PV Monitoring System for a Water Pumping Scheme with a Lithium-Ion Battery Using Free Open-Source Software and IoT Technologies

Abstract

The development of photovoltaic (PV) technology is now a reality. The inclusion of lithium-ion batteries in grid-connected PV systems is growing, and the sharp drop in prices for these batteries will enable their use in applications such as PV water pumping schemes (PVWPS). A technical solution for the monitoring and tracking of PV systems is shown in this work, and a novel quasi-real-time monitoring system for a PVWPS with a Li-ion battery is proposed in which open-source Internet of Things (IoT) tools are used. The purpose of the monitoring system is to provide a useful tool for the operation, management, and development of these facilities. The experimental facility used to test the monitoring system includes a 2.4 kWpk photovoltaic field, a 3.6 kVA hybrid inverter, a 3.3 kWh/3 kW lithium-ion battery, a 2.2 kVA variable speed driver, and a 1.5 kW submersible pump. To address this study, data acquisition is performed using commercial hardware solutions that communicate using a Modbus-RTU protocol over an RS485 bus and open software. A Raspberry Pi is used in the data gateway stage, including a PM2 free open-source process manager to increase the robustness and reliability of the monitoring system. Data storage is performed in a server using InfluxDB for open-source database storage and Grafana as open-source data visualization software. Data processing is complemented with a configurable data exporter program that enables users to select and copy the data stored in InfluxDB. Excel or .csv files can be created that include the desired variables with a defined time interval and with the desired data granularity. Finally, the initial results of the monitoring system are presented, and the possible uses of the acquired data and potential users of the system are identified and described.

Photovoltaic Water Pumping: Comparison Between Direct and Lithium Battery Solutions

“…Some of the benefits of the PVWPS + LIB facilities can be verified by analyzing the plots in FIGURE 11 in which GI, P PV , SOC, and Q Values during 09/24 and 09/25 are represented in the graphic user interface built in Grafana [42]. FIGURE 11-d shows how the flow rate remains constant during all the pumping intervals, with the VSD operating at its rated conditions, and where electro-mechanical efficiency is at its maximum values.…”

“…The main parts of a DPVWPS facility, and the monitoring system, are described in [42]. FIGURE 1 shows a block diagram of the DPVWPS and details the main magnitudes that are acquired for the study.…”

“…This value corresponds to the stand-by consumption of the hybrid inverter. The measurement error was also corrected in the value of P PCU _out = 2150 W presented in FIGURE 13 and which according to the measurement of the hybrid inverter was 2289 W. The problems regarding the stand-by consumption of the hybrid inverter, mainly observed during the night, were reported in [42]. The decrease in the battery SOC during nights (around 25 %) is avoided by disconnecting the hybrid inverter when the WPS is not in operation.…”

“…This paper is within the framework of an R&D line that has already resulted in a previous study [42] on a PVWPS with a lithium-ion battery (PVWPS + LIB). In this previous study, the PV monitoring system developed for the PVWPS + LIB facility was described in detail.…”

Fault Prediction and Early-Detection in PV Power Plants based on Self-Organizing Maps

“…The implementation of accurate and systematic preventive maintenance strategies is emerging nowadays as an essential tool to maintain high technical and economic performance of solar photovoltaic (PV) plants over time [1]. Analytical monitoring systems have been installed worldwide to timely detect possible malfunctions through the assessment of PV system performance [2][3][4][5][6][7][8][9][10]. Due to the abundance of relevant data, and the difficulty in modeling many complex aspects of PV plants, statistical methods based on data mining and machine learning algorithms are recently emerging as a very promising approach both for fault prediction and early detection.…”