New fault zone identification scheme for busbar using support vector machine

Chothani, Nilesh; Bhalja, Bhavesh R.; Parikh, Urmil

doi:10.1049/iet-gtd.2010.0462

Cited by 33 publications

(18 citation statements)

References 14 publications

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“…Referring to many literatures, it has been observed by the authors that Radial Basis Function (RBF) kernel is the first choice for training as it nonlinearly maps samples into higher dimensional space most effectively among all types of kernels used [24].…”

Section: Svm Based Proposed Methodsmentioning

confidence: 99%

Performance Metrics of Grid Connected Solar PV Power Plant � A Practical Case Study

Kumar¹,

Raju²,

Kar³

et al. 2017

JGE

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During out of zone fault, Current Transformer (CT) saturation leads maloperation in unit type protective schemes. Detection and classification of saturation condition of CT is still a challenging issue. Thus, it is most important to correctly categorize CT saturation condition to increase reliability and stability of protective schemes. The proposed scheme utilizes transmission line CT secondary post fault current signals (sliding window) as an input to SVM. In order to achieve the most optimized classifier, Gaussian Radial Basis Function (RBF) has been used for training of SVM. Feasibility of the proposed scheme has been tested by modelling a part of 220 kV power systems in PSCAD/EMTDC software package. The algorithm is executed in MATLAB software. More than 720 unsaturated and 3600 saturated cases with varying burden resistance, remnant flux, DC component of current, noise penetration to current signal and fault inception angle have been generated and used for validation of the proposed scheme. The proposed scheme effectively discriminates between CT saturated and unsaturated conditions with very high classification accuracy more than 99% for different parameter variations.

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Section: Svm Based Proposed Methodsmentioning

confidence: 99%

Performance Metrics of Grid Connected Solar PV Power Plant � A Practical Case Study

Kumar¹,

Raju²,

Kar³

et al. 2017

JGE

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show abstract

“…While the effect of location can be improved when combining with other techniques like wavelet transformer, ANN, genetic algorithm, etc. Other techniques such as Zero-Mode transient method, Injection method, Bayesian network method, were proposed in [18][19][20][21] as well.…”

Section: Introductionmentioning

confidence: 99%

Fault location in loop distribution network using SVM technology

Deng

Yuan

Xiao

et al. 2015

International Journal of Electrical Power & Energy Systems

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“…In addition, [12] presented a novel wavelet-transform-based directional algorithm for busbar protection, where only the high-frequency details at the first level were employed. Recently, a new SVM based fault zone identification scheme for busbar was presented [13]. This scheme could identify faults accurately, yet the methodology needed one cycle post-fault current signals of all the lines.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, [14] combined several protection principles and measuring techniques into a scheme with minimum CT performance requirements, which enhanced the performance of busbar protection to some extent. However, due to the deficiency of neglecting the impacts of the transition resistance and fault initial angle [6][7][8][9][10][11][12][13][14], the reliability of traditional transient protection methods cannot be guaranteed whilst their performance are not stable enough, thus limiting their wide utilization in reality. Overall, all these methods need information of all lines connected to the busbar, which may lead to the complexity of such methods and the poor reliability of the protection schemes.…”

Section: Introductionmentioning

confidence: 99%