Bees are very important for terrestrial ecosystems and, above all, for the subsistence of many crops, due to their ability to pollinate flowers. Currently, the honey bee populations are decreasing due to colony collapse disorder (CCD). The reasons for CCD are not fully known, and as a result, it is essential to obtain all possible information on the environmental conditions surrounding the beehives. On the other hand, it is important to carry out such information gathering as non-intrusively as possible to avoid modifying the bees’ work conditions and to obtain more reliable data. We designed a wireless-sensor networks meet these requirements. We designed a remote monitoring system (called WBee) based on a hierarchical three-level model formed by the wireless node, a local data server, and a cloud data server. WBee is a low-cost, fully scalable, easily deployable system with regard to the number and types of sensors and the number of hives and their geographical distribution. WBee saves the data in each of the levels if there are failures in communication. In addition, the nodes include a backup battery, which allows for further data acquisition and storage in the event of a power outage. Unlike other systems that monitor a single point of a hive, the system we present monitors and stores the temperature and relative humidity of the beehive in three different spots. Additionally, the hive is continuously weighed on a weighing scale. Real-time weight measurement is an innovation in wireless beehive—monitoring systems. We designed an adaptation board to facilitate the connection of the sensors to the node. Through the Internet, researchers and beekeepers can access the cloud data server to find out the condition of their hives in real time.

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Abstract:The starting point of the operation and maintenance tasks in photovoltaic plants is the continuous monitoring and supervision of its components. The great amount of registered data requires a major improvement in the ways this information is processed and analyzed to rapidly detect any potential fault, without incurring additional costs. In this paper, a procedure to perform a detailed graphical supported analysis of the operation of photovoltaic installations, based on inverter data, and using a self-developed application, is presented. The program carries out the automated processing of the registered data, providing their access and visualization by means of color maps. These graphs allow a large volume of data set to be simultaneously represented in a readable way, enabling operation and maintenance operators to quickly detect patterns that would require any type of intervention. As a case study, the operation of a grid-connected photovoltaic plant located in southern Spain was studied during a period of three years. The average daily efficiency values of the PV modules and inverters were in the range of 7.6-14.6%, and 73.5-94% respectively. Moreover, the presence of shadings, as well as the hours and days mainly affected by this issue, was easily detected.