MMC-HVDC grids transmission line protection method: Based on permutation entropy algorithm

• The permutation entropy algorithm has good performance in analyzing the degree of signal disorder. Based on this principle, a novel MMC-HVDC grids transmission line protection method is proposed to identify faults in MMC-HVDC grids transmission lines. • Due to the characteristics of reactors, the high-frequency signal components of voltage differ during internal and external faults. This article distinguishes between internal and external faults based on this feature by calculating the permutation entropy of high-frequency detail coefficients. It also identifies fault types using the permutation entropy of high-frequency detail coefficients and low-frequency detail coefficients. • The proposed method using Δ u > 0.1 u N as the starting criterion for protection. In order to avoid the impact of voltage fluctuations and coupling on DC transmission lines, real-time sampled voltage data was processed using MATLAB. • Compared with other existing methods, this method is simpler and only requires single- terminal voltage sampling data to identify faults and does not require two-terminal synchronous communication. In addition, this method has a low sampling frequency and can accurately identify faults under 30 dB Gaussian white noise, which has high engineering application value. Modular multilevel converter high-voltage direct current is increasingly used for electric energy transmission and is a method for addressing energy distribution. However, when a DC short circuit fault occurs in the transmission line, the fault current rapidly increases, posing a threat to the normal operation of the MMC-HVDC grid. Therefore, it is necessary to quickly identify faults in the transmission line to protect the safe operation of the grid. To address this issue, this paper proposes a novel protection method for MMC-HVDC grids based on the permutation entropy algorithm. The permutation entropy algorithm exhibits good performance in analyzing the degree of disorder in signal data. In this paper, wavelet transform is used to decompose the sampled signal, extract detail coefficients at different frequencies, and then calculate permutation entropy values to identify faults based on their characteristics. Finally, a simulation model of an MMC-HVDC grid is built using PSCAD/EMTDC to verify the accuracy of the proposed method. The results show that the proposed method performs well under different fault distances, fault resistances, and noise interference.