel-1/2 data: a case study on Maui Island, Hawaii

ABSTRACT Climate change has contributed to the recent increase in wildfire occurrences, vegetation failures, human health risks, physical damage, and economic losses. Wildfire susceptibility mapping is an essential technique for assessing areas prone to wildfires. In this study, we proposed the combination of the damage proxy map (DPM) and differenced normalized burn ratio (dNBR) method to generate a precise wildfire inventory map and used it to predict areas susceptible to wildfire. The wildfire susceptibility maps were produced using frequency ratio (FR), convolutional neural network (CNN), and long short-term memory (LSTM)-based deep learning and their performances were compared. We implemented the proposed method on Maui Island, Hawaii, where wildfires frequently occur. We started the process by generating a wildfire inventory map from 2019 to 2023 based on the DPM method applied to Sentinel-1 synthetic aperture radar (SAR) data combined with a dNBR map retrieved from Sentinel-2 data. The wildfire inventory was randomly divided into a training dataset (70%) and a testing dataset (30%). Fifteen wildfire-related factors, including topographical, meteorological, land use, environmental, and anthropological factors, were selected to predict wildfires. The wildfire-related factors were selected by conducting study literature and considering spatial correlation analysis based on the FR method, information gain ratio analysis (IGR), and multicollinearity assessment using tolerance (TOL) and variance inflation factor (VIF) metrics. The level of susceptibility of an area to wildfire is divided into five, namely very high, high, moderate, low, and very low. The FR, CNN, and LSTM produced wildfire susceptibility maps with similar patterns, significantly influenced by land use and rainfall factors. The highly susceptible areas are located on gentle slopes covered by agricultural land and unhealthy vegetation, and these areas have low rainfall intensity but receive high levels of solar radiation. Meanwhile, areas with relatively low susceptibility occur in forests with high levels of wet canopy evaporation. The prediction results were evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), and the CNN performed slightly better than the FR and LSTM, with AUC values of 0.879, 0.877, and 0.870, respectively. Hence, the use of the CNN algorithm in the proposed method is appropriate, specifically for the study area. In addition, the susceptibility map provides information on susceptible areas on Maui Island, Hawaii, to increase public awareness.

Post-veraison sunlight supplementation improved the phenolic profile of Cabernet Gernischt (Vitis vinifera L.) grape and wine in a temperate monsoon climate

The regulation of solar radiation is a pivotal agricultural practice in enhancing the quality of grapes and subsequently, wine. In regions like Yantai, where solar radiation levels are typically low, the accumulation of wine phenolic compounds is negatively impacted. In light of this, our study sought to assess the efficacy of silver reflective mulch (RM) treatment on grape yield, maturity, and phenolic composition. Specifically, we applied RM treatment post-veraison to a Cabernet Gernischt (Vitis vinifera L.) vineyard in the Yantai wine region of China. Our findings indicate a consistent improvement in grape maturity with RM treatment, alongside enhanced accumulation of total phenols (TP), total tannins (TT), and total anthocyanins (TAC) in grape skin. Consequently, this led to elevated levels of phenolic compounds in wines and heightened color intensity. Moreover, tandem mass spectrometry analysis unveiled higher concentrations of key compounds such as petunidin-3-glucoside, malvidin-3-glucoside, procyanidin B1, quercetin-3-glucoside, cis-resveratrol, and piceatannol in the resultant wines. This study underscores the potential of RM treatment to bolster wine phenolics, particularly in regions characterized by a Temperate Monsoon Climate.

Impact of Solar Shading on Façades’ Surface Temperatures under Summer and Winter Conditions by IR Thermography

In warm climates with high levels of solar irradiation, solar shading plays a determinant role on buildings’ envelope performance, both during summer and winter conditions. In this research, an evaluation of the solar shading effect on sunny façades through IR thermography non-destructive testing was performed. Sunny and shaded areas revealed temperature differences of 7.4 °C in summer conditions and up to 1.2 °C in wintertime. Moreover, solar shading was shown to be beneficial not only for decreasing surface temperature in summertime but also for reducing convective air flow in wintertime. In addition, it was found that the prevalence of dense shadows, especially with non-reflective materials in louvres, is favorable. External Thermal Insulation Constructive Systems (ETICS) must be shadowed and the use of clear colors is recommended to reinforce homogeneity in the surface in wintertime and reduce solar absorptance in summertime. Under steady-state calculations, thermal losses can be reduced up to 30% at night in wintertime and up to 50–60% at daytime in summertime because of the shadowing. However, another important finding lied in the confirmation of the performance gap that arises between using air temperature, sol-air temperature and the actual surface temperature data, in such a way that the two former implied high levels of inaccuracy and overestimated the performance of the buildings compared to the actual behavior. Some of the main conclusions can be extrapolated to other circumstances.

