A comparison between different approaches for fault classification in transmission lines

Sudha, G.; Basavaraju, T. G.

doi:10.1049/ic:20070645

Cited by 6 publications

(5 citation statements)

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“…Transmission operators, therefore, ensure acquaintance with the inherent limitations on each line as well as different topological and operational classifications. Besides classifying transmission lines based on fault types (Donde et al , 2008b; Yusuff et al , 2011), line models (Sudha and Basavaraju, 2007), voltage and current levels (Belhocine and Marinescu, 2013), etc., the type of buses at both ends may be used to group lines in a network (Sikiru et al , 2012). Figure 4 is a modified IEEE 14-bus network consisting of two generator buses and seventeen load buses that may be used to classify transmission lines regarding the bus link structure.…”

Section: Taxonomy Of Transmission Line Switchingmentioning

confidence: 99%

Optimal capacity utilisation of transmission lines network in power flow operations: a survey

Okojie

Jimoh

Hamam

et al. 2019

JEDT

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Purpose This paper aims to survey the need for full capacity utilisation of transmission lines in power systems network operations. It proposes a review of the N-1 security criterion that does not ensure reliable dispatch of optimum power flow during outage contingency. The survey aims to enlarge the network capacity utilisation to rely on the entire transmission lines network operation. Design/methodology/approach The paper suggests transmission line switching (TLS) approach as a viable corrective mechanism for power dispatch. The TLS process is incorporated into a constraint programming language extension optimisation solver that selects the switchable line candidates as integer variables in the mixed integer programming problem. Findings The paper provides a practical awareness of reserve capacity in the lines that provide network security in outage contingency. At optimum power flow dispatch, the TLS is extended to optimal transmission line switching (OTLS) that indicates optimal capacity utilisation (OCU) of the available reserve capacity (ARC) in the network lines. Practical implications Computational efficiency influenced the extension of the OTLS to optimal transmission switching of power flow (OTSPF). The application of OTSPF helps reduce the use of flexible AC transmission systems (FACTS) and construction of new transmission lines.. Originality/value The paper surveys TLS efforts in network capacity utilisation. The suggested ARC fulfils the need for an index with which the dispatchable lines may be identified for the optimal capacity utilisation of transmission lines network.

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Section: Taxonomy Of Transmission Line Switchingmentioning

confidence: 99%

Optimal capacity utilisation of transmission lines network in power flow operations: a survey

Okojie

Jimoh

Hamam

et al. 2019

JEDT

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“…Post-fault classification can be executed offline, and its input consists of a multivariate variable duration (length) time series, which differs from online classification in that entry is a vector of fixed size. Online and post-fault systems attempt to solve problems that can be treated as problems of conventional classification and of sequence, respectively [3]. This paper focuses on the classification of sequence.…”

Section: Introductionmentioning

confidence: 99%

Transmission Line Fault Classification Using Hidden Markov Models

et al. 2019

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The maintenance of power quality in electrical power systems depends on addressing the major disturbances that may arise during generation, transmission and distribution. Many studies aim to investigate these disturbances by analyzing the behavior of the electrical signal through the classification of short circuit faults in power transmission lines as a way to assist the administration and maintenance of the electrical system. However, most fault classification methods generate a high computational cost that do not always yield satisfactory results; these methods utilize front ends in data processing before being processed by conventional classification algorithms such as Artificial Neural Network (ANN), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF) that are adopted into the Frame Based Sequence Classification (FBSC) architecture that uses the front ends Waveletenergy, Waveletconcat, RAW, Root Mean Square front ends (RMS) and ConcatFrontEnd. An alternative method for classifying faults without having to use front ends employs the UFPAFaults database and the Hidden Markov Model (HMM) algorithm that directly treats the electrical signal in the form of multivariate time series. The results indicate the HMM algorithm as a potential classifier because its comparatively low error rate of 0.03% exceeds the performance of the conventional classifiers ANN, SVM, KNN and RF as used with the FBSC architecture. When the statistical test with a significance of α = 5% is applied, only the ANN and RF classifiers present a result close to what the HMM algorithm provides. Another relevant factor is that the HMM algorithm considerably decreases the computational cost by more than 90% of processing time as compared to the conventional classifiers of the FBSC architecture, thereby validating its potential in the direct classification of faults in electric power system transmission lines.INDEX TERMS Electric power quality, electrical power systems, short circuit, classification of faults, hidden markov model.The associate editor coordinating the review of this article and approving it for publication was Chao Shen.

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“…Therefore, it is important to correctly discriminate between LIF and HIF especially for locating faults schemes. Several algorithms have been reported for LIF and HIF detections, classifications and locations as [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…Reference [13] evaluated two fault classification methods in transmission lines. It was concluded that fast fault classification with adaptive window programming feature which was provided through the WT algorithm compared with the classification performed on the basis of symmetrical components of fault currents and discrete Fourier transform.…”

Section: Introductionmentioning

confidence: 99%

Critical aspects on wavelet transforms based fault identification procedures in HV transmission line

Adly

El‐Sehiemy

Abdelaziz

et al. 2016

IET Generation, Transmission & Distribution

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A comparison between different approaches for fault classification in transmission lines

Cited by 6 publications

References 0 publications

Optimal capacity utilisation of transmission lines network in power flow operations: a survey

Optimal capacity utilisation of transmission lines network in power flow operations: a survey

Transmission Line Fault Classification Using Hidden Markov Models

Critical aspects on wavelet transforms based fault identification procedures in HV transmission line

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