Configurable IoT Open-Source Hardware and Software I-V Curve Tracer for Photovoltaic Generators

Design of a Portable Low-Cost I-V Curve Tracer for On-Line and In Situ Inspection of PV Modules

Identifying underperforming photovoltaic (PV) modules is crucial to ensure optimal energy production and financial returns, as well as preventing potential safety hazards in case of severe damage. To this aim, current-voltage (I-V) curve tracing can be employed as in situ monitoring technique for the early detection of faults. In this paper, we introduce a novel low-cost, microcontroller-based I-V tracer for the diagnosis of individual PV modules. The tool features a unique power conditioning circuit, facilitating accurate data acquisition under static conditions as well as the even distribution of the measured points along the I-V curve. A specific active disconnecting circuit enables in situ and on-line measurement without interrupting the string power generation. The designed prototype is used to characterize a set of PV modules under real operating conditions. The measured I-V curves exhibit expected trends, with the measured data closely matching theoretical values and an estimated mean relative error less than 3%.

Intelligent IoT Platform for Multiple PV Plant Monitoring

Due to the accelerated growth of the PV plant industry, multiple PV plants are being constructed in various locations. It is difficult to operate and maintain multiple PV plants in diverse locations. Consequently, a method for monitoring multiple PV plants on a single platform is required to satisfy the current industrial demand for monitoring multiple PV plants on a single platform. This work proposes a method to perform multiple PV plant monitoring using an IoT platform. Next-day power generation prediction and real-time anomaly detection are also proposed to enhance the developed IoT platform. From the results, an IoT platform is realized to monitor multiple PV plants, where the next day's power generation prediction is made using five types of AI models, and an adaptive threshold isolation forest is utilized to perform sensor anomaly detection in each PV plant. Among five developed AI models for power generation prediction, BiLSTM became the best model with the best MSE, MAPE, MAE, and R2 values of 0.0072, 0.1982, 0.0542, and 0.9664, respectively. Meanwhile, the proposed adaptive threshold isolation forest achieves the best performance when detecting anomalies in the sensor of the PV plant, with the highest precision of 0.9517.

PV Panel Model Parameter Estimation by Using Neural Network

Photovoltaic (PV) panels have been widely used as one of the solutions for green energy sources. Performance monitoring, fault diagnosis, and Control of Operation at Maximum Power Point (MPP) of PV panels became one of the popular research topics in the past. Model parameters could reflect the health conditions of a PV panel, and model parameter estimation can be applied to PV panel fault diagnosis. In this paper, we will propose a new algorithm for PV panel model parameters estimation by using a Neural Network (ANN) with a Numerical Current Prediction (NCP) layer. Output voltage and current signals (VI) after load perturbation are observed. An ANN is trained to estimate the PV panel model parameters, which is then fined tuned by the NCP to improve the accuracy to about 6%. During the testing stage, VI signals are input into the proposed ANN-NCP system. PV panel model parameters can then be estimated by the proposed algorithms, and the estimated model parameters can be then used for fault detection, health monitoring, and tracking operating points for MPP conditions.

Development and Testing of a 5G Multichannel Intelligent Seismograph Based on Raspberry Pi

A seismograph was designed based on Raspberry Pi. Although comprising 8 channels, the seismograph can be expanded to 16, 24, or 32 channels by using a USB interfacing with a microcontroller. In addition, by clustering more than one Raspberry Pi, the number of possible channels can be extended beyond 32. In this study, we also explored the computational intelligence of Raspberry Pi for running real-time systems and multithreaded algorithms to process raw seismic data. Also integrated into the seismograph is a Huawei MH5000-31 5G module, which provided high-speed internet real-time operations. Other hardware peripherals included a 24 bit ADS1251 analog-to-digital converter (ADC) and a STM32F407 microcontroller. Real-time data were acquired in the field for ambient noise tomography. An analysis tool called spatial autocorrelation (SPAC) was used to analyze the data, followed by inversion, which revealed the subsurface velocity of the site location. The proposed seismograph is prospective for small, medium, or commercial data acquisition. In accordance with the processing power and stability of Raspberry Pi, which were confirmed in this study, the proposed seismograph is also recommended as a template for developing high-performance computing applications, such as artificial intelligence (AI) in seismology and other related disciplines.

