An ESR Quasi-Online Identification Method for the Fractional-Order Capacitor of Forward Converters Based on Variational Mode Decomposition

Zhang, Yunning; Lian, Zheng; Fu, Wenlong; Chen, Xi

doi:10.1109/tpel.2021.3119966

Cited by 37 publications

(31 citation statements)

References 15 publications

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“…If the K value is too large, the center frequency of different IMFs may be close to each other, resulting in mode aliasing. Hence, the center frequency observation method is adopted to get K value in [ 19 ]. The center frequency of the same IMF in different times is different.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Epilepsy Detection Based on Variational Mode Decomposition and Improved Sample Entropy

Yan-dong

Chen

et al. 2022

Computational Intelligence and Neuroscience

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Epilepsy detection based on electroencephalogram (EEG) signal is of great significance to diagnosis and treatment of epilepsy. The denoised EEG signal is adopted by most traditional epilepsy detection methods. But due to nonideal denoising ability, the loss of local information and residual noise will occur, resulting in detection performance degradation. To solve the problem, the paper proposed an epilepsy detection method in noisy environment. Although epileptic signals and nonepileptic signals have some discrimination, they need to overcome the interference of noise. Hence, the improved sample entropy and phase synchronization indexes of corresponding 2 intrinsic mode functions (IMFs) caused by variational mode decomposition (VMD) are proposed as features, which can reduce the impact of noise on detection performance. The experimental results show that the accuracy, sensitivity, and specificity are 91.78%, 91.27%, and 93.61%, respectively. It can be used as an auxiliary method for clinical treatment of epilepsy.

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Section: Experiments and Resultsmentioning

confidence: 99%

Epilepsy Detection Based on Variational Mode Decomposition and Improved Sample Entropy

Yan-dong

Chen

et al. 2022

Computational Intelligence and Neuroscience

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“…Due to sensor failure, electromagnetic interference, and missing communication packets, the data quality of the actual measured signal is usually low, which is mainly manifested as data anomaly and data loss [5,6]. Most signal denoising methods, including spectrum analysis and signal reconstruction, are effective for signals with a high sampling rate and consistent sampling frequency [7][8][9]. However, data loss has a great influence on their practical application.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Prognostic Method of Francis Turbine Units Using Low-Quality Data under Variable Operating Conditions

Duan

Liu

Zhou

et al. 2022

Sensors

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The prognostic is the key to the state-based maintenance of Francis turbine units (FTUs), which consists of performance state evaluation and degradation trend prediction. In practical engineering environments, there are three significant difficulties: low data quality, complex variable operation conditions, and prediction model parameter optimization. In order to effectively solve the above three problems, an ensemble prognostic method of FTUs using low-quality data under variable operation conditions is proposed in this study. Firstly, to consider the operation condition parameters, the running data set of the FTU is constructed by the water head, active power, and vibration amplitude of the top cover. Then, to improve the robustness of the proposed model against anomaly data, the density-based spatial clustering of applications with noise (DBSCAN) is introduced to clean outliers and singularities in the raw running data set. Next, considering the randomness of the monitoring data, the healthy state model based on the Gaussian mixture model is constructed, and the negative log-likelihood probability is calculated as the performance degradation indicator (PDI). Furthermore, to predict the trend of PDIs with confidence interval and automatically optimize the prediction model on both accuracy and certainty, the multiobjective prediction model is proposed based on the non-dominated sorting genetic algorithm and Gaussian process regression. Finally, monitoring data from an actual large FTU was used for effectiveness verification. The stability and smoothness of the PDI curve are improved by 3.2 times and 1.9 times, respectively, by DBSCAN compared with 3-sigma. The root-mean-squared error, the prediction interval normalized average, the prediction interval coverage probability, the mean absolute percentage error, and the R2 score of the proposed method achieved 0.223, 0.289, 1.000, 0.641%, and 0.974, respectively. The comparison experiments demonstrate that the proposed method is more robust to low-quality data and has better accuracy, certainty, and reliability for the prognostic of the FTU under complex operating conditions.

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“…The generalized wave energy converter (WEC) is composed of the energy capture device (ECD) and the power take-off (PTO). ECD can be segmented into three types based on the energy harvesting working principle (Falcão, 2010), namely Oscillating Water Column (OWC) (Folley et al, 2006; Sheng, 2019), Oscillating Body (OB) (Valério et al, 2007; Zhang et al, 2021), and Overtopping (OT) (Vicinanza and Frigaard, 2008).…”

Section: Introductionmentioning

confidence: 99%

Enhancement of dynamic performance of wave energy converter by introducing a flow control

Geng

Chen

Zheng

et al. 2022

Transactions of the Institute of Measurement and Control

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The stabilization of power take-off (PTO) is imperative especially under circumstances of fluctuating input wave energy. In this paper, a flow control valve is introduced to optimize the transient process of the hydraulic PTO, which can contribute to a quicker adjustment and a stronger stability. Under variations of input power and load torque in transient process, an open-loop control method and a closed-loop control method are proposed as the opening law of the above valve, and the hydraulic motor speed, the pressure at the accumulator inlet and the generated power are chosen as indicators to examine the regulation performance. Then, the synergic effect of the flow control valve and the accumulator in the transient process is discussed. The effectiveness of the two presented control methods on the fluctuation suppression is respectively tested and compared in both regular wave and irregular wave situations via simulation. To validate the practical effectiveness of the proposed methods, field experiments are conducted. The results demonstrate that the open-loop control can only improve the damping ability of the hydraulic PTO in the speed raising stage, while the closed-loop control can improve the stability both in the speed raising stage and in the load increasing stage.

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An ESR Quasi-Online Identification Method for the Fractional-Order Capacitor of Forward Converters Based on Variational Mode Decomposition

Cited by 37 publications

References 15 publications

Epilepsy Detection Based on Variational Mode Decomposition and Improved Sample Entropy

Epilepsy Detection Based on Variational Mode Decomposition and Improved Sample Entropy

An Ensemble Prognostic Method of Francis Turbine Units Using Low-Quality Data under Variable Operating Conditions

Enhancement of dynamic performance of wave energy converter by introducing a flow control

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