A Multi-Stage Approach to Assessing the Echo-Tech Feasibility of a Hybrid SAM-CREST Model for Solar PV Power Plants in Maryland, USA

Maryland is actively working towards doubling its Renewable Portfolio Standard (RPS) target, aiming to increase the share of renewable energy from 25% by 2020 to 50% by 2030. Furthermore, Maryland stands out as a state that strongly supports solar initiatives, offering incentives and specialized programs to assist residents in adopting solar energy solutions. The paper presents a multi-stage approach: Stage 1—Location Selection Process, Stage 2—Technical Feasibility Study, and Stage 3—Economical Feasibility Study. In Stage 1, the study focuses on three potential solar farm locations in Maryland: Westover, Princess Anne, and Eden. Stages 2 and 3 involve a feasibility assessment with detailed technical analysis using the NREL System Advisor Model (SAM) and PVWatts to determine monthly power to the grid and Energy Yield. Subsequently, economic feasibility is assessed using the NREL Clean Renewable Energy Estimation Simulation Tool (CREST), focusing on competitive levelized costs of energy (LCOE), payback time, and cumulative cash flows. Results indicate that all three locations exhibit promising solar irradiance levels, system outputs, and potential energy yields. Due to high solar irradiation, the Westover area has the highest energy yield at 1583.13 kWh/kW, while Princess Anne boasts the highest system output at 333.59 GWh. The economic evaluation suggests that all three locations become profitable within a two-year payback time, with competitive levelized costs of energy (LCOE). Westover emerges as the most cost-effective option at 5.99 cents/kWh, attributed to its higher solar irradiation values and energy yield compared to Princess Anne and Eden. Cumulative cash flows provide insights into long-term profitability, with Princess Anne, MD, having the highest Cumulative Cash Flow over 25 years at $183,383,304. By evaluating technical and economic aspects, this feasibility study offers quantitative insights to guide decision-making for the installation of Solar PV, considering both technological and economic feasibility.

Optimizing the characteristic cooling curves of PCM-integrated thermally active buildings: Experimental and numerical investigations

Phase change materials (PCMs) exploit their cyclic phase change (solidification and melting) to enable latent heat storage. While incorporating them in thermally activated building systems can potentially enhance energy savings and thermal comfort, they can introduce significant challenges to the building energy management system. This work first reports the result of small-scale side-by-side experiments on two thermally active cuboids with and without BioPCMs at different supply water and PCM melting temperatures, as well as irradiance levels. Next, a dynamic model is developed in TRNSYS v18.05 for a typical design of an office space under hot desert climate conditions. The model is then integrated into a multi-objective optimization routine using NSGA-II algorithms to identify the optimal characteristic cooling curves, melting temperatures, and PCM thicknesses that simultaneously minimize energy consumption and maximize the percentage of comfortable hours. The experimental observations of the PCM-integrated cuboid highlighted energy savings between 80.45 and 84.56 %, compared to the reference cuboid, at nearly the same levels of indoor temperatures. The PCM panels reduced the cooling capacity of the radiant floor but also dampened air temperature variations by up to 83.17 %. Besides, higher solar irradiance levels, encountered in desert areas, were found to enhance the cooling capacity by up to 46.8 %. The Pareto solutions highlighted the possibility of achieving acceptable unmet hours of 3.16 % using PCMs and without the need for an assisting air system, compared to 14.1 % without PCMs. Besides, at the same unmet hours of the reference system, the PCMs reduced the total energy consumption and the peak cooling power by 9.75 and 41.2 %, which translates into substantial operation and capital investments, respectively. Moreover, the study demonstrated by example the importance of calibrating the commonly used cooling curves of thermally active buildings when planning for PCM integration.

Development of partitioned rectangular-shaped heat sink for photovoltaic thermal systems and performance evaluation using TiO2/Water-based nanofluids

