Online two-section PV array fault diagnosis with optimized voltage sensor locations

Abstract

Photovoltaic (PV) stations have been widely built in the world to utilize solar energy directly. In order to reduce the capital and operational costs, early fault diagnosis is playing an increasingly important role by enabling the long effective operation of PV arrays. This paper analyzes the terminal characteristics of faulty PV strings and arrays, and it develops a PV array fault diagnosis technique. The terminal current-voltage curve of a faulty PV array is divided into two sections, i.e., high-voltage and low-voltage fault diagnosis sections. The corresponding working points of healthy string modules and of healthy and faulty modules in an unhealthy string are then analyzed for each section. By probing into different working points, a faulty PV module can be located. The fault information is of critical importance for the maximum power point tracking and the array dynamical reconfiguration. Furthermore, the string current sensors can be eliminated, and the number of voltage sensors can be reduced by optimizing voltage sensor locations. Typical fault scenarios including monostring, multistring, and a partial shadow for a 1.6-kW 3 $times$ 3 PV array are presented and experimentally tested to confirm the effectiveness of the proposed fault diagnosis method.

Publication DOI: https://doi.org/10.1109/TIE.2015.2448066
Divisions: College of Engineering & Physical Sciences
Additional Information: © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publication ISSN: 1557-9948
Last Modified: 18 Mar 2024 08:17
Date Deposited: 29 Jun 2016 13:05
Full Text Link: http://ieeexplo ... rnumber=7128691
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PURE Output Type: Article
Published Date: 2015-11
Published Online Date: 2015-06-19
Accepted Date: 2015-06-07
Authors: Hu, Yihua
Zhang, Jiangfeng
Cao, Wenping (ORCID Profile 0000-0002-8133-3020)
Wu, Jiande
Tian, Gui Yun
Finney, Stephen J.
Kirtley, James L.

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Version: Accepted Version


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