A Low-Cost Monitoring and Fault Detection System for Stand-Alone Photovoltaic Systems Using IoT Technique

Mellit, A.; Hamied, A.; Lughi, Vanni; Pavan, Alessandro

doi:10.1007/978-3-030-37161-6_26

Cited by 12 publications

(8 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To test the PV power system, we plotted in Figure 7 the data acquisition system, the output current, voltage, solar irradiance, and air temperature for a short period (25 May 2021, from 10 am to 12 am, with a time step of 5 s). To track the maximum power, the maximum power point algorithm was integrated, and more details can be found in our previous work [25,26]. From Figure 7b, it can be seen that the produced PV current and voltage were enough to supply the main components of the greenhouse.…”

Section: Photovoltaic Power Supply Systemmentioning

confidence: 99%

Design of a Novel Remote Monitoring System for Smart Greenhouses Using the Internet of Things and Deep Convolutional Neural Networks

et al. 2021

Self Cite

View full text Add to dashboard Cite

To support farmers and improve the quality of crops production, designing of smart greenhouses is becoming indispensable. In this paper, a novel prototype for remote monitoring of a greenhouse is designed. The prototype allows creating an adequate artificial environment inside the greenhouse (e.g., water irrigation, ventilation, light intensity, and CO2 concentration). Thanks to the Internet of things technique, the parameters controlled (air temperature, relative humidity, capacitive soil moisture, light intensity, and CO2 concentration) were measured and uploaded to a designed webpage using appropriate sensors with a low-cost Wi-Fi module (NodeMCU V3). An Android mobile application was also developed using an A6 GSM module for notifying farmers (e.g., sending a warning message in case of any anomaly) regarding the state of the plants. A low-cost camera was used to collect and send images of the plants via the webpage for possible diseases identification and classification. In this context, a deep learning convolutional neural network was developed and implemented into a Raspberry Pi 4. To supply the prototype, a small-scale photovoltaic system was built. The experimental results showed the feasibility and demonstrated the ability of the prototype to monitor and control the greenhouse remotely, as well as to identify the state of the plants. The designed smart prototype can offer real-time remote measuring and sensing services to farmers.

show abstract

Section: Photovoltaic Power Supply Systemmentioning

confidence: 99%

Design of a Novel Remote Monitoring System for Smart Greenhouses Using the Internet of Things and Deep Convolutional Neural Networks

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Several monitoring systems with different technical solutions were published in 2019 [5,23,[41][42][43]. The monitoring systems for a small-scale microgrid that combined four RE installations (including PV systems and wind generators) were presented in [5].…”

Section: Data Processingmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

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.

show abstract

“…For PV fault detection, a GPRS communication-based IoT system is implemented; it provides an opportunity to monitor from any remote location [19]. An IoTbased prototype is implemented for fault detection and monitoring of stand-alone photovoltaic systems (SAPVS) [20,21]. Cloud server and Raspberry-pi controller-based IoT platform are utilized to implement accurate monitoring and control the solar power and PV array [22].…”

Section: Introductionmentioning

confidence: 99%

Automatic PV Grid Fault Detection System with IoT and LabVIEW as Data Logger

Samkria¹,

Abd‐Elnaby²,

Singh³

et al. 2021

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Fault detection of the photovoltaic (PV) grid is necessary to detect serious output power reduction to avoid PV modules' damage. To identify the fault of the PV arrays, there is a necessity to implement an automatic system. In this IoT and LabVIEW-based automatic fault detection of 3 × 3 solar array, a PV system is proposed to control and monitor Internet connectivity remotely. Hardware component to automatically reconfigure the solar PV array from the series-parallel (SP) to the complete cross-linked array underneath partial shading conditions (PSC) is centered on the Atmega328 system to achieve maximum power. In the LabVIEW environment, an automated monitoring system is developed. The automatic monitoring system assesses the voltage drop losses present in the DC side of the PV generator and generates a decimal weighted value depending on the defective solar panels and transmits this value to the remote station through an RF modem, and provides an indicator of the faulty solar panel over the built-in Interface LabVIEW. The managing of this GUI indicator helps the monitoring system to generate a panel alert for damaged panels in the PV system. Node MCU in the receiver section enables transmission of the fault status of PV arrays via Internet connectivity. The IoT-based Blynk app is employed for visualizing the fault status of the 3 × 3 PV array. The dashboard of Blynk visualizes every array with the status.

show abstract

A Low-Cost Monitoring and Fault Detection System for Stand-Alone Photovoltaic Systems Using IoT Technique

Cited by 12 publications

References 8 publications

Design of a Novel Remote Monitoring System for Smart Greenhouses Using the Internet of Things and Deep Convolutional Neural Networks

Design of a Novel Remote Monitoring System for Smart Greenhouses Using the Internet of Things and Deep Convolutional Neural Networks

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

Automatic PV Grid Fault Detection System with IoT and LabVIEW as Data Logger

Contact Info

Product

Resources

About