New algorithm for detection and fault classification on parallel transmission line using DWT and BPNN based on Clarke’s transformation

Zin, Abdullah Asuhaimi Mohd; Saini, Makmur; Mustafa, Mohd Wazir; Sultan, Ahmad Rizal; Rahimuddin, Rahimuddin

doi:10.1016/j.neucom.2015.05.026

Cited by 63 publications

(20 citation statements)

References 22 publications

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“…Modal transformations such as Clarke transformation (CT) was used in [39][40][41] to decouple three-phase quantities represented by a, b and c and transform them into components represented by α, β and 0, on the basis of which fault types were characterised by describing the relationships between phase quantities and modal components [41], and fault detection and location indices were calculated [39,40]. Authors in [42,43] adopted a modification of CT called Clarke-Concordia transformation.…”

Section: Modal Transformationmentioning

confidence: 99%

“…In [10], authors employed separate FNN modules to identify different types of faults, so that each network had less patterns to learn. Researchers implemented Clarke's transform and DWT in [41] to produce appropriate input for the FNN. For the dataset used in [41], the 12‐24‐48‐4 structure outperforms the 12‐6‐12‐4 and the 12‐12‐24‐4 structures, for such a structure has better learning ability.…”

Section: Fault‐type Classificationmentioning

confidence: 99%

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Fault detection, classification and location for transmission lines and distribution systems: a review on the methods

2016

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A comprehensive review on the methods used for fault detection, classification and location in transmission lines and distribution systems is presented in this study. Though the three topics are highly correlated, the authors try to discuss them separately, so that one may have a more logical and comprehensive understanding of the concepts without getting confused. Great significance is also attached to the feature extraction process, without which the majority of the methods may not be implemented properly. Fault detection techniques are discussed on the basis of feature extraction. After the overall concepts and general ideas are presented, representative works as well as new progress in the techniques are covered and discussed in detail. One may find the content of this study helpful as a detailed literature review or a practical technical guidance.

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Section: Modal Transformationmentioning

confidence: 99%

Section: Fault‐type Classificationmentioning

confidence: 99%

Fault detection, classification and location for transmission lines and distribution systems: a review on the methods

2016

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“…The technique presented in [18][19][20] were dependent on phasor measurements. Other methods used Traveling Wave (TW), DWT and BPNN based on Clarke's Transformation, Artificial Neural Networks (ANNs) and Fuzzy for analyzing, detecting, classifying, discriminating direction and locating different faults types [21][22][23]. Some drawbacks for the conventional digital protection relays and systems are listed as follows:…”

Section: Introductionmentioning

confidence: 99%

“…3. In some conventional approaches, the manner for determining the pre-set threshold values is complicated values [3,5,8,11,12,[21][22][23]. 4.…”

Section: Introductionmentioning

confidence: 99%

Simple algorithm for fault detection, classification and direction discrimination in power systems based on digital current signals processing

Mahmoud

2022

The Journal of Engineering

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A power system suffers from unexpected various faults, which needs smart devices for accurate estimates of fault detection, classification and direction discrimination to provide inspection, maintenance, and repair of the actual faults. Signal processing‐based protection technique can be used to detect the disturbances of the measured signals. This paper presents a digital protection scheme, based on three indices derived from the three‐phase input and output current signals of the protected equipment, to detect current disturbances, classify faults, discriminate fault directions, select faulty phase(s), define current unbalances, and identify CT saturation extent. Moreover, it develops a new closed‐tripping characteristic with a quadrilateral form for differentiating the fault location (internal/external) with respect to the element protection zone. A power system simulation is carried out using ATP platform, and the technique performance is validated via MATLAB software. Extensive simulation case studies show that the proposed algorithm provides a fast relay response and high accuracy under various operating and fault conditions. It is able to perform successfully the function of fault diagnosis within one periodic cycle. Besides, it responds very well with regard to dependability, security, stability and sensitivity. In addition to that it has the ability to recognize the saturated and unsaturated secondary current signals, measured using the current transformers, in the cases of internal and external faults.

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“…Normally, the source types of PQ disturbance in the commercial building can be identified by using the classification technique [8]. Nowadays, a lot of classifier tools are combined with the signal processing technique to classify the PQ disturbances in the commercial building [9][10][11]. The signal processing is utilized to extract the desired information which is applied as an input for the classifier tools [8].…”

Section: Introductionmentioning

confidence: 99%

Classification of The NTEV Problems on The Commercial Building

Yusoh¹,

Asman²,

Yasin³

et al. 2018

IJEECS

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Neutral to Earth Voltage (NTEV) is one of power quality (PQ) problems in the commercial building that need to be resolved. The classification of the NTEV problems is a method to identify the source types of disturbance in alleviating the problems. This paper presents the classification of NTEV source in the commercial building which is known as the harmonic, loose termination, and lightning. The Euclidean, City block, and Chebyshev variables for K-Nearest Neighbor (K-NN) classifying are being utilized in order to identify the best performance for classifying the NTEV problems. Then, S-Transform (ST) is applied as a pre-processing signal to extract the desired features of NTEV problem for classifier input. Furthermore, the performance of K-NN variables is validated by using the confusion matrix and linear regression. The classification results show that all the K-NN variables capable to identify the NTEV problems. While the K-NN results show that the Euclidean and City block variables are well performed rather than the Chebyshev variable. However, the Chebyshev variable is still reliable as the confusion matrix shows minor misclassification. Then, the linear regression outperformed the percentage close to a perfect value which is hundred percent.

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New algorithm for detection and fault classification on parallel transmission line using DWT and BPNN based on Clarke’s transformation

Cited by 63 publications

References 22 publications

Fault detection, classification and location for transmission lines and distribution systems: a review on the methods

Fault detection, classification and location for transmission lines and distribution systems: a review on the methods

Simple algorithm for fault detection, classification and direction discrimination in power systems based on digital current signals processing

Classification of The NTEV Problems on The Commercial Building

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