The unique challenges posed by the high altitude and extreme-irradiance variability in the Peruvian Altiplano necessitate innovative and cost-effective monitoring solutions for photovoltaic (PV) systems. This study presents a low-cost wireless monitoring system for PV systems, designed for performance analysis and with potential application in DC nanogrids. The system, based on an Arduino Nano and Raspberry Pi architecture, captures real-time data on key electrical parameters such as voltage, current, and power, as well as environmental conditions like temperature and irradiance, which are critical factors influencing PV system performance. Deployed on a 3 kW grid-connected PV system in the Peruvian Altiplano, the system reveals significant irradiance variability, with fluctuations exceeding 20% within a single day and extreme events surpassing 1500 W/m². This variability resulted in an average daily energy generation fluctuation of 15%, underscoring the importance of continuous monitoring for optimizing PV system operation. This variability impacts energy generation and underscores the importance of continuous monitoring for optimizing PV system operation. The study analyzes the system’s performance under different irradiance conditions and discusses its adaptability for use in DC nanogrids, which offer enhanced efficiency and accessibility in remote areas like the Altiplano. This research contributes a practical and versatile tool for advancing sustainable energy solutions, with implications for improving the efficiency and reliability of both grid-connected PV systems and the emerging field of DC nanogrids in remote areas.

Micro inverters, with a distributed orientation and unlike a conventional inverter that had a centralized installation approach, are installed behind each solar panel and convert direct current electricity to alternating current, just below the panel. All micro inverters can be connected directly to the home network, independently of the other micro inverters in the system. When a panel is shaded, for example, it will in no way affect the performance of the other panels. The main objective of this work is to evaluate the efficiency and regulation of the 250W grid connection micro inverter operating in the geographical and climatic conditions of 3800 meters above sea level, to improve the operation of these systems in the area. from Puno. If the evaluation and regulation of the micro inverter will be carried out, we will have better efficiency in the work of this team, which is the micro inverter. The methodology will go with the correct voltage found through a technique called Maximum Power Point Tracking or MPPT. When the maximum power point is applied at the panel level rather than a system as a whole. Performance will increase dramatically, bringing a better solution to the customer. Finally, a measurement system was used that helped us to collect data. As a result, the efficiency of our micro inverter is greater than 90 percent, at the same time it works with power factors very close to unity, if the equipment works at a capacity greater than 20%, on the date of analysis the maximum efficiency was 93.215%. One of the conclusions in this case, as the power of the micro inverter is greater than that of the photovoltaic generator, does not have limitations or what guarantees that it operates in optimal conditions.

Classification green coffee beans is one of the main tasks during the quality grading process. This evaluation is normally carried out by specialist doing a visual inspection or using traditional instruments which have some limitations. This work is focused on the implementation of a computer vision system combining a hardware prototype and a software module. The hardware was made to guarantee the controlled conditions to capture the images of green coffee beans, the software is based on computer vision algorithms in order to detect defects of the coffee beans. The novelty of our proposal is the combination of algorithms to enhance the accuracy and the high number of defects detected. We applied a White Patch algorithm as an image enhancement procedure, color histograms as feature extractor and Support Vector Machine (SVM) for the classification task. It was constituted an image beans database of 1930 instances, and it was extracted 768 features, finally, the model was applied over 13 categories of defects described by the Specialty Coffee Association of America (SCAA). Results of classification achieved a 98.8% of overall accuracy detection, therefore the proposed system proved to be effective in classifying physical defects of green coffee beans. With this work we showed that the grading green coffee process can be automatized, adding a new paradigm in quality evaluation task to enhance the coffee industry.