Keywords

Photovoltaics, renewable energy, solar, fault detection, pnn, matlab, modeling, literature review

Abstract

Fault detection in PV systems is a key factor in maintaining the integrity of any PV system. Faults in photovoltaic systems can cause irrevocable damages to the stability of the PV system and substantially decrease the power output generated from the array of PV modules. Among'st the various AC and DC faults in a PV system, the clearance of the AC side faults is achieved by conventional AC protection schemes,the DC side, however , there still exists certain faults which are difficult to detect and clear. This paper deals with the modeling, detection and classification of these types of DC faults. It is essential to be able to simulate the PV characteristics and faults through software. In this thesis a comprehensive literature survey of fault detection methods for DC side of a PV system is presented. The disparities in the techniques employed for fault detection are studied . A new method for modeling the PV systems information only from manufacturers datasheet using both the Normal Operating Cell temperature conditions (NOCT) and Standard Operating Test Conditions (STC) conditions is then proposed.The input parameters for modeling the system are Isc,Voc,Impp,Vmpp and the temperature coefficients of Isc and Voc for both STC and NOCT conditions. The model is able to analyze the variations of PV parameters such as ideality factor, Series resistance, thermal voltage and Band gap energy of the PV module with temperature. Finally a novel intelligent method based on Probabilistic Neural Network for fault detection and classification for PV farm with string inverter technology is proposed.

Notes

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Graduation Date

2014

Semester

Summer

Advisor

Lotfifard, Saeed

Degree

Master of Science in Electrical Engineering (M.S.E.E.)

College

College of Engineering and Computer Science

Department

Electrical Engineering and Computing

Degree Program

Electrical Engineering

Format

application/pdf

Identifier

CFE0005293

URL

http://purl.fcla.edu/fcla/etd/CFE0005293

Language

English

Release Date

August 2019

Length of Campus-only Access

5 years

Access Status

Masters Thesis (Campus-only Access)

Subjects

Dissertations, Academic -- Engineering and Computer Science; Engineering and Computer Science -- Dissertations, Academic

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