On-Line Faults Detection and Classification in PV Array Using Bayesian and K-Nearest Neighbor Classifier

Authors

Abstract

Nowadays, Distributed Generation (DG), especially PV systems, as - new sources of power, has attracted a considerable rate of investment. Fault detection and analysis in solar photovoltaic (PV) arrays are important issues to increase reliability, efficiency, and safety in PV arrays since faults in PV arrays are not properly recognized due to PV’s non-linear characteristics, current-limiting nature, high fault impedances, low irradiance conditions, the PV grounding schemes or inverter condition, protection system weakness. To fill this protection gap, therefore, machine learning techniques have been proposed for fault detection, based on PV array voltage, current measurements, irradiance, and temperature in a grid-connected 17.6 kW photovoltaic power system. . However, the choice of the best method of classification with high accuracy and the finding of suitable feature in commercial-scale photovoltaic arrays to determine the type and classification of the faults are important issues which have not been yet undertaken. The input data for using Bayesian and K-Nearest Neighbor Methods are the simulation results of different defined classes of the line to line and open circuit faults by various temperature and irradiance. The results have shown that using the suggested classification system is very successful in the detection and classification of faults in an array string.

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References

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Energy Engineering and Management
Volume 8, Issue 2
July 2018
Pages 14-25

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