A Review of Conventional Fault Detection Techniques in Solar PV Systems and a Proposal of Long Range (LoRa) Wireless Sensor Network for Module Level Monitoring and Fault Diagnosis in Large Solar PV Farms

Okere, Aruoriwoghene; Iqbal, M. Tariq

doi:10.24018/ejece.2020.4.6.267

Cited by 7 publications

(2 citation statements)

References 20 publications

(31 reference statements)

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“…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]. In [23], the authors introduced a monitoring framework (MS) to calculate electrical and environmental variables to process instantaneous and historical data, enabling the estimation of plant productivity parameters.…”

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

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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.

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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

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“…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.…”

Section: Introduction 1motivationmentioning

confidence: 99%

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

Betti¹,

Tucci²,

Crisostomi³

et al. 2021

Preprint

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In this paper a novel and flexible solution for fault prediction based on data collected from Supervisory Control and Data Acquisition (SCADA) system is presented. Generic fault/status prediction is offered by means of a data driven approach based on a self-organizing map (SOM)and the definition of an original Key Performance Indicator (KPI). The model has been assessed on a park of three photovoltaic (PV) plants with installed capacity up to 10 MW, and on more than sixty inverter modules of three different technology brands. The results indicate that the proposed method is effective in predicting incipient generic faults in average up to 7 days in advance with true positives rate up to 95%. The model is easily deployable for on-line monitoring of anomalies on new PV plants and technologies, requiring only the availability of historical SCADA data, fault taxonomy and inverter electrical datasheet.

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