Optimized ensemble of regression tree-based location of evolving faults in dual-circuit line

In a dual-circuit transmission line, the location of evolving faults (EVFs) is more tiresome due to its multifaceted nature. In this paper, a novel data-mining-based scheme is proposed for location of EVFs by using an ensemble of regression trees, that is, bagged regression trees and boosted regress...

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Vydané v:Neural computing & applications Ročník 33; číslo 14; s. 8795 - 8820
Hlavní autori: Valabhoju, Ashok, Yadav, Anamika, Pazoki, Mohammad, El-Sehiemy, Ragab A.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Springer London 01.07.2021
Springer Nature B.V
Predmet:
Artificial Intelligence
Circuits
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data mining
Data Mining and Knowledge Discovery
Discrete Wavelet Transform
Errors
Evolution
Fault location
Image Processing and Computer Vision
Machine learning
Mean square errors
Optimization
Original Article
Performance assessment
Probability and Statistics in Computer Science
Regression analysis
Signal processing
Simulation
Transmission lines
Wavelet transforms
Discrete wavelet transform (DWT)
Evolving faults (EVFs)
Boosted regression tree (BSRT)
Fault location
Data-mining model
Dual-circuit transmission line (DCTL)
Bagged regression tree (BGRT)
ISSN:0941-0643, 1433-3058
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Popis
Shrnutí:In a dual-circuit transmission line, the location of evolving faults (EVFs) is more tiresome due to its multifaceted nature. In this paper, a novel data-mining-based scheme is proposed for location of EVFs by using an ensemble of regression trees, that is, bagged regression trees and boosted regression trees. This ensemble of regression tree modules is trained with optimized hyperparameters such as minimum leaf size, leaning cycles, and learning rate by using Bayesian optimization. A practical power transmission network of Chhattisgarh state is modeled/simulated in MATLAB software to employ the proposed fault location scheme. Exclusive datasets are provided by performing extensive simulation studies at a wide range of fault scenarios, thereby applying discrete wavelet transform as an explanatory signal processing technique. Further performance assessment is carried out by comparing different error metrics such as mean absolute error, mean absolute relative error, mean square error, and root mean square error. The simulation results confirm the applicability of the proposed scheme for fault location estimation, and it makes a research insight while designing relaying schemes to practical power transmission networks.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-020-05628-6