Two-terminal traveling wave fault location based on successive variational mode decomposition and frequency-dependent propagation velocity

Zeng, Ruochen; Zhang, Luliang

doi:10.1016/j.epsr.2022.108768

Cited by 10 publications

(3 citation statements)

References 28 publications

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“…Noise refers to undesired electrical signals, commonly characterized by a broad spectrum typically below 200 kHz, and they are superimposed on the power system voltage or current in phase conductors. The typical noise magnitude is less [3,6,18]. The simulation of the power system was carried out using the Alternative Transient Program (ATP) software, using the J. Marti's TW method [19].…”

Section: Considerations Adopted In the Analysismentioning

confidence: 99%

Traveling Wave Fault Location for AC Transmission Lines: An Approach Based on the Application of EMD and Teager Energy Operator

Oliveira,

Moreira,

Picanço

et al. 2024

Preprint

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This paper proposes an approach for the single-ended and the double-ended traveling wave based fault location algorithm using the empirical mode decomposition associated with the Teager energy operator to extract characteristic data from the faulted voltage signals of an overhead transmission line. The simulation of the power system uses the JMarti line model, with an ideally transposed transmission line, and it was carried out using the Alternative Transients Program (ATP) software. After, the MATLAB® software was used for extracting the traveling wave arrival times and to perform the single-ended and the double-ended fault location algorithms for all simulated scenarios in ATP. The numerical and graphical results prove that the proposed methodology with the Teager Energy operator and the double-ended analysis can better extract the characteristic data of the voltage signals and estimate the fault location with good accuracy, with percentage error of 0.034% for the best results, depending only on the fault type and the sampling rate adopted.

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Section: Considerations Adopted In the Analysismentioning

confidence: 99%

Traveling Wave Fault Location for AC Transmission Lines: An Approach Based on the Application of EMD and Teager Energy Operator

Oliveira,

Moreira,

Picanço

et al. 2024

Preprint

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“…Successive variational mode decomposition (SVMD) does not require the exact number of mode components to be set as in VMD when processing a signal, and it uses a continuous approach to identify and extract all components. It helps to increase the speed of convergence, neither extracting unwanted modes nor reducing computation time, and has been used successfully in wind speed prediction and other fields [30,31]. Therefore, SVMD is used to eliminate battery capacity regeneration in this paper.…”

Section: Introductionmentioning

confidence: 99%

A novel lithium-ion battery capacity prediction framework based on SVMD-AO-DELM

Ding¹,

Chen

2023

Preprint

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Accurate and efficient lithium-ion battery capacity prediction plays an important role in improving performance and ensuring safe operation. In this study, a novel lithium-ion battery capacity prediction model combining successive variational mode decomposition (SVMD) and aquila optimized deep extreme learning machine (AO-DELM) is proposed. Firstly, SVMD is used to divide capacity signal and it improves short-term trend prediction, especially for capacity growth that occurs during the degradation process. Secondly, the DELM network outperforms other networks in efficiently extracting time-dependent features, and it is more accurate than other standard ELM-based methods. The AO algorithm is used to optimize the parameters of the DELM training process for the problem of sensitivity to initial weights. Finally, experiments are conducted to validate the predictive performance of the proposed model based on NASA and CALCE lithium-ion batteries discharge capacity decay sequences. The MAE (0.0066Ah, 0.0044Ah), RMSE (0.0113Ah, 0.0078Ah), MAPE (0.44%, 0.82%) are effectively reduced and the R2 (98.94%, 99.87%) are better than the prediction performance of other hybrid models.

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“…the penalty factor α [15,16]. Zeng et al [17] utilized SVMD for signal decomposition, selecting the IMF components of the highest central frequency to match the wave frequency and propagation speed, thus achieving fault distance measurement for traveling wave faults. Guo et al [18] proposed a line search algorithm with an energy concentration and position accuracy (EP) index to determine the penalty factor of SVMD, and diagnosed faults through analyzing the IMF components with a higher EP index.…”

Section: Introductionmentioning

confidence: 99%

A feature extraction method for rotating machinery fault diagnosis based on a target detection index and successive variational mode decomposition

Cao,

Zhang,

et al. 2023

Meas. Sci. Technol.

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Rotating machinery feature extraction is critical for the subsequent fault diagnosis and ensuring safe and stable operation. However, the commonly used methods often have limitations, such as the extracted features being redundant or insufficient and the method parameters generally being set based on experience. Although some feature extraction methods apply optimization algorithms for parameter setting, their objective functions are often too simple to obtain favorable fault diagnosis results. To address these issues, a target detection index (TDI) is constructed which can consider the discrimination among features more comprehensively and make the extracted features more sensitive. Furthermore, a fault feature extraction method is proposed based on TDI and successive variational mode decomposition (SVMD). Taking TDI as the objective function, genetic algorithm (GA) is used for the feature selection process and SVMD parameter optimization. The obtained features are then fused and visualized using t-distributed stochastic neighbor embedding and are classified using support vector machines. The Case Western Reserve University data and hydropower generating unit data are employed for method verification. When compared with other decomposition algorithms, the proposed method exhibits great ability at extracting highly sensitive features.

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Two-terminal traveling wave fault location based on successive variational mode decomposition and frequency-dependent propagation velocity

Cited by 10 publications

References 28 publications

Traveling Wave Fault Location for AC Transmission Lines: An Approach Based on the Application of EMD and Teager Energy Operator

Traveling Wave Fault Location for AC Transmission Lines: An Approach Based on the Application of EMD and Teager Energy Operator

A novel lithium-ion battery capacity prediction framework based on SVMD-AO-DELM

A feature extraction method for rotating machinery fault diagnosis based on a target detection index and successive variational mode decomposition

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