The current study presents a partitioned rectangular-shaped heat sink for improving the PVT system performance. The experiment was examined under various experimental parameters. Five cooler block types with different dimensions were arranged to share the same total contact area with the panel back surface. These cooler blocks were placed in different arrangements, namely (PVT-I, PVT-II, PVT-III, PVT-IV, PVT-V), and studied comparatively using water. The dimensions and numbers of each cooler block arrangement were altered, while maintaining the same surface area in contact with the photovoltaic panel. Through the response surface methodology (RSM) optimization, the optimal arrangement with the best performance was determined, and then TiO2/nanofluid was applied to that arrangement with different volumetric concentrations. Using water, the highest electrical and thermal efficiencies were obtained by case PVT-III with values of 19.39 % and 63.72 %, respectively. This was followed by PVT-II, PVT-V, PVT-I, and PVT-IV in that order for electrical efficiency; and at the same time by PVT-II, PVT-I, PVT-IV, and PVT-V in that order for thermal efficiency. The RSM method recommended the optimal arrangement case as case PVT-III. By using nanofluid in that case arrangement, the highest electrical efficiency value was recorded upon using 0.3 wt% and operating the system at 1.5 L/m and 900 W/m2. Regarding the thermal efficiency results, in that case, the highest and lowest values were recorded upon using 0.3 wt% and 0.1 wt%, respectively, with values of 72.49 % and 31.19 %. Increasing the volumetric concentrations positively reflected on the system's performance, resulting in high efficiencies being achieved. In addition, the highest electrical efficiencies were obtained at high levels of solar irradiance and flow rates.

A novel data-driven model for prediction and adaptive control of pH in raceway reactor for microalgae cultivation

This work proposes a new data-driven model to estimate and predict pH dynamics in freshwater raceway photobioreactors. The resulting model is based purely on data measured from the reactor and divides the pH dynamics into two different behaviors. One behavior is described by the variation of pH due to the photosynthesis phenomena made by microalgae; and the other comes from the effect of CO2 injections into the medium for control purposes. Moreover, it was observed that the model parameters vary throughout the day depending on the weather conditions and reactor status. Thus, a decision tree algorithm is also developed to capture the parameter variation based on measured variables of the system, such as solar radiation, medium temperature, and medium level. The proposed model has been validated for a data set of more than 100 days during 10 months in a semi-industrial raceway reactor, covering a wide range of weather and system scenarios. Additionally, the proposed model was used to design an adaptive control algorithm which was also experimentally tested and compared with a classical fixed parameter control approach.

Realization of a Novel Artificial Intelligence Fuzzy Controller with Solar Irradiance-altered Maximum Power

This paper reports the realization of a novel artificial intelligence fuzzy Logic controller (AI-FLC) that attained the optimum power of the photovoltaic cell (PV array) with solar irradiance altered (from 300 to 1000 w/m2) using the boost-converter circuit. Experiments were conducted using the Simulink /MATLAB coding. The nonlinear current-voltage character of the PV array was observed to be affected by the weather conditions, solar irradiance level, and ambient temperature. In addition, unique maximum power point for each irradiance level was achieved which was ascribed to the rapid response of the FLC to the atmospheric changes without requiring any system parameters. The obtained accuracy was as much as 98.875%. The post converter circuit was connected to the buck converter for taking the advantage of the generated power, thereby stabilizing the synchronous generator-emanated voltage. The response of the proposed system was demonstrated to be better than the conventional PD control, wherein the output voltage was stable at 220 V even at low levels of the solar irradiance.

Analysis of Solar Water Heater with Modification Absorber Plate Integrated Thermal Storage

In various countries worldwide, solar water heater systems (SWHs) are utilized as devices that harness solar energy as a primary energy source. This study investigates the performance of SWHs numerical simulation by integrating phase change material (PCM) paraffin wax onto an absorber plate collector at the bottom for thermal storage. This study presents the thermal performance of a SWHs that uses an absorber plate with PCM for thermal energy storage. In this test, four variations of the model tested, namely a) standard flat plate (SFP), b) standard flat plate with PCM storage thickness 10mm (SFP+PCM 10mm), c) standard flat plate with PCM storage thickness 7mm (SFP+PCM 7mm), and d) standard flat plate with PCM storage thickness 4mm (SFP+PCM 4mm), were investigated using numerical simulation. Initially, an analytical investigation was conducted to examine the material qualities of paraffin wax utilised for phase change material (PCM) storage. The SWH systems were subsequently imported and simulated under three different levels of continuous solar radiation: 400 W/m2 700 W/m2 and 1000 W/m2. The simulation incorporates the use of computational fluid dynamics (CFD) tools. The results of the study revealed that the collector of the SWH system, which utilised an absorber plate containing phase change material (PCM) storage, demonstrated exceptional performance. Models with a thickness of 7mm PCM or SFP+PCM 7mm have the highest efficiency compared to other models with an efficiency value of 64%. There is a 3-4% increase in efficiency with variations in models that use PCM thermal storage compared to models that do not use PCM thermal storage.

The role of climatic variables on nest evolution in tanagers.

