Abstract
The Modular Multilevel Converter-High Voltage Direct Current (MMC-HVDC) system is recognized worldwide as a highly efficient strategy for transporting renewable energy across regions. As most of the MMC-HVDC system electronics are weak against overcurrent, protections of the MMC-HVDC system are the major focus of research. Because of the insufficiencies of the conventioned fault diagnosis method of MMC-HVDC system, such as hand-designed fault thresholds and complex data pre-processing, this paper proposes a new method for fault detection and location based on Bidirectional Gated Recurrent Unit (Bi-GRU). The proposed method has obvious advantages of feature extraction on the bi-directional structure, and it simplifies the pre-processing of fault data. The simplified pre-processing avoids the loss of valid information in the data and helps to extract detailed fault characteristics, thus improving the accuracy of the method. Extensive simulation experiments show that the proposed method meets the speed requirement of MMC-HVDC protections (2 ms) and the accuracy rate reaches 99.9994%. In addition, the method is not affected by noise and has a high potential for practical applications.
Highlights
With the gradual exhaustion of traditional fossil energy, the pressure on the ecological environment becomes serious, and the development of new energy is imperative
Plentiful test results show that increasing the number of Bidirectional Gated Recurrent Unit (Bi-GRU) layers and GRU neurons in the model improves the performance of the method, but the complexity and computational effort of the neural network increase correspondingly
To test the reliability of the fault diagnosis method, each sample set is randomly divided into a training set and a test set with appropriate proportions
Summary
With the gradual exhaustion of traditional fossil energy, the pressure on the ecological environment becomes serious, and the development of new energy is imperative. Parts of the fault diagnosis research are based on the fault mechanism model of the system He Zhen et al [10] propose a fault analysis method, which is based on Common- and Differential-Mode (CDM) network. The above research follows the process that first analyze the system fault mechanism and establish the equivalent circuit model, obtain the expression of fault current or voltage, realizing the fault diagnosis. Wang Qinghua et al [21] establish a neural network based on Long Short-Term Memory (LSTM) to detect and classify MMC faults with high accuracy. To accurately describe the transient process of the fault traveling wave, the DC transmission line adopts the frequency-varying parameter model. The P converter station adopts the constant DC voltage control method, and the other converter stations adopt the constant active power control method
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