RNN-based Fault Detection Method for MMC Photovoltaic Gridconnected System

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

Journal Name: Recent Advances in Electrical & Electronic Engineering
Formerly Recent Patents on Electrical & Electronic Engineering

Volume 14 , Issue 7 , 2021

Become EABM
Become Reviewer
Call for Editor


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.

Method: 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.

open access plus

Rights & PermissionsPrintExport Cite as

Article Details

Year: 2021
Published on: 17 September, 2021
Page: [755 - 766]
Pages: 12
DOI: 10.2174/2352096514666210917150429

Article Metrics

PDF: 326