Abstract
In order to remedy the current problem of having been buffeted by competing requirements for both protection sensitivity and quick reaction of High Voltage Direct Current (HVDC) transmission lines simultaneously, a new intelligent fault identification method based on Random Forests (RF) for HVDC transmission lines is proposed. S transform is implemented to extract fault current traveling wave of 8 frequencies and calculate the fluctuation index and energy sum ratio, in which the wave index is used to identify internal and external faults, and energy sum ratio is used to identify the positive and negative pole faults occurred on the transmission line. The intelligent fault identification model of RF is established, and the fault characteristic sample set of HVDC transmission lines is constructed by using multi-scale S transform fluctuation index and multi-scale S-transform energy sum ratio. Training and testing have been carried out to identify HVDC transmission line faults. According to theoretical researches and a large number of results of simulation experiments, the proposed intelligent fault identification method based on RF for HVDC transmission lines can effectively solve the problem of protection failure caused by inaccurate identification of traditional traveling wave wavefront or wavefront data loss. It can accurately and quickly realize the identification of internal and external faults and the selection of fault poles under different fault distances and transitional resistances, and has a strong ability to withstand transitional resistance and a strong ability to resist interference.
Highlights
Because of the vast territory of China, the unbalanced distributions of energy and load center determine the wide application of High Voltage Direct Current (HVDC) transmission technology, so that the rational utilization and optimal allocation of resources can be achieved[1]
In literature[18], Utilizing the ability of intelligent algorithms to learn features, a fault identification method for HVDC transmission lines based on multi-resolution singular spectrum entropy and Support Vector Machine (SVM) is proposed to identify internal and external faults, and small sized sample data is applied to identify the faults occurred on the transmission line
Stransform is a reversible local time-frequency analysis method, which is an extension of the ideas of continuous wavelet transform (CWT) and short-time Fourier transform (STFT) [23]
Summary
Because of the vast territory of China, the unbalanced distributions of energy and load center determine the wide application of HVDC transmission technology, so that the rational utilization and optimal allocation of resources can be achieved[1]. According to the attenuation characteristic of HVDC transmission line boundary elements to high frequency transient signals, references [3, 4, 5] respectively use wavelet energy, polar wave information entropy, and high frequency transient energy to quantitatively describe, analyze and estimate fault characteristics, so as to realize the identification of internal and external faults These methods can effectively identify internal and external faults, but the threshold setting has no theoretical basis and requires numerous simulation verification. In literature[18], Utilizing the ability of intelligent algorithms to learn features, a fault identification method for HVDC transmission lines based on multi-resolution singular spectrum entropy and Support Vector Machine (SVM) is proposed to identify internal and external faults, and small sized sample data is applied to identify the faults occurred on the transmission line. Theoretical analysis and numerous simulation results show that the proposed new method can accurately and quickly realize internal and external fault identification and fault pole selection under different fault distances and transitional resistances, and has a strong ability to withstand transitional resistance and a strong ability to resist interference
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