Avian nests are fundamental structures in avian reproduction and face strong selective forces. Climatic conditions are likely to have shaped the evolution of specific nest traits, but evidence is scarce at a macroevolutionary level. The Thraupidae family (commonly known as tanagers) is an ideal clade to understand the link between nest architecture and climate because it presents wide variation in nest traits. To understand whether climatic variables have played a role in the diversification of nest traits among species in this family, we measured nests from 49 species using museum collections. We observed that dome-nesting species are present in dryer and hotter environments, in line with previous findings suggesting that domed nests are a specialisation for arid conditions. We also found evidence that nests with thicker walls are present in locations with lower precipitation and that solar radiation can influence the shape of domed nests; birds tend to build shorter and narrower domes in areas with high levels of solar radiation. Open nest architecture is also potentially influenced by wind speed, with longer and deeper nests in areas characterised by strong winds. Our results support the hypothesis that different climatic variables can drive the evolution of specific aspects of nest architecture and contribute to the diversity of nest shapes we currently observe. However, climatic variables account only for a small fraction of the observed structural variation, leaving a significant portion still unexplained.

Assessment of thermohydraulic performance and entropy generation in an evacuated tube solar collector employing pure water and nanofluids as working fluids

This study conducts a numerical comparison of the thermal performance of three distinct working fluids (pure water, TiO2, and SiO2 water-based nanofluids) within an evacuated tube solar collector using Computational Fluid Dynamics. The study evaluates thermohydraulic performance alongside global and local entropy generation rates, while considering variations in solar radiation values and inlet mass flow rates. Results indicate that nanofluids demonstrate superior performance under low solar radiation, exhibiting higher outlet temperatures, velocities, thermal efficiency, and exergy efficiency compared to pure water. However, at the higher solar radiation level, the efficiency of SiO2 water-based nanofluid diminishes due to its impact on specific heat. Furthermore, the entropy generation analysis reveals significant reductions with TiO2 water-based nanofluid in all the phenomena considered (up to 79 %). The SiO2 nanofluid performance aligns closely with pure water under high radiation value. This investigation offers valuable insights into the utilization of nanofluids in solar collectors across diverse operating conditions, emphasizing their pivotal role in enhancing overall performance.

From Crops to Kilowatts: An Empirical Study on Farmland Conversion to Solar Photovoltaic Systems in Kushida River Basin, Japan

In Japan, rural areas are grappling with population decline and aging, leading to a shortage of labor for farmland maintenance. This has resulted in the abandonment of farmland or its conversion for solar photovoltaic (PV) use. However, this unplanned conversion raises concerns about agricultural productivity decline, landscape degradation, biodiversity loss, water resource maintenance, and disaster prevention. This study focuses on the Kushida watershed, examining (1) accurate farmland classification using remote sensing data, (2) the geographical distribution of farmland converted to PV systems from 2016 to 2021 and concentrated along the river, especially on north-facing slopes, (3) the highest conversion rates in wheat fields, followed by legume fields, tea fields, and paddy fields, and (4) no clear correlation between farmland conversions and changes in the number of farmers, but associations with farmland geography and solar radiation levels. These findings contribute to a nuanced understanding of sustainable rural development in Japan, emphasizing the importance of considering geographical factors in the conversion of farmland to PV.

Growth and flowering of carnation cultivated in different pot colors and protected environments

Growth and flowering of carnation cultivated in different pot colors and protected environments

Adaptive Particle Swarm Optimization of a Photovoltaic System under Partial Shading

The solar photovoltaic (PV) energy is the most prevalent and popular source of energy. But the PV output characteristics are mainly depending on temperature and irradiance and are nonlinear in nature. Therefore, PV array characteristics greatly vary with change in the atmospheric condition. Under partial shading condition (PSC), PV modules will not receive the same level of incident solar irradiance throughout the system due to some obstructions such as: dust, cloudy weather, shadows of nearby objects: buildings, trees, mountains, birds etc… which causes mismatch in PV module characteristics of the PV array and losses arise in the entire PV configuration. Consequently, power extraction from the PV system is reduced and the PSC on the PV array can be minimized by the proper selection of PV configurations, physical relocation of the PV modules and maximum power point tracking techniques (MPPT) to overcome this problem. The present article studies and compares the MPPT based on the Adaptive particle swarm optimization (APSO) algorithm under partial and completely shaded. The perturbation and observation (P&O) method is widely due to its simplicity and easy implementation but the Intelligent and hybrid control such as: fuzzy logic control (FLC) and adaptive neural fuzzy inference system (ANFIS) can track the MPP with better efficiency but in a long time compared to conventional approaches. In addition, these methods need big data for good results and the data problem is regulated with the evolutionary algorithms and searching the duty cycle (d) in a shorter time than FLC and ANFIS. The principle of PSO, grey wolf optimization (GWO), and APSO techniques is the search for a global solution, and it have good behaviour under PSC but APSO can be classified as best solution between the studied approaches. The simulation results, which are presented in MATLAB/Simulink software, show the effectiveness of the proposed APSO technique.