This work evaluates the use of particle swarm optimization to extract the parameters that represent a photovoltaic cell in the model of one diode and five parameters (1D5P) to obtain current versus voltage characteristic curves (I Â V) of a photovoltaic module operating under a wide range of temperature and irradiation scenarios. In all the modeling and simulation processes, only information of the module's data sheet provided by the manufacturers was used, without depending on results from other simulations or external data collections. To validate the study, the simulation of the current versus voltage (I Â V) curves of a commercial photovoltaic module was made, and this was compared with data provided by the manufacturer and also compared with Chenni's method, which is consolidated by the literature. Errors of less than 0.3% were obtained for the simulations performed under the standard test conditions and in temperature and irradiation situations commonly found in practice, as is the case, of G = 700 W/m 2 and T c = 40 C, the 1D5P method showed an average error of approximately 1%, slightly surpassing the Chenni's method where the error was approximately 1.4%. Statement of Industrial Relevance:In developing countries, such as Brazil, a significant increase in the portion referring to solar energy in the Brazilian energy grid is noted. As in 2016, this portion was only 0.02%, though in 2020, according to the Brazilian Solar Energy Association (ABSOLAR), 1 this portion had an increase of 75 times and reached the value of 1.5%. In addition, ABSOLAR's forecast for 2030 is that solar energy will reach 10% of the Brazilian energy grid. One of the reasons for this is that industrial consumer units are increasingly looking for alternative energy sources due to high energy inflation. Photovoltaic generators are an excellent option for industries because they are clean and sustainable energy. 2 It is silent and brings a financial return for the investor. Although, even with all the benefits, the photovoltaic generator has a high acquisition cost, especially for consumer units that demand much energy, as is the case of industries. Thus, correct and precise dimensioning for the photovoltaic generator is necessary. One way to increase the accuracy of dimensioning is to use simulation of the photovoltaic modules, which is the objective of this article.Novelty or Significance: The developed method uses the particle swarm optimization with constriction factor to simulate I Â V curves of a photovoltaic module for any
The monitoring of water resources through conventional methods, related to a manual process when performing the sample collection, followed by laboratory analysis, presents some difficulties concerning the logistics of the process, such as access to the interior of a lake, in addition to often being based on a small number of samples. The concept of the internet of things (IoT) is used here to collect data through five parametric probes contained in the floating station located inside a lake and inform them in real time continuously. The main objective of this research is to demonstrate the applicability of the IoT concept in the continuous monitoring of water in a lentic environment. Therefore, it is necessary to develop a tool for this. Upon reaching this objective, the advantages observed in this research confirmed that the IoT paradigm is an essential resource, justifying a natural tendency to establish itself when there is a need to collect data efficiently and continuously. Furthermore, the experimental result proves the IoT concept’s efficiency, agility, and reliability to environmental issues, especially regarding the most significant natural and indispensable resource for the planet, water.
The Internet of Things (IoT) has become widespread. Widely used worldwide, it already penetrates all spheres of life, and its symbiosis with the environment has become increasingly important and necessary. IoT in life sciences has gained much importance because it minimizes the costs associated with field research, shipments, and transportation of the sensors needed for physical and chemical measurements. This study proposes an IoT water monitoring system in real time that allows the measurement of dissolved oxygen levels in water at several monitoring points in a difficult-to-access location, the Pirapo River, in southern Brazil, responsible for supplying water to large urban centers in the region. The proposed method can be used in urban and rural areas for consumption and quality monitoring or extended to a modern water infrastructure that allows water providers and decision makers to supervise and make optimal decisions in difficult times. The experimental results prove that the system has excellent perspectives and can be used practically for environmental monitoring, providing interested parties with experiences acquired during the system implementation process and timely relevant information for safe decision making.
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