Aiming at the problem of fault diagnosis after the UHVDC system fails, a deep learning-based UHVDC fault diagnosis method under the cloud-edge architecture is proposed. First, based on the edge computing framework of the “cloud” + “edge terminal,” a four-layer fault diagnosis structure including the data integration layer, edge prediction layer, cloud diagnosis layer, and human-computer interaction layer is constructed. Then, a fault data set is constructed by finding effective information that can fully reflect the DC fault in the huge power grid environmental information, and the data set is screened, processed by classification feature fields, and linearly normalized. Finally, a deep convolutional generative adversarial network (DCGAN) is constructed by introducing a deep convolutional neural network (DCNN) into the traditional generative adversarial network (GAN) for data training and DC fault diagnosis. In addition, the corresponding process is given. The proposed method and the other three methods are compared and analyzed by simulation experiments. The results show that the method proposed has the highest accuracy and smallest error loss value of 95.6% and 0.18, respectively. It has the highest diagnosis accuracy under different fault types, and its performance is better than the other three comparison methods.
An optical DC current transformer anomaly handling mechanism is proposed to address the problem that the conventional DC current transformer anomaly handling mechanism cannot compensate for the defect of capacitor anomaly blocking. First, the implementation principle, modulation loop, demodulation method and its anomaly warning mechanism of the sine-wave modulated all-fibre-optic current transformer (FOCT) are investigated, and the effects of light source intensity and modulation voltage on current decoding are explained. The modulation loop is then simulated and modelled and a FOCT anomaly handling mechanism is proposed based on the Bessel function with real-time dynamic current compensation for small changes in modulation depth. Finally, an integrated dynamic test system for DC current transformers and DC protection is designed, and the actual system operation and fault model is established using the RTDS simulation system. The experiments demonstrate that the proposed FOCT anomaly handling and improvement measures can effectively improve the transient performance of FOCT, and at the same time provide a complete set of testing means for the engineering application and later upgrade and replacement of FOCT.
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