High voltage direct current has been more and more popular in modern transmission systems. Accurate fault location could help fault clearance and fast recovery of the faulted system. A stacked denoising autoencoder based fault location method for high voltage direct current transmission systems is proposed. The local measurements are analysed, and an end-to-end stacked denoising autoencoder-based fault location is realised. Representative features are extracted with unsupervised learning and labelled as the input of the regression network for fine-tuning in a supervised manner. The trained network can precisely map the local measurements and their corresponding fault distance. The performance of the proposed method is tested on a point-to-point high voltage direct current transmission system, which is modelled on the platform of PSCAD/EMTDC. The faults on both overhead lines and cables are considered, and the location performance in different scenarios are discussed. The simulation results show that the proposed method is effective in pinpointing faults location in various cases.

Lightning is one of the most common transient interferences on overhead transmission lines of high-voltage direct current (HVDC) systems. Accurate and effective recognition of faults and disturbances caused by lightning strokes is crucial in transient protections such as traveling wave protection. Traditional recognition methods which adopt feature extraction and classification models rely heavily on the performance of signal processing and practical operation experiences. Misjudgments occur due to the poor generalization performance of recognition models. To improve the recognition rates and reliability of transient protection, this paper proposes a transient recognition method based on the deep belief network. The normalized line-mode components of transient currents on HVDC transmission lines are analyzed by a deep belief network which is properly designed. The feature learning process of the deep belief network can discover the inherent characteristics and improve recognition accuracy. Simulations are carried out to verify the effectiveness of the proposed method. Results demonstrate that the proposed method performs well in various scenarios and shows higher potential in practical applications than traditional machine learning based ones. Index Terms-Deep belief network, transient recognition, machine learning, voltage source converter based high-voltage direct current (VSC-HVDC).

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