Comparison of Artificial Intelligence Methods for Fault Classification of the 115-kV Hybrid Transmission System

Klomjit, Jittiphong; Ngaopitakkul, Atthapol

doi:10.3390/app10113967

Cited by 7 publications

(2 citation statements)

References 28 publications

(50 reference statements)

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“…It is possible to use signal-based techniques in the time, frequency, and time-frequency domains [6]. The classification of faults also makes use of artificial intelligence and pattern recognition techniques [7]. A variety of time-frequency transformations were applied, such as the Choi-Williams, Wigner-Ville, short-time Fourier, and undecimated wavelet transforms [8].…”

Section: Introductionmentioning

confidence: 99%

AdaBoost Ensemble Approach with Weak Classifiers for Gear Fault Diagnosis and Prognosis in DC Motors

Hussain,

Zaidi

2024

Applied Sciences

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This study introduces a novel predictive methodology for diagnosing and predicting gear problems in DC motors. Leveraging AdaBoost with weak classifiers and regressors, the diagnostic aspect categorizes the machine’s current operational state by analyzing time–frequency features extracted from motor current signals. AdaBoost classifiers are employed as weak learners to effectively identify fault severity conditions. Meanwhile, the prognostic aspect utilizes AdaBoost regressors, also acting as weak learners trained on the same features, to predict the machine’s future state and estimate its remaining useful life. A key contribution of this approach is its ability to address the challenge of limited historical data for electrical equipment by optimizing AdaBoost parameters with minimal data. Experimental validation is conducted using a dedicated setup to collect comprehensive data. Through illustrative examples using experimental data, the efficacy of this method in identifying malfunctions and precisely forecasting the remaining lifespan of DC motors is demonstrated.

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Section: Introductionmentioning

confidence: 99%

AdaBoost Ensemble Approach with Weak Classifiers for Gear Fault Diagnosis and Prognosis in DC Motors

Hussain,

Zaidi

2024

Applied Sciences

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“…Recently, the artificial neural network (ANN) as a prevalent modeling method has been used for identification of the complicated non-linear relationship of inputs and output (e.g., see, [2][3][4][5][6][7][8]). The relationship between the hydrological phenomena is a complicated issue and hot topic in hydrological studies, due to spatial and temporal changes of factors that influence the process.…”

Section: Introductionmentioning

confidence: 99%

Prediction Interval Estimation Methods for Artificial Neural Network (ANN)-Based Modeling of the Hydro-Climatic Processes, a Review

2021

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Despite the wide applications of artificial neural networks (ANNs) in modeling hydro-climatic processes, quantification of the ANNs’ performance is a significant matter. Sustainable management of water resources requires information about the amount of uncertainty involved in the modeling results, which is a guide for proper decision making. Therefore, in recent years, uncertainty analysis of ANN modeling has attracted noticeable attention. Prediction intervals (PIs) are one of the prevalent tools for uncertainty quantification. This review paper has focused on the different techniques of PI development in the field of hydrology and climatology modeling. The implementation of each method was discussed, and their pros and cons were investigated. In addition, some suggestions are provided for future studies. This review paper was prepared via PRISMA (preferred reporting items for systematic reviews and meta-analyses) methodology.

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Fault detection and classification in overhead transmission lines through comprehensive feature extraction using temporal convolution neural network

Tunio,

Hashmani,

Khokhar

et al. 2024

Engineering Reports

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Faults in transmission lines cause instability of power system and result in degrading end users sophisticated equipment. Therefore, in case of fault and for the quick restoration of problematic phases, reliable and accurate fault detection and classification techniques are required to categorize the faults in a minimum time. In this work, 500 kV transmission line (Jamshoro‐New Karachi), Sindh, Pakistan has been modeled in MATLAB. The discrete wavelet transform (DWT) has been used to extract features from the transient current signal for different faults in 500 kV transmission line under various parameters such as fault location, fault inception angle, ground resistance and fault resistance and time series data has been obtained for fault classification. Moreover, the temporal convolutional neural network (TCN) is used for fault classification in 500 kV transmission network due to its robust framework. From simulation results, it is found that faults in 500 kV transmission line are classified with 99.9% accuracy. Furthermore, the simulation results of the TCN model compared to bidirectional long short‐term memory (BiLSTM) and Gated Recurrent Unit (GRU) and it has been found that TCN model is capable of classifying faults in 500 kV transmission line with high accuracy due to its ability to handle long receptive field size, less memory requirement and parallel processing due to dilated causal convolutions. Through this work, the meantime to repair of 500 kV transmission line can be reduced.

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Comparison of Artificial Intelligence Methods for Fault Classification of the 115-kV Hybrid Transmission System

Cited by 7 publications

References 28 publications

AdaBoost Ensemble Approach with Weak Classifiers for Gear Fault Diagnosis and Prognosis in DC Motors

AdaBoost Ensemble Approach with Weak Classifiers for Gear Fault Diagnosis and Prognosis in DC Motors

Prediction Interval Estimation Methods for Artificial Neural Network (ANN)-Based Modeling of the Hydro-Climatic Processes, a Review

Fault detection and classification in overhead transmission lines through comprehensive feature extraction using temporal convolution neural network

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