A classification method for multiple power quality disturbances using EWT based adaptive filtering and multiclass SVM

Thirumala, Karthik; Pal, Sushmita; Jain, Trapti; Umarikar, Amod C.

doi:10.1016/j.neucom.2019.01.038

Cited by 90 publications

(56 citation statements)

References 38 publications

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“…We also summarized some articles that mentioned the test time, such as [4, 6 9, 27 29, 30]. Although the test times in some of these articles are shorter than the test time reported here, it cannot be ignored that these test times are often affected by the factors such as the number of sample points, the number of disturbances in the test set, and the processor speed.…”

Section: Methodsmentioning

confidence: 94%

A simple gated recurrent network for detection of power quality disturbances

Wei

2020

IET Generation Trans & Dist

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This paper presents a new concise deep learning-based sequence model to detect the power quality disturbances (PQD), which only uses original signals and does not require pre-processing and complex artificial feature extraction process. A simple gated recurrent network (SGRN) with a new recurrent cell structure is developed, which consists of only two gates: forget gate and input gate, and two weight matrices. Compared with the standard Recurrent Neural Network (RNN) model, the training process of the proposed method is more stable and the prediction accuracy is higher. In addition, this special structure retains basic non-linearity and long-term memory, while enabling the simple gated recurrent network model to be superior to Long Short-Term Memory (LSTM) Network and Gated Recurrent Unit (GRU) Network in terms of the number of parameters (i.e. memory cost) and detection speed. In the light of the experimental results, the simple gated recurrent network algorithm can achieve 99.07% detection accuracy, and contains only 18,959 parameters, which indicates that our proposed method is easier to deploy in resource-constrained internet of things (IoT) micro-controllers.

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Section: Methodsmentioning

confidence: 94%

A simple gated recurrent network for detection of power quality disturbances

Wei

2020

IET Generation Trans & Dist

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“…Table 5 lists the overall fault diagnosis performance under different test sets in a noisy environment. As listed in Table 5, the proposed parallel EMD-SVM [51,52] machine learning [53][54][55][56] method has a robust anti-noise performance, with high fault diagnosis accuracy in noisy conditions.…”

Section: Anti-noise Performance Of Parallel Emd-svm In a Noisy Conmentioning

confidence: 99%

Parallel Machine Learning Algorithm Using Fine-Grained-Mode Spark on a Mesos Big Data Cloud Computing Software Framework for Mobile Robotic Intelligent Fault Recognition

Xian

2020

IEEE Access

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An accurate and efficient intelligent fault diagnosis of mobile robotic roller bearings can significantly enhance the reliability and safety of mechanical systems. To improve the efficiency of intelligent fault classification of mobile robotic roller bearings, this paper proposes a parallel machine learning algorithm using fine-grained-mode Spark on a Mesos big data cloud computing software framework. Through the segmentation of datasets and the support of a parallel framework, the parallel processing technology Spark is combined with a support vector machine (SVM), and a parallel single-SVM algorithm is realized using Scala language. In this approach, empirical mode decomposition (EMD) is applied to extract the energy of the acceleration vibration signal in different frequency bands as features. The parallel EMD-SVM approach is applied to detect faults in mobile robotic roller bearings from fault vibration signals. The experimental results show that it can accurately and effectively identify the faults, and it outperforms existing methods based on parallel deep belief network (DBN) and parallel radial basis function neural network under different training set sizes. Fault classification tests are conducted on outerrace and inner-race faults: in both cases, the proposed parallel EMD-SVM outperforms the serial EMD-SVM in terms of the classification accuracy and classification time under different test sizes. For a small number of nodes, the processing time of the proposed Spark model is less than that of Hadoop but close to that of Storm. For 17 slave nodes, the average precision, average recall, and average F1 score of Spark on Mesos in the fine-grained mode reach 97.3, 97.8, and 97.9%, respectively. The parallel EMD-SVM algorithm in the big data Spark cloud computing framework can improve the accuracy of intelligent fault classification, albeit by a small margin, with higher processing speed and learning convergence rate.

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“…33,34 Most recently, EWT has been demonstrated to be effective in ensuring the stable accuracy of decomposing multiple frequency components. 35,36 To obtain an unbiased comparison of HT and the proposed approach in identifying instantaneous frequency under strong noise environment, EWT is used in both methods for signal decomposition. Figure 5 shows the time-frequency distribution from the EWT-HT-based method without or with 30% noise.…”

Section: Numerical Casementioning

confidence: 99%

High‐resolution time‐frequency representation for instantaneous frequency identification by adaptive Duffing oscillator

Peng

Hao

et al. 2020

Struct Control Health Monit

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Time-frequency analysis of structural vibration responses provides essential information for structural system identification, modal updating, and condition assessment. However, spurious peaks introduced by the strong noise will significantly increase the false positive rate as well as compromise the sparse time-frequency signal representation. This paper proposes a high-resolution time-frequency representation approach for nonstationary signals polluted with strong noise. With a high sensitivity to detect slight frequency shift and an immunity to noise effect, the intermittent chaotic of Duffing oscillator system is introduced to accurately identify the time-varying instantaneous frequencies. Furthermore, an adaptive Duffing oscillator array is adopted to improve the instantaneous frequency identification efficiency. The feasibility and effectiveness of the proposed method are verified with numerical and experimental studies. Numerical studies are conducted on a multicomponent synthetic nonstationary signal as well as on a two-story shear building with time-varying stiffness under seismic loads. In experimental validations, the acceleration response of a laboratory bridge model under moving vehicle load is also analyzed by using the proposed approach to obtain the instantaneous frequency variations induced by the bridge-vehicle interaction. These results are compared with those obtained from a method based on empirical wavelet transform and Hilbert transform (EWT-HT), to highlight the superiority of the proposed approach in obtaining high-resolution time-frequency analysis results for nonstationary signals with strong noise.

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A classification method for multiple power quality disturbances using EWT based adaptive filtering and multiclass SVM

Cited by 90 publications

References 38 publications

A simple gated recurrent network for detection of power quality disturbances

A simple gated recurrent network for detection of power quality disturbances

Parallel Machine Learning Algorithm Using Fine-Grained-Mode Spark on a Mesos Big Data Cloud Computing Software Framework for Mobile Robotic Intelligent Fault Recognition

High‐resolution time‐frequency representation for instantaneous frequency identification by adaptive Duffing oscillator

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