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Modeling and fault detection in DC side of Photovoltaic Arrays

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Date Issued:
2014
Abstract/Description:
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.
Title: Modeling and fault detection in DC side of Photovoltaic Arrays.
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Name(s): Akram, Mohd, Author
Lotfifard, Saeed, Committee Chair
Mikhael, Wasfy, Committee Member
Wu, Thomas, Committee Member
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2014
Publisher: University of Central Florida
Language(s): English
Abstract/Description: 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.
Identifier: CFE0005293 (IID), ucf:50571 (fedora)
Note(s): 2014-08-01
M.S.E.E.
Engineering and Computer Science, Electrical Engr and Computing
Masters
This record was generated from author submitted information.
Subject(s): photovoltaics -- renewable energy -- solar -- fault detection -- PNN -- MATLAB -- Modeling -- Literature Review
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0005293
Restrictions on Access: campus 2019-08-15
Host Institution: UCF

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