This paper proposes a wireless low-cost solution based on long-range (LoRa) technology able to communicate with remote PV power plants, covering long distances with minimum power consumption and maintenance. This solution includes a low-cost open-source technology at the sensor layer and a low-power wireless area network (LPWAN) at the communication layer, combining the advantages of long-range coverage and low power demand. Moreover, it offers an extensive monitoring system to exchange data in an Internet-of-Things (IoT) environment. A detailed description of the proposed system at the PV module level of integration is also included in the paper, as well as detailed information regarding LPWAN application to the PV power plant monitoring problem. In order to assess the suitability of the proposed solution, results collected in real PV installations connected to the grid are also included and discussed.

The integration of renewables into power systems has led to multiple studies and analysis in terms of grid-power quality, reliability, and/or feasibility. Among different resources to be considered as alternative energy systems, wind and solar emerge as the most mature technologies. With regard to photovoltaic (PV) installations, monitoring problems requires detailed analysis, since solar-radiation fluctuations, soiling on solar panels, or deficiency of PV-panel performance can involve unexpected power-output oscillations and, subsequently, undesirable power-generation oscillations. Under this framework, this paper describes and assesses a wireless low-cost PV-module monitoring system based on open-source solutions. Our proposal allows us to monitor installations at the PV-module level, giving detailed information regarding PV power-plant performance. The proposed monitoring system is based on the IEC-61724 standard requirements, as a flexible and ad hoc solution with relevant connectivity options. Meteorological and electrical data are collected from the developed nodes and available for subsequent analysis. Detailed information of the solution, as well as extensive results collected in Spanish PV power plants connected to the grid, are also included in the paper.

In this work, we present power and quality measurements of four transmissions using different emission technologies in an indoor environment, specifically a corridor, at the frequency of 868 MHz under two non-line-of-sight (NLOS) conditions. A narrowband (NB) continuous wave (CW) signal has been transmitted, and its received power has been measured with a spectrum analyzer, LoRa and Zigbee signals have also been transmitted, and their Received Signal Strength Indicator (RSSI) and bit error rate (BER) have been measured using the transceivers themselves; finally, a 20 MHz bandwidth 5G QPSK signal has also been transmitted and their quality parameters, such as SS-RSRP, SS-RSRQ and SS-RINR, have been measured using a SA. Thereafter, two fitting models, the Close-in (CI) model and the Floating-Intercept (FI) model, were used to analyze the path loss. The results show that slopes below 2 for the NLOS-1 zone and above 3 for the NLOS-2 zone have been found. Moreover, the CI and FI model behave very similarly in the NLOS-1 zone, while in the NLOS-2 zone, the CI model has poor accuracy in contrast to the FI model, which achieves the best accuracy in both NLOS situations. From these models, the power predicted with the FI model has been correlated with the measured BER value, and power margins have been established for which LoRa and Zigbee would each reach a BER greater than 5%; likewise, −18 dB has been established for the SS-RSRQ of 5G transmission.

Due to the limitations of drones and satellites to obtain aerial images of the crops in real time, the time to flight delay, the problems caused by adverse weather conditions and other issues, the use of fixed cameras placed on the regions of interest is essential to get closer, periodic and on-demand images. Water management in agriculture is one of the most important applications of these images. Top view images of a crop can be processed for determining the percentage of green cover (PGC), and 2D images from different viewing angles can be applied for obtaining 3D models of the crops. In both cases, the obtained data can be managed for calculating several parameters such as crop evapotranspiration, water demand, detection of water deficit and indicators about solute transport of fertilizers in the plant. For this purpose, a remote image capture system has been developed for an application in lettuce crops. The system consists of several capture nodes and a local processing base station which includes image processing algorithms to obtain key features for decision-making in irrigation and harvesting strategies. Placing multiple image capture nodes allows obtaining different observation zones that are representative of the entire crop. The nodes have been designed to have autonomous power supply and wireless connection with the base station. This station carries out irrigation and harvesting decisions using the results of the processing of the images captured by the nodes and the information of other local sensors. The wireless connection is made using the ZigBee communication architecture, supported by XBee hardware. The two main benefits of this choice are its low energy consumption and the long range of the connection.

This paper discusses and evaluates a novel multi-channel real-time architecture aimed at monitoring a Proton Exchange Membrane (PEM) electrolyzer, both at the individual cell and stack levels. The proposed solution includes two primary subsystems: a hardware subsystem dedicated to data acquisition (DAQ) and a software subsystem focused on monitoring purposes. The DAQ subsystem utilizes an Arduino platform, being an affordable and open-source solution. The real-time monitoring data can be encoded in JSON format, widely used as a light-weight inter-exchange data format between a variety of IoT applications. They are also available to be transferred to Excel. Indeed, and to enhance convenience, the proposed system integrates graphs displaying a template based on Excel spreadsheets, which are commonly used in industrial environments. The current, voltage, temperature, and pressure data of both individual cells and stacks were monitored and collected, being configurable under a variety of ranges. As a case study, the validation of the system involved static and dynamic operational modes using a 1.2 kW PEM electrolyzer prototype (100 A, 1 A/cm${}^2$). The results successfully provided the monitored variables across individual cells and within the stack. The proposed approach exhibits relevant key characteristics such as scalability, flexibility, user-friendliness, versatility, and affordability and are suitable to monitor PEM electrolyzers in real-time at both the cell and stack levels.