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The fault characteristics of voltage source converter-based high voltage direct current （VSC-HVDC） transmission systems connected to offshore wind farms differ from those of conventional power systems. Consequently， there exists a potential risk of inadequate adaptability when conventional relay protection schemes are applied to the VSC-HVDC transmission system. This paper analyzes the impact of harmonic components induced by asymmetrical electrical quantities， negative sequence suppression， and current-limiting measures on the performance of differential protection during fault ride-through （FRT）. Sensitivity calculation formulas for differential protection considering sequence components are derived， and adaptability analysis is conducted. It is noted that differential protection is susceptible to odd-order harmonics during fault conditions， while negative sequence suppression and current-limiting measures may also diminish its sensitivity. Extensive simulation calculations are carried out using a simulated system of a practical demonstration project. The findings indicate a decrease in the sensitivity and reliability of conventional differential protection schemes.

Decomposing the output signal of fiber optical current transformer (FOCT) with time-frequency conversion is the key step to obtain the gradual fault characteristics. Aiming at the characteristics of FOCT gradual fault signal with large time domain span and random deterioration process,the output signal is sampled across intervals,and the wavelet packet decomposition algorithm is used to extract fault signal features according to the frequency band of the fault signal. The characteristic parameters are screened to obtain the optimal characteristic parameters that characterize the FOCT degradation trend.The principal component analysis method is proposed to reduce the dimensionality of high-dimensional features,the problem of high signal feature dimensionality is solved,and the need for fast fault feature identification is met. Experiment results show that the wavelet algorithm can decompose the signal into various frequency bands and obtain 64 sub-sequences containing signals of different frequency bands. The operating status of the transformer is determined by the energy ratio of each frequency band of the wavelet signal,and the gradual fault identification is realized.

DC converter stations have a high voltage level, a long transmission distance, and complex internal equipment, and contain power electronic devices, which seriously endanger the stable operation of the system itself and the active distribution network at the receiving end when faults occur. Accurate fault analysis and diagnosis are critical to the safe and stable operation of power systems. Traditional fault diagnosis methods often rely on a single source of information, leading to issues such as insufficient information utilization and incomplete diagnostic scope when applied to DC transmission systems. To address these problems, a fault diagnosis method for converter stations based on preliminary identification of the fault range and the fusion of evidence information of the switch signal and electrical quantity is proposed. First, the preprocessing of converter station sequential event recording (SER) events and a statistical analysis of event characteristics are completed to initially determine the range of the fault.Then, a fuzzy Petri net model and a BP neural network model are constructed on the basis of the fault data from a real-time digital simulation system (RTDS), and the corresponding evidence information of the switch signal and electrical quantity are obtained via iterative inference and deep learning methods. Finally, on the basis of D-S evidence theory, a comprehensive diagnosis result is obtained by fusing the switch and electric evidence information. Taking the fault data of a DC converter station as an example, the proposed method is analyzed and compared with the traditional method, which is based on single information. The results show that the proposed method can reliably and accurately identify fault points in the protected area of the converter station. [ABSTRACT FROM AUTHOR]

The correctness of the intelligent electronic devices (IEDs) virtual circuit connections in intelligent substations directly affects the stability of the system operation. Existing verification methods suffer from low efficiency in manual verification and lack uniformity in design specifications. Therefore, this paper proposes a virtual circuit automatic verification method that combines knowledge graphs with deep learning. Firstly, this method utilizes expert knowledge and relevant standard specifications to construct a knowledge graph of virtual circuits, integrating knowledge from historical intelligent substation configuration files into the knowledge graph. Then, leveraging multi-head attention mechanisms and Siamese neural networks, it achieves matching between the textual descriptions of virtual terminals and standard virtual terminal descriptions. Additionally, a verification process for the virtual terminal port address string is incorporated. Finally, experimental validation confirms the effectiveness of the proposed method and strategy, further enhancing the accuracy of virtual circuit verification. [ABSTRACT FROM AUTHOR]