A Robust Islanding Detection Method for Inverter-Based Distributed Generation Systems Using DC-Link Voltage Perturbation
High penetration of photovoltaic (PV) inverters in the utility grid is happening in several countries. PV inverters must be able to detect the utility grid disconnection and interrupt the injection of active power. This scenario is known as unintentional islanding. However, islanding detection metho...
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          | Published in | IEEE journal of photovoltaics Vol. 12; no. 6; pp. 1559 - 1566 | 
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| Main Authors | , , , , , | 
| Format | Journal Article | 
| Language | English | 
| Published | 
        Piscataway
          IEEE
    
        01.11.2022
     The Institute of Electrical and Electronics Engineers, Inc. (IEEE)  | 
| Subjects | |
| Online Access | Get full text | 
| ISSN | 2156-3381 2156-3403  | 
| DOI | 10.1109/JPHOTOV.2022.3196830 | 
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| Summary: | High penetration of photovoltaic (PV) inverters in the utility grid is happening in several countries. PV inverters must be able to detect the utility grid disconnection and interrupt the injection of active power. This scenario is known as unintentional islanding. However, islanding detection methods (IDM) are still subject to improvement, especially when considering the high penetration of inverters, making vulnerable IDMs not made for this case. Thus, the purpose of this article is to present a new IDM using dc-link voltage perturbation. The proposed method was defined through an algorithm, and its feasibility was tested through simulations and on an experimental setup with multiple inverters. Several scenarios were analyzed, in which the presented method was able to detect grid disconnections, while other methods failed in multiple inverter scenarios. As a result, the method has proven to be robust, even in the presence of several PV inverters connected at the same point. Furthermore, its implementation is easy and can be included in the inverter control firmware, without requiring any additional hardware. | 
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14  | 
| ISSN: | 2156-3381 2156-3403  | 
| DOI: | 10.1109/JPHOTOV.2022.3196830 |