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RNN-based Fault Detection Method for MMC Photovoltaic Gridconnected System

Author(s):

Yuqi Pang, Gang Ma*, Xiaotian Xu, Xunyu Liu and Xinyuan Zhang   Pages 1 - 12 ( 12 )

Abstract:


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.

Affiliation:

School of Electrical & Automation Engineering, Nanjing Normal University, Nanjing, School of Electrical & Automation Engineering, Nanjing Normal University, Nanjing, School of Electrical & Automation Engineering, Nanjing Normal University, Nanjing, School of Electrical & Automation Engineering, Nanjing Normal University, Nanjing, School of Electrical & Automation Engineering, Nanjing Normal University, Nanjing



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