Application of DWT and ANN for fault classification and location in a series compensated transmission line

Singh, Sunil K.; Vishwakarma, D. N.

doi:10.1109/icpes.2016.7584117

Cited by 4 publications

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The single input vector is composed of a discretization of the transient current spectrum in the frequency range below 10 kHz, calculated by means of a t/f analysis. In this case, since a large portion of the frequency spectrum must be analysed, DFT [32] and DWT [33] are better than the CWT because of their lower computation times. The total datasheet is composed of 45 fault transient current spectra, and the fault currents have been simulated by means of EMTP-rv environment and processed in Matlab "Deep Learning" toolbox.…”

Section: Artificial Neural Network-based Algorithmmentioning

confidence: 99%

Overcoming the Limits of the Charge Transient Fault Location Algorithm by the Artificial Neural Network

2019

View full text Add to dashboard Cite

In this paper, two algorithms for single-ended fault location are presented with reference to the unearthed sub-transmission Italian grid (with a voltage level of 60 kV). Both algorithms deal with the correlation between the ground capacitance charging frequency of sound phases and the fault position. In the former, the frequency response of a lumped parameter circuit in the Laplace domain is linked to the fault distance. With such a simplified lumped parameter circuit, the average error in locating a phase-to-ground (PtG) short circuit is 5.18% (total overhead line length equal to 60 km). Since this error is too high, another approach is presented. In this second algorithm, the frequency spectra of the transient current waveforms are used as a database for the training of an Artificial Neural Network (ANN). With this approach, the average error decreases significantly up to 0.36%. The fault location accuracies of the two proposed methods are compared in order to reveal their strengths and weaknesses. The developed procedures are applied to a single-circuit overhead line and to a double-circuit one, both modelled in the EMTP-rv environment, whereas the fault location algorithms are implemented in the MATLAB environment (for the ANN-based algorithm, the Deep Learning toolbox is used).

show abstract

Section: Artificial Neural Network-based Algorithmmentioning

confidence: 99%

Overcoming the Limits of the Charge Transient Fault Location Algorithm by the Artificial Neural Network

2019

View full text Add to dashboard Cite

show abstract

“…The combination of WT and ANN appears to be an acceptable approach for solving the HIF detection problem, as shown in [6,[14][15][16], where DWT was used as a pre-processing stage for feature extraction and then used as an input to ANN stage [11].…”

Section: Literature Reviewmentioning

confidence: 99%

Approach for identification and classification of HIFs in medium voltage distribution networks

Hubana¹,

Šarić²,

Avdaković

2018

IET Generation, Transmission & Distribution

View full text Add to dashboard Cite

“…In the last few years, the advancements in signal processing and artificial intelligence techniques are utilized in the field of power systems digital protection relaying. The less complexity in fault diagnosis approaches, the improved accuracy in the decisions and predictions made, as well as the fast responsiveness provided a superiority over the old protection relaying techniques [23].…”

Section: Introductionmentioning

confidence: 99%

Short Circuit Faults Identification and Localization in IEEE 34 Nodes Distribution Feeder Based on the Theory of Wavelets

Authafa¹

2018

IJEEE

View full text Add to dashboard Cite

In this paper a radial distribution feeder protection scheme against short circuit faults is introduced. It is based on utilizing the substation measured current signals in detecting faults and obtaining useful information about their types and locations. In order to facilitate important measurement signals features extraction such that better diagnosis of faults can be achieved, the discrete wavelet transform is exploited. The captured features are then utilized in detecting, identifying the faulted phases (fault type), and fault location. In case of a fault occurrence, the detection scheme will make a decision to trip out a circuit breaker residing at the feeder mains. This decision is made based on a criteria that is set to distinguish between the various system states in a reliable and accurate manner. After that, the fault type and location are predicted making use of the cascade forward neural networks learning and generalization capabilities. Useful information about the fault location can be obtained provided that the fault distance from source, as well as whether it resides on the main feeder or on one of the laterals can be predicted. By testing the functionality of the proposed scheme, it is found that the detection of faults is done fastly and reliably from the view point of power system protection relaying requirements. It also proves to overcome the complexities provided by the feeder structure to the accuracy of the identification process of fault types and locations. All the simulations and analysis are performed utilizing MATLAB R2016b version software package.

show abstract

Application of DWT and ANN for fault classification and location in a series compensated transmission line

Cited by 4 publications

References 16 publications

Overcoming the Limits of the Charge Transient Fault Location Algorithm by the Artificial Neural Network

Overcoming the Limits of the Charge Transient Fault Location Algorithm by the Artificial Neural Network

Approach for identification and classification of HIFs in medium voltage distribution networks

Short Circuit Faults Identification and Localization in IEEE 34 Nodes Distribution Feeder Based on the Theory of Wavelets

Contact Info

Product

Resources

About