A new monitoring technique for fault detection and classification in PV systems based on rate of change of voltage-current trajectory

el‐Ghany, Hossam A. Abd; ELGebaly, Ahmed E.; Taha, Ibrahim B. M.

doi:10.1016/j.ijepes.2021.107248

Cited by 24 publications

(6 citation statements)

References 29 publications

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“…The ADF method is adopted in this study to select the dimensions required for automatic diagnosis. ADF test values were calculated to determine the stationarity of data dimensions under different confidence levels [8], as shown in Figure 1. The data dimensions greater than 97% of the stationarity test results are extracted and used as the dimensions of behavior automatic diagnosis.…”

Section: Screen the Dimensions Of Consumption Behavior Datamentioning

confidence: 99%

An automatic diagnosis method of power consumption anomaly of station users based on the k-medoids clustering algorithm

Liu,

Du,

Chang

et al. 2024

J. Phys.: Conf. Ser.

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Obtaining reliable data on electricity consumption can be difficult due to faulty or inaccurate data acquisition equipment. Therefore, a k-medoids clustering algorithm is used to design an automatic diagnosis method of power consumption anomaly. The K-Medoids algorithm was used to cluster the power consumption data of users in the Taiwan area. The data dimensions suitable for automatic diagnosis are screened by the ADF method. Based on this, the power consumption anomaly of the distribution network station area is automatically diagnosed, and the marked power consumption behavior data characteristics are checked step by step to realize the automatic power consumption anomaly diagnosis of station area users. The experimental results show that the K-medoids clustering algorithm can reasonably avoid the influence of transient abnormal data caused by isolated points on the automatic anomaly diagnosis results. For different types of abnormal automatic diagnosis rate of more than 98.6%, can accurately diagnose the abnormal power consumption of users in the station area.

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Section: Screen the Dimensions Of Consumption Behavior Datamentioning

confidence: 99%

An automatic diagnosis method of power consumption anomaly of station users based on the k-medoids clustering algorithm

Liu,

Du,

Chang

et al. 2024

J. Phys.: Conf. Ser.

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“…Penelitian sebelumnya telah dilakukan beberapa penelitian mengenai monitoring kinerja panel surya [3], [7]- [15]. Pada penelitian [14], sistem monitoring kinerja panel surya memfokuskan pada output daya dari panel surya dengan mengukur tegangan dan arus keluaran panel surya.…”

Section: Pendahuluanunclassified

Optimalisasi Labview Sebagai Kendali dan Monitoring Arus Tegangan pada Modul Solar Cell Menggunakan Jaringan Lokal

Dwi Riyanto,

Purwiyanto,

Dewi

2023

infotekmesin

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Renewable energy sources in Indonesia have been widely used, one of which is solar energy, for various needs, one of which is for solar power generation by using solar cells as the medium. In generating electricity, solar cells are very dependent on the intensity of the sun so as to produce currents and voltages that are not constant. Because of this, a monitoring system was created to monitor the current and voltage in a solar cell module to determine current and voltage conditions. In this study, the monitoring system used wireless data transmission via the XBee S2C module installed on the Arduino Mega 2560 and Arduino UNO. The INA 219 sensor is mounted on a solar cell module along with a relay module connected to the Arduino Mega 2560 and the XBee S2C module. The test results show that wireless data transmission cannot be used indoors at a distance of more than 20 meters. The INA 219 sensor as a reader for DC current and voltage parameters has an error rate of 0.49% to 0.75% for voltage and for DC current readings it has an error rate of 1.85%. The results of current and voltage readings are displayed on a LabVIEW interface in real-time and can be accessed via a web browser using a local network. Relay control carried out through LabVIEW has a delay time of 6.08 seconds. Logging data can be stored in .xls files and data displayed on a web browser has a delay of one to two seconds.

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“…The authors of [19] deployed the voltage sensor by distinguishing the odevity of the number of the photovoltaic array string, meanwhile, formulated the positioning rules for each fault to achieve accurate location of faults. Abd El-Ghany et al [20] installed a diode in each photovoltaic string and adopted the change rate of instantaneous voltage and current to detect the fault of photovoltaic system. In [21], the authors normalized the sequence voltage and current of photovoltaic array, and gained the sequence power waveform of photovoltaic array by numerical calculation, finally used semi-supervised ladder network for fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Ghost-RetinaNet: Fast Shadow Detection Method for Photovoltaic Panels Based on Improved RetinaNet

Wu¹,

Fan²,

Sun³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Based on the artificial intelligence algorithm of RetinaNet, we propose the Ghost-RetinaNet in this paper, a fast shadow detection method for photovoltaic panels, to solve the problems of extreme target density, large overlap, high cost and poor real-time performance in photovoltaic panel shadow detection. Firstly, the Ghost CSP module based on Cross Stage Partial (CSP) is adopted in feature extraction network to improve the accuracy and detection speed. Based on extracted features, recursive feature fusion structure is mentioned to enhance the feature information of all objects. We introduce the SiLU activation function and CIoU Loss to increase the learning and generalization ability of the network and improve the positioning accuracy of the bounding box regression, respectively. Finally, in order to achieve fast detection, the Ghost strategy is chosen to lighten the size of the algorithm. The results of the experiment show that the average detection accuracy (mAP) of the algorithm can reach up to 97.17%, the model size is only 8.75 MB and the detection speed is highly up to 50.8 Frame per second (FPS), which can meet the requirements of real-time detection speed and accuracy of photovoltaic panels in the practical environment. The realization of the algorithm also provides new research methods and ideas for fault detection in the photovoltaic power generation system.

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A new monitoring technique for fault detection and classification in PV systems based on rate of change of voltage-current trajectory

Cited by 24 publications

References 29 publications

An automatic diagnosis method of power consumption anomaly of station users based on the k-medoids clustering algorithm

An automatic diagnosis method of power consumption anomaly of station users based on the k-medoids clustering algorithm

Optimalisasi Labview Sebagai Kendali dan Monitoring Arus Tegangan pada Modul Solar Cell Menggunakan Jaringan Lokal

Ghost-RetinaNet: Fast Shadow Detection Method for Photovoltaic Panels Based on Improved RetinaNet

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