Artificial neural network approach to fault classification for double circuit transmission lines

Khorashadi-Zadeh, H.

doi:10.1109/tdc.2004.1432494

Cited by 22 publications

(10 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Different power system faults such as LG, LL, LLG, LLL, and LLLG on a protected transmission line should be detected, classified, and located and faulty phase should be selected swiftly for performing the normal system operation. The summarized study of different ANN based fault phase selection schemes is given in Table 4 highlighting the methods used, their response time, and ANN features along with remarks [97][98][99][100][101][102][103][104][105].…”

Section: Studies On "Faultymentioning

confidence: 99%

An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination

Yadav

Dash

2014

Advances in Artificial Neural Systems

View full text Add to dashboard Cite

Contemporary power systems are associated with serious issues of faults on high voltage transmission lines. Instant isolation of fault is necessary to maintain the system stability. Protective relay utilizes current and voltage signals to detect, classify, and locate the fault in transmission line. A trip signal will be sent by the relay to a circuit breaker with the purpose of disconnecting the faulted line from the rest of the system in case of a disturbance for maintaining the stability of the remaining healthy system. This paper focuses on the studies of fault detection, fault classification, fault location, fault phase selection, and fault direction discrimination by using artificial neural networks approach. Artificial neural networks are valuable for power system applications as they can be trained with offline data. Efforts have been made in this study to incorporate and review approximately all important techniques and philosophies of transmission line protection reported in the literature till June 2014. This comprehensive and exhaustive survey will reduce the difficulty of new researchers to evaluate different ANN based techniques with a set of references of all concerned contributions.

show abstract

Section: Studies On "Faultymentioning

confidence: 99%

An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination

Yadav

Dash

2014

Advances in Artificial Neural Systems

View full text Add to dashboard Cite

show abstract

“…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]. A lot of classification algorithms are used in fault analysis, such as artificial neural networks (ANNs), support vector machines (SVMs), auto-encoders, expert systems, and so on [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Entropy SVM–Based Recognition of Transient Surges in HVDC Transmissions

Luo

Yao

Liu

et al. 2018

Entropy

View full text Add to dashboard Cite

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.

show abstract

“…Das et al Presented a comparison of the Fourier Transform method with Wavelet Transform method for detection and classification of faults on transmission lines. Reference [16] showed an application of artificial neural network approach to fault classification for double circuit transmission lines using superimposed sequence components of current signals. An algorithm of fault classification and faulted phase selection for a single circuit transmission line based on the initial current traveling wave is very recently proposed in [17].Ngaopitakkal et al [18] presented identification of simultaneous faults on transmission system using wavelet transform.…”

Section: Introductionmentioning

confidence: 99%

Wavelet Entropy and Neural Network Based Fault Detection on A Non Radial Power System Network

Jana¹

2012

IOSRJEEE

View full text Add to dashboard Cite

Artificial neural network approach to fault classification for double circuit transmission lines

Cited by 22 publications

References 7 publications

An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination

An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination

Entropy SVM–Based Recognition of Transient Surges in HVDC Transmissions

Wavelet Entropy and Neural Network Based Fault Detection on A Non Radial Power System Network

Contact Info

Product

Resources

About