Dominant noise-aided EMD (DEMD): Extending empirical mode decomposition for noise reduction by incorporating dominant noise and deep classification

Shamaee, Zeinab; Mivehchy, Mohsen

doi:10.1016/j.bspc.2022.104218

Cited by 6 publications

(1 citation statement)

References 54 publications

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“…The LSTM is an advanced RNN that can store and delete temporal state data automatically. This capability resolves the issue of vanishing the RNN gradient when facing long‐time sequences and boosts the feature extraction of complicated time series [21]. A usual LSTM includes an input gate, an output gate, and a forget gate.…”

Section: Proposed Approachmentioning

confidence: 99%

A power transformer differential protection method based on variational mode decomposition and CNN‐BiLSTM techniques

Afsharisefat,

Jannati,

Shams

2024

IET Generation Trans & Dist

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Power transformers play a critical role in the performance of power systems. This equipment is costly due to significant copper and iron prices and manufacturing costs. Therefore, maintenance and protection of such equipment is essential. Despite its robust performance, maloperation of differential protection (DP) in transformers may cause operational challenges to power system operators. The differential relay may operate incorrectly after the transformer energization leads to an inrush current (IC) and the relay identifies the event as an internal fault, and consequently issues the trip command. The other case of maloperation includes, but not limited to, a moment when the current transformer saturates due to an external fault. In this paper, a novel approach for DP is proposed, that is based on signal processing methods. In this paper, variational mode decomposition (VMD) and the deep neural network are implemented by using the convolutional neural network (CNN) and bi‐directional long short‐term memory (BiLSTM) models. The VMD decomposes differential current signal (DCS) to intrinsic mode functions with corresponding narrow‐band property frequency spectrums, which provides more detailed information about signal characteristics in different frequency bands. At the next stage, an effective feature for the BiLSTM is extracted by the CNN with the convolutional layers to classify events and proper discrimination. Extensive simulations on a 500 MVA transformer in MATLAB demonstrate the effectiveness of the proposed protection approach to differentiate ICs from internal and external faults with 99.8% accuracy in less than 1/8th of a power cycle.

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Section: Proposed Approachmentioning

confidence: 99%

A power transformer differential protection method based on variational mode decomposition and CNN‐BiLSTM techniques

Afsharisefat,

Jannati,

Shams

2024

IET Generation Trans & Dist

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New formulation for predicting total dissolved gas supersaturation in dam reservoir: application of hybrid artificial intelligence models based on multiple signal decomposition

Heddam,

Al-Areeq,

Tan

et al. 2024

Artif Intell Rev

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Total dissolved gas (TDG) concentration plays an important role in the control of the aquatic life. Elevated TDG can cause gas-bubble trauma in fish (GBT). Therefore, controlling TDG fluctuation has become of great importance for different disciplines of surface water environmental engineering.. Nowadays, direct estimation of TDG is expensive and time-consuming. Hence, this work proposes a new modelling framework for predicting TDG based on the integration of machine learning (ML) models and multiresolution signal decomposition. The proposed ML models were trained and validated using hourly data obtained from four stations at the United States Geological Survey. The dataset are composed from: (i) water temperature (Tw), (ii) barometric pressure (BP), and (iii) discharge (Q), which were used as the input variables for TDG prediction. The modelling strategy is conducted based on two different steps. First, six singles ML model namely: (i) multilayer perceptron neural network, (ii) Gaussian process regression, (iii) random forest regression, (iv) random vector functional link, (v) adaptive boosting, and (vi) Bootstrap aggregating (Bagging), were developed for predicting TDG using Tw, BP, and Q, and their performances were compared. Second, a new framework was introduced based on the combination of empirical mode decomposition (EMD), the variational mode decomposition (VMD), and the empirical wavelet transform (EWT) preprocessing signal decomposition algorithms with ML models for building new hybrid ML models. Hence, the Tw, BP, and Q signals were decomposed to extract the intrinsic mode functions (IMFs) by using the EMD and VMD methods and the multiresolution analysis (MRA) components by using the EWT method. Then after, the IMFs and MRA components were selected and regraded as new input variables for the ML models and used as an integral part thereof. The single and hybrid prediction models were compared using several statistical metrics namely, root mean square error, mean absolute error, coefficient of determination (R2), and Nash–Sutcliffe efficiency (NSE). The single and hybrid models were trained several times with high number of repetitions, depending on the kind of modeling process. The obtained results using single models gave good agreement between the predicted TDG and the situ measured dataset. Overall, the Bagging model performed better than the other five models with R2 and NSE values of 0.906 and 0.902, respectively. However, the extracted IMFs and MRA components using the EMD, VMD and the EWT have contributed to an improvement of the hybrid models’ performances, for which the R2 and NSE were significantly increased reaching the values of 0.996 and 0.995. Experimental results showed the superiority of hybrid models and more importantly the importance of signal decomposition in improving the predictive accuracy of TDG. Graphical abstract

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Enhanced ECG Signal features transformation to RGB matrix imaging for advanced deep learning classification of myocardial infarction and cardiac arrhythmia

Khatar,

Bentaleb

2024

Multimed Tools Appl

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Dominant noise-aided EMD (DEMD): Extending empirical mode decomposition for noise reduction by incorporating dominant noise and deep classification

Cited by 6 publications

References 54 publications

A power transformer differential protection method based on variational mode decomposition and CNN‐BiLSTM techniques

A power transformer differential protection method based on variational mode decomposition and CNN‐BiLSTM techniques

New formulation for predicting total dissolved gas supersaturation in dam reservoir: application of hybrid artificial intelligence models based on multiple signal decomposition

Enhanced ECG Signal features transformation to RGB matrix imaging for advanced deep learning classification of myocardial infarction and cardiac arrhythmia

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