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.
The Fault Detection, Localization, and Tolerant Operation of Modular Multilevel Converters with an Insulated Gate Bipolar Transistor (IGBT) Open Circuit Fault
