Abstract:This study presents an investigation of the impact of the quasi‐stationary voltage support provided by a voltage source converter (VSC) connected to a single point of a power system. Based on the directional derivative concept, an analytical method is developed to quantify the sensitivities of the AC bus voltage with respect to the VSC reactive power control modes. Based on a real case study, it is shown that the method applies to VSC units that are part of VSC‐HVDC systems, which can operate in a point‐to‐poi… Show more
“…High-voltage direct current (HVDC)/Muti-terminal direct current (MTDC) is proposed as a promising technology for super-grid or collecting bulk renewable energy sources, that the amount of projects has been considerably increased since the last decade [1][2][3]. However, the insensitivity of high impedance, unsoundness of close to terminal fault detection and distraction of noise result in immaturity of the HVDC protection system.…”
In order to overcome the drawbacks of the conventional protection methods in high‐voltage direct current transmission lines, a deep learning approach is proposed that directly learn the fault conditions based on unsupervised feature extraction to the detection and location decision by leveraging the hidden layer activations of recurrent neural network. The deep‐recurrent neural network boosting with the gated recurrent unit compared with the long short‐term memory unit is used by analysing both the signal presented in time domain and frequency domain. The proposed method is tested based on a modular multilevel converter based four‐terminal high‐voltage direct current system. Various faults under different conditions were simulated against fault resistance, external faults and small disturbance immunity with the validity, and the simulation verified a high accuracy, robustness and fast results because of the utilization of characteristic feature extraction.
“…High-voltage direct current (HVDC)/Muti-terminal direct current (MTDC) is proposed as a promising technology for super-grid or collecting bulk renewable energy sources, that the amount of projects has been considerably increased since the last decade [1][2][3]. However, the insensitivity of high impedance, unsoundness of close to terminal fault detection and distraction of noise result in immaturity of the HVDC protection system.…”
In order to overcome the drawbacks of the conventional protection methods in high‐voltage direct current transmission lines, a deep learning approach is proposed that directly learn the fault conditions based on unsupervised feature extraction to the detection and location decision by leveraging the hidden layer activations of recurrent neural network. The deep‐recurrent neural network boosting with the gated recurrent unit compared with the long short‐term memory unit is used by analysing both the signal presented in time domain and frequency domain. The proposed method is tested based on a modular multilevel converter based four‐terminal high‐voltage direct current system. Various faults under different conditions were simulated against fault resistance, external faults and small disturbance immunity with the validity, and the simulation verified a high accuracy, robustness and fast results because of the utilization of characteristic feature extraction.
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