Power quality disturbance classification under noisy conditions using adaptive wavelet threshold and DBN-ELM hybrid model

Gao, Yunpeng; Li, Yunfeng; Yanqing, Zhu; Wu, Cong; Gu, Dexi

doi:10.1016/j.epsr.2021.107682

Cited by 36 publications

(9 citation statements)

References 37 publications

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“…Fiore et al [97] proposed an RBM-based network anomaly detection method, which could also be applied to PQ event classification. They achieved an overall accuracy of 99.3% for network anomaly detection, but the accuracy for PQ events classification was not reported.…”

Section: Restricted Boltzmann Machine (Rbm)mentioning

confidence: 99%

“…They achieved an overall accuracy of 99.3% for network anomaly detection, but the accuracy for PQ events classification was not reported. Gao et al [97] proposed an adaptive wavelet threshold and DBN-ELM hybrid model for PQ disturbance classification under noisy conditions. They achieved an overall classification accuracy of 98%.…”

Section: Restricted Boltzmann Machine (Rbm)mentioning

confidence: 99%

See 1 more Smart Citation

A Comprehensive Review of Deep-Learning Applications to Power Quality Analysis

et al. 2023

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Power quality (PQ) monitoring and detection has emerged as an essential requirement due to the proliferation of sensitive power electronic interfacing devices, electric vehicle charging stations, energy storage devices, and distributed generation energy sources in the recent smart grid and microgrid scenarios. Even though, to date, the traditional approaches play a vital role in providing a solution to the above issue, the limitations, such as the requirement of significant human effort and not being scalable for large-scale power systems, force us to think of alternative approaches. Looking at a better perspective, deep-learning (DL) has gained the main attraction for various researchers due to its inherent capability to classify the data by extracting dominating and prominent features. This manuscript attempts to provide a comprehensive review of PQ detection and classification based on DL approaches to explore its potential, efficiency, and consistency to produce results accurately. In addition, this state-of-the-art review offers an overview of the novel concepts and the step-by-step method for detecting and classifying PQ events. This review has been presented categorically with DL approaches, such as convolutional neural networks (CNNs), autoencoders, and recurrent neural networks (RNNs), to analyze PQ data. This paper also highlights the challenges and limitations of using DL for PQ analysis, and identifies potential areas for future research. This review concludes that DL algorithms have shown promising PQ detection and classification results, and could replace traditional methods.

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Section: Restricted Boltzmann Machine (Rbm)mentioning

confidence: 99%

Section: Restricted Boltzmann Machine (Rbm)mentioning

confidence: 99%

A Comprehensive Review of Deep-Learning Applications to Power Quality Analysis

et al. 2023

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“…CNN can achieve very high performances, but too many parameters can result in heavy computation. To address the challenges of noise interference and artificial feature extraction in PQD's classification, a novel method that combines adaptive wavelet threshold denoising with a deep belief network fusion ELM has been proposed in Gao et al 26 Garcia et al 27 investigated the effectiveness of various deep architectures (CNN, LSTM, and CNN–LSTM) for PQDs detection and classification.…”

Section: Related Workmentioning

confidence: 99%

Classification of multiple power quality disturbances based on continuous wavelet transform and lightweight convolutional neural network

Zhou

et al. 2023

Energy Science & Engineering

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Aiming at the problems of noise interference and too many network parameters for power quality disturbances' (PQDs') classification based on deep learning, the lightweight convolutional neural network combining maximum likelihood Kalman filter and continuous wavelet transform is proposed. In this proposed method, the disturbed PQD signals are denoised by maximum likelihood Kalman filter, and then the denoised PQDs are converted to time‐frequency diagrams, which can provide rich time and frequency domain information, and finally the lightweight convolution neural network is used for automatically extracting and classifying multiple PQDs. To verify the effectiveness and superiority of the proposed method, a variety of PQDs were tested under different noise levels, the experiment results indicate that the average classification accuracy can reach more than 99% even in the case of 10 dB noise. Compared with the existing classification methods, the accuracy and noise immunity ability are improved. Additionally, the proposed method has decided advantages, as evidenced by its low parameter count of 0.73M and short average test time with only 0.7 ms.

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“…Many scholars have deep learning technology applied in control valve pattern recognition, such as deep convolutional neural network (DCNN), deep belief network (DBN), etc [27,28]. The DBN exhibits excellent learning and feature extraction abilities [29]. The Extreme Learning Machine (ELM) has superior generalization capability [30].…”

Section: Introductionmentioning

confidence: 99%

“…The deep belief network-extreme learning machine (DBN-ELM) model combines DBN and ELM, which has been successful in electric load forecasting and fault diagnosis. Gao et al [29] proposed a method that combines adaptive wavelet transform with DBN-ELM to achieve the classification of power quality disturbances. Xu et al [31] employed a prediction model based on DBN-ELM, which effectively achieves electric load forecasting.…”

Section: Introductionmentioning

confidence: 99%

Improved DBO-VMD and optimized DBN-ELM based fault diagnosis for control valve

Zhang,

Han

et al. 2024

Meas. Sci. Technol.

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Control valves play a vital role in process production. In practical applications, control valves are prone to blockage and leakage faults. At the small control valve openings, the vibration signals exhibit the drawbacks of significant interference and weak fault characteristics, which causes subpar fault diagnosis performance. To address the issue, a diagnostic model based on optimized Variational Mode Decomposition (VMD) and improved Deep Belief Network-Extreme Learning Machine (DBN-ELM) is proposed. Firstly, good point set population initialization, nonlinear convergence factor, and adaptive Gaussian-Cauchy mutation strategies are applied in the dung beetle optimization algorithm (DBO) to escape local optima. Then, the improved DBO (IDBO) is used to optimize VMD parameters to obtain a series of modal components. Next, the generalized dispersion entropy (GDE) is formed by the combination of Generalized Gaussian Distribution and refined composite multiscale fluctuation-based dispersion entropy. The maximum correlation coefficient modal components are applied to extract generalized dispersion entropy. Finally, the IDBO is applied to optimize the parameters of the DBN-ELM network to improve the classification performance of control valve faults. The comparative experiment results demonstrate that the proposed model can extract effective features and the diagnostic accuracy reaches 99.87%.

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Power quality disturbance classification under noisy conditions using adaptive wavelet threshold and DBN-ELM hybrid model

Cited by 36 publications

References 37 publications

A Comprehensive Review of Deep-Learning Applications to Power Quality Analysis

A Comprehensive Review of Deep-Learning Applications to Power Quality Analysis

Classification of multiple power quality disturbances based on continuous wavelet transform and lightweight convolutional neural network

Improved DBO-VMD and optimized DBN-ELM based fault diagnosis for control valve

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