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Recent Advances in Electrical & Electronic Engineering


ISSN (Print): 2352-0965
ISSN (Online): 2352-0973

General Research Article

RNN-based Fault Detection Method for MMC Photovoltaic Gridconnected System

Author(s): Yuqi Pang, Gang Ma*, Xiaotian Xu, Xunyu Liu and Xinyuan Zhang

Volume 14, Issue 7, 2021

Published on: 17 September, 2021

Page: [755 - 766] Pages: 12

DOI: 10.2174/2352096514666210917150429

Price: $65


Background: Fast and reliable fault detection methods are the main technical challenges faced by photovoltaic grid-connected systems through Modular Multilevel Converters (MMC) during the development.

Objective: Existing fault detection methods have many problems, such as the inability of non-linear elements to form accurate analytical expressions, the difficulty of setting protection thresholds, and the long detection time.

Methods: Aiming at the problems above, this paper proposes a rapid fault detection method for photovoltaic grid-connected systems based on Recurrent Neural Network (RNN).

Results: The phase-to-mode transformation is used to extract the fault feature quantity to get the RNN input data. The hidden layer unit of the RNN is trained through a large amount of simulation data, and the opening instruction is given to the DC circuit breaker.

Conclusion: The simulation verification results show that the proposed fault detection method has the advantage of faster detection speed without difficulties in setting and complicated calculation.

Keywords: Photovoltaic grid-connected, recurrent neural network, fault identification, fault selection, DC side fault, submodule fault.

Graphical Abstract

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