Strategic Investment in Open Hardware for National Security

Free and open-source hardware (FOSH) development has been shown to increase innovation and reduce economic costs. This article reviews the opportunity to use FOSH as a sanction to undercut imports and exports from a target criminal country. A formal methodology is presented for selecting strategic national investments in FOSH development to improve both national security and global safety. In this methodology, first the target country that is threatening national security or safety is identified. Next, the top imports from the target country as well as potentially other importing countries (allies) are quantified. Hardware is identified that could undercut imports/exports from the target country. Finally, methods to support the FOSH development are enumerated to support production in a commons-based peer production strategy. To demonstrate how this theoretical method works in practice, it is applied as a case study to a current criminal military aggressor nation, who is also a fossil-fuel exporter. The results show that there are numerous existing FOSH and opportunities to develop new FOSH for energy conservation and renewable energy to reduce fossil-fuel-energy demand. Widespread deployment would reduce the concomitant pollution, human health impacts, and environmental desecration as well as cut financing of military operations.

On the Construction of an Edge-Based Remote Sensing Framework: The Applications on Automated Guided Vehicles and Drones

To monitor the status and mission progress of automated guided vehicles (AGVs), most platforms typically obtained real-time data through a data acquisition system that is deployed on the end vehicles. The data acquired from an end vehicle are generally sparse but are required frequently, and an examination process using cloud storage cannot commence until the device’s raw data are received. To reduce communication costs, the proposed edge-based monitoring system (EMS) applies edge computation to move the data examination from the cloud to an end site. The data buffered in the end device could be pre-processed by some detectors. For example, checking the energy is adequate for returning to the base. Thus, buffering data on the end device helps to minimize the time required by the decision maker for abnormal events, e.g., shutdowns caused by exhausted energy. In addition to adopting the common methods of storing, processing, and analyzing data at the data center, the EMS moves some time-sensitive services to the end vehicle. Moreover, after obtaining real-time motion data, the edge computing architecture immediately targets abnormal actions and sends reaction commands to shorten the decision making delay caused by the communication cost between the end vehicles and cloud storage sites, thereby avoiding collisions or accidents. The EMS has been implemented to monitor AGV and unmanned aerial vehicles. The EMS primarily monitored the power and motion of the vehicles. It also combined task-oriented motion commands for monitoring unexpected vehicle motions during tasks. If an abnormal event occurred, immediate warnings were provided through a notification interface and were immediately processed by the EMS to ensure safety during task execution. After checking data consistency between the EMS and the real device, the EMS reveals the corrected status of the device with very little delay. Therefore, the EMS could help with minimizing the time taken to make decisions. Moreover, the EMS has been modified to be deployed on drones to confirm its cross-platform applicability. In the simulations of drones, the EMS also got similar results to the simulations of AGVs. Therefore, the EMS could reduce the time in examining abnormal events and has cross-platform functionality.

An Open-Source System for Generating and Computer Grading Traditional Non-Coding Assignments

One of the most time-consuming activities in higher education is reviewing and grading student evaluations. Rapid and effective feedback of evaluations, along with an appropriate assessment strategy, can significantly improve students’ performance. Furthermore, academic dishonesty is a major issue in higher education that has been aggravated by the limitations derived from the COVID-19 pandemic. One of the possible ways to mitigate this issue is to give different evaluations to each student, with the negative cost of increasing reviewing time. In this work, an open-source system developed in Python to automatically create and correct evaluations is presented. Using Jupyter Notebook as the graphical user interface, the system allows the creation of individual student question sheets, with the same structure and different parameter values, to send them to students, grade them, and send the final score back to the students. The proposed system requires little programming knowledge for the instructors to use it. The system was applied in Civil Engineering and Geological Engineering programs at the Universidad Católica de la Santísima Concepción, drastically reducing grading time while improving students’ performance.

IoT Solutions for Maintenance and Evaluation of Photovoltaic Systems

The effective operation of photovoltaic systems depends on many factors and parameters that must be continuously monitored. The factors listed in the article are frequently variable, which makes it very difficult to predict the amount of radiation that will reach photovoltaic panels and can be converted into electricity. Therefore, to optimize the operating point of a photovoltaic power plant, it is necessary to track the changes in these quantities. IoT systems may help in controlling and managing a power plant, storage, and energy flow to the power grid. The results recorded at the hybrid power plant of the Faculty of Electrical Engineering of the Bialystok University of Technology are useful for a comprehensive analysis of the operation of the plant and ways of its optimization. It is shown that implementation of a comprehensive maintenance system may deliver extensive important information regarding the PV plant installation.