Two novel proposed discrete wavelet transform and filter based approaches for short-circuit faults detection in power transmission lines

Fathabadi, Hassan

doi:10.1016/j.asoc.2015.07.039

Cited by 24 publications

(8 citation statements)

References 32 publications

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“…The collaborative methods used have their respective advantages. Some of the methods used are the traveling wave on the transmission line, such as fuzzy logic [29]- [31], artificial neural network (ANN) [8], [32], support vector machines (SVM) [33], [34], WT, and ANN [13], [35], WT and fuzzy logic [26], [36] and combination of ANN and fuzzy logic. [13], [37], [38].…”

Section: Methodsmentioning

confidence: 99%

“…Discrete wavelet transforms can determine the location of errors; this paper will present several methodologies that are suitable for classifying errors of different kinds that occur in transmission lines, particularly in the insulator breakdown voltages [24]. This wavelet transform theory is able to process the mathematical equations using the discrete wavelet transform (DWT), which can extract the transient features from three-phase currents, and the features obtained would be used to detect short circuit faults [25], [26].…”

Section: Introductionmentioning

confidence: 99%

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Disturbance Modeling on Transmission Lines Using Wavelet Transform Method: A Review

Amalia

Hasanah²,

Warmi³

et al. 2023

AJEEET

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This paper introduces a number of methods for identifying disturbances on transmission lines and provides algorithms for classifying various disturbance types. working with the discrete wavelets transform (DWT) and various embedded systems techniques using a simulator to carry out digital simulations of system trials using the controlled technique, the nature of disturbance in distinct sub-systems is determined. The analysis of transmission lines using the wavelet transform is the main topic of this study, along with techniques for transmission line protection. These factors include channel capacity, channel length, simulation software, mother wavelet type, parameters, sampling frequency, and type of disturbance analysis.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Disturbance Modeling on Transmission Lines Using Wavelet Transform Method: A Review

Amalia

Hasanah²,

Warmi³

et al. 2023

AJEEET

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show abstract

“…19 By integrating the theory of fuzzy sets, pairwise probabilistic multi-label classification, and decision-by-threshold, a new framework of simultaneous FD, called fuzzy and probabilistic simultaneous fault diagnosis (FPSD), was proposed by Vong et al 20 Xia et al 21 put forward a multi-objective unsupervised feature selection algorithm (MOUFSA) that is verified by nine UCI datasets and five fault recognition datasets. Fathabadi 22 proposed a soft computing method via discrete wavelet transform and a hardware via twostage finite impulse response to detect short-circuit faults in power transmission lines. Qin et al 23 proposed a method to recognize power cable fault types via an annealed chaotic competitive learning network.…”

Section: Preliminariesmentioning

confidence: 99%

Improved probabilistic neural networks with self-adaptive strategies for transformer fault diagnosis problem

Wang

2016

Advances in Mechanical Engineering

110

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Probabilistic neural network has successfully solved all kinds of engineering problems in various fields since it is proposed. In probabilistic neural network, Spread has great influence on its performance, and probabilistic neural network will generate bad prediction results if it is improperly selected. It is difficult to select the optimal manually. In this article, a variant of probabilistic neural network with self-adaptive strategy, called self-adaptive probabilistic neural network, is proposed. In self-adaptive probabilistic neural network, Spread can be self-adaptively adjusted and selected and then the best selected Spread is used to guide the self-adaptive probabilistic neural network train and test. In addition, two simplified strategies are incorporated into the proposed self-adaptive probabilistic neural network with the aim of further improving its performance and then two versions of simplified self-adaptive probabilistic neural network (simplified selfadaptive probabilistic neural networks 1 and 2) are proposed. The variants of self-adaptive probabilistic neural networks are further applied to solve the transformer fault diagnosis problem. By comparing them with basic probabilistic neural network, and the traditional back propagation, extreme learning machine, general regression neural network, and selfadaptive extreme learning machine, the results have experimentally proven that self-adaptive probabilistic neural networks have a more accurate prediction and better generalization performance when addressing the transformer fault diagnosis problem.

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“…It is used for analyzing signals with a complex frequency-time structure [10]. WT Refs [11][12][13] and has been combined with other techniques in Refs [14][15][16]. In addition, WT entropy has been used in faults analysis in transmission line, as in Refs [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Linear regression index‐based method for fault detection and classification in power transmission line

Musa

et al. 2018

IEEJ Transactions Elec Engng

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In this paper, a new algorithm is proposed for faults detection and classification in the power transmission line. The key of the proposed algorithm depends on the computing of the linear regression coefficient indices (LRCIs) of the three‐phase current signals. The proposed algorithm has constructed a rule as follows: when the system is running under the health condition, the LRCIs will be equal to zero; when the system is subjected to the fault condition, the LRCIs of faulted phases will be greater than zero. Different faults circumstances, such as different inception time, different fault resistances, and different locations, have been verified. Additional scenarios such as far‐end fault with high resistance, fault occurred near the terminal, fault considering variable loading angle, and fault at the presence of noise are also discussed. For each possible scenario of faults, the proposed algorithm required only the three‐phase current measurement of the local measurement. The proposed algorithm has demonstrated a reasonable time response, where the fault condition could be detected within a few milliseconds after the fault inception. Therefore, the proposed algorithm is quite suitable for faults detection and classification in power transmission lines. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Two novel proposed discrete wavelet transform and filter based approaches for short-circuit faults detection in power transmission lines

Cited by 24 publications

References 32 publications

Disturbance Modeling on Transmission Lines Using Wavelet Transform Method: A Review

Disturbance Modeling on Transmission Lines Using Wavelet Transform Method: A Review

Improved probabilistic neural networks with self-adaptive strategies for transformer fault diagnosis problem

Linear regression index‐based method for fault detection and classification in power transmission line

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