Abstract
Protection based on transient information is the primary protection of high voltage direct current (HVDC) transmission systems. As a major part of protection function, accurate identification of transient surges is quite crucial to ensure the performance and accuracy of protection algorithms. Recognition of transient surges in an HVDC system faces two challenges: signal distortion and small number of samples. Entropy, which is stable in representing frequency distribution features, and support vector machine (SVM), which is good at dealing with samples with limited numbers, are adopted and combined in this paper to solve the transient recognition problems. Three commonly detected transient surges—single-pole-to-ground fault (GF), lightning fault (LF), and lightning disturbance (LD)—are simulated in various scenarios and recognized with the proposed method. The proposed method is proved to be effective in both feature extraction and type classification and shows great potential in protection applications.
Highlights
High voltage direct current (HVDC) transmission plays an important role in power transmissions due to its advantages such as large transmission capacity and good performance in power flow control [1,2]
Traveling wave–based protection or voltage derivate based protection are used as the primary protection, and under-voltage protection or current-differential protection are adopted as the backup protections in the HVDC systems [3,4]
Combining the advantages of entropy and support vector machine (SVM), this paper proposed a transient surge recognition method to improve the reliability of HVDC protections
Summary
High voltage direct current (HVDC) transmission plays an important role in power transmissions due to its advantages such as large transmission capacity and good performance in power flow control [1,2]. Machine learning algorithms can effectively solve the problems of uncertain correspondence [18] They show a number of advantages in pattern recognition, classification, and generalization and play an important role in the field of power system fault diagnosis [19,20,21]. Combining the advantages of entropy and SVMs, this paper proposed a transient surge recognition method to improve the reliability of HVDC protections. Based on the analysis of transient waveforms of pole-to-ground faults (GFs), lightning faults (LFs), and lightning disturbances (LDs) in direct current (DC) transmission lines, the features of different transient signals are represented by frequency spectrum entropy (FSE) vectors. The simulation results and the comparisons prove the potential of FES-SVM in protection applications
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