Transmission Line Fault Classification of Multi-Dataset Using CatBoost Classifier

Ogar, Vincent Nsed; Hussain, Sajjad; Gamage, Kelum A. A.

doi:10.3390/signals3030027

Cited by 14 publications

(3 citation statements)

References 36 publications

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“…To ensure that the model does not fall into a local minimum and to avoid excessive computation, a Bayesian optimization algorithm can be used. The core components of Bayesian optimization are a statistical description proxy model and an acquisition function [19][20][21]. In the proxy function model, a flexible surrogate model was used to randomly approximate the target function, which was difficult to calculate, and different kernel functions were used to increase the nonlinear expression ability of the proxy model.…”

Section: Bo-catboost Model Based On Bayesian Optimization Algorithmmentioning

confidence: 99%

Prediction of Solid Soluble Content of Green Plum Based on Improved CatBoost

et al. 2023

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Most green plums need to be processed before consumption, and due to personal subjective factors, manual harvesting and sorting are difficult to achieve using standardized processing. Soluble solid content (SSC) of green plum was taken as the research object in this paper. Visible near-infrared (VIS-NIR) and shortwave near-infrared (SW-NIR) full-spectrum spectral information of green plums were collected, and the spectral data were corrected and pre-processed. Random forest algorithm based on induced random selection (IRS-RF) was proposed to screen four sets of characteristic wavebands. Bayesian optimization CatBoost model (BO-CatBoost) was constructed to predict SSC value of green plums. The experimental results showed that the preprocessing method of multiplicative scatter corrections (MSC) was obviously superior to Savitzky–Golay (S–G), the prediction effect of SSC based on VIS-NIR spectral waveband by partial least squares regression model (PLSR) was obviously superior to SW-NIR spectral waveband, MSC + IRS-RF was obviously superior to corresponding combination of correlation coefficient method (CCM), successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and random forest (RF). With the lowest dimensional selected feature waveband, the lowest VIS-NIR band group was only 53, and the SW-NIR band group was only 100. The model proposed in this paper based on MSC + IRS-RF + BO-CatBoost was superior to PLSR, XGBoost, and CatBoost in predicting SSC, with R2P of 0.957, which was 3.1% higher than the traditional PLSR.

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Section: Bo-catboost Model Based On Bayesian Optimization Algorithmmentioning

confidence: 99%

Prediction of Solid Soluble Content of Green Plum Based on Improved CatBoost

et al. 2023

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“…Yandex developers built Cat-Boost architecture to automatically handle crucial aspects and suggested model 2017 [21]. It adds priors to target its victim using variable statistics and combines category features to expand the dataset.…”

Section: The Use Of Cat-boost Architecture For Fault Classification A...mentioning

confidence: 99%

Improved Fault Classification and Localization in Power Transmission Networks Using VAE-Generated Synthetic Data and Machine Learning Algorithms

Khan,

Asad,

Vaimann

et al. 2023

Machines

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The reliable operation of power transmission networks depends on the timely detection and localization of faults. Fault classification and localization in electricity transmission networks can be challenging because of the complicated and dynamic nature of the system. In recent years, a variety of machine learning (ML) and deep learning algorithms (DL) have found applications in the enhancement of fault identification and classification within power transmission networks. Yet, the efficacy of these ML architectures is profoundly dependent upon the abundance and quality of the training data. This intellectual explanation introduces an innovative strategy for the classification and pinpointing of faults within power transmission networks. This is achieved through the utilization of variational autoencoders (VAEs) to generate synthetic data, which in turn is harnessed in conjunction with ML algorithms. This approach encompasses the augmentation of the available dataset by infusing it with synthetically generated instances, contributing to a more robust and proficient fault recognition and categorization system. Specifically, we train the VAE on a set of real-world power transmission data and generate synthetic fault data that capture the statistical properties of real-world data. To overcome the difficulty of fault diagnosis methodology in three-phase high voltage transmission networks, a categorical boosting (Cat-Boost) algorithm is proposed in this work. The other standard machine learning algorithms recommended for this study, including Support Vector Machine (SVM), Decision Trees (DT), Random Forest (RF), and K-Nearest Neighbors (KNN), utilizing the customized version of forward feature selection (FFS), were trained using synthetic data generated by a VAE. The results indicate exceptional performance, surpassing current state-of-the-art techniques, in the tasks of fault classification and localization. Notably, our approach achieves a remarkable 99% accuracy in fault classification and an extremely low mean absolute error (MAE) of 0.2 in fault localization. These outcomes represent a notable advancement compared to the most effective existing baseline methods.

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“…The CatBoost classifier algorithm was adopted for training transmission line faults data in the multi-dataset from an electrical power system owing to its training speed, accuracy, and ability. The trained model implements an excellent accuracy of 99.54% [15]. The OES data have been widely built-in virtual metrology (VM) system.…”

Section: Introductionmentioning

confidence: 99%

Impact of Pulse Parameters of a DC Power Generator on the Microstructural and Mechanical Properties of Sputtered AlN Film with In-Situ OES Data Analysis

et al. 2023

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In the present study, the sputtered aluminum nitride (AlN) films were processed in a reactive pulsed DC magnetron system. We applied a total of 15 different design of experiments (DOEs) on DC pulsed parameters (reverse voltage, pulse frequency, and duty cycle) with Box–Behnken experimental method and response surface method (RSM) to establish a mathematical model by experimental data for interpreting the relationship between independent and response variables. For the characterization of AlN films on the crystal quality, microstructure, thickness, and surface roughness, X-ray diffraction (XRD), atomic force microscopy (AFM), and field emission-scanning electron microscopy (FE-SEM) were utilized. AlN films have different microstructures and surface roughness under different pulse parameters. In addition, in-situ optical emission spectroscopy (OES) was employed to monitor the plasma in real-time, and its data were analyzed by principal component analysis (PCA) for dimensionality reduction and data preprocessing. Through the CatBoost modeling and analysis, we predicted results from XRD in full width at half maximum (FWHM) and SEM in grain size. This investigation identified the optimal pulse parameters for producing high-quality AlN films as a reverse voltage of 50 V, a pulse frequency of 250 kHz, and a duty cycle of 80.6061%. Additionally, a predictive CatBoost model for obtaining film FWHM and grain size was successfully trained.

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Transmission Line Fault Classification of Multi-Dataset Using CatBoost Classifier

Cited by 14 publications

References 36 publications

Prediction of Solid Soluble Content of Green Plum Based on Improved CatBoost

Prediction of Solid Soluble Content of Green Plum Based on Improved CatBoost

Improved Fault Classification and Localization in Power Transmission Networks Using VAE-Generated Synthetic Data and Machine Learning Algorithms

Impact of Pulse Parameters of a DC Power Generator on the Microstructural and Mechanical Properties of Sputtered AlN Film with In-Situ OES Data Analysis

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