A novel dc fault protection scheme based on intelligent network for meshed dc grids

This paper proposes a fault detection and classification scheme for multi-terminal high voltage direct current (MT-HVdc) lines by integrating discrete wavelet transform (DWT) multi-resolution analysis with artificial neural networks (ANNs). Previously, such intelligent protection schemes used manual approaches or arbitrary rules of thumb to optimize a set of hyperparameters of the neural networks without applying any optimization algorithm. In order to improve accuracy, this work proposes an efficient Bayesian Optimization (BO) approach for evaluating and establishing the regulated hyperparameters for ANNs. The DWT multi-resolution analysis (MRA) and Parseval's theorem are used to extract energy variation for various faults. The energy variation of fault signals at different scales is fed into a multi-stage model to optimize the hyperparameters of neural networks with minimal training setup time and compute effort. After training, the data-based algorithm is implemented in a single-end main and coordinated secondary unit with control logic. The proposed scheme intends to detect internal short-circuit dc faults as quickly as possible and cover the failure of the main unit with expedited backup action. The findings of the study reveal that the proposed scheme can accurately detect internal faults in a variety of testing conditions and remain stable against external faults or disturbances with an average recognition accuracy of 99.38%.