Classification of Multiple and Single Power Quality Disturbances Using a Decision Tree-Based Approach

Barbosa, Bruno Henrique Groenner; Ferreira, Danton Diego

doi:10.1007/s40313-013-0061-y

Cited by 20 publications

(10 citation statements)

References 26 publications

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“…The NF method is a good way to separate a low‐frequency component from high‐frequency components (transients). It has been widely used in literature for PQ disturbance detection and classification and can as well be used to extracting the transient component. Although the algorithm is computationally simple, harmonics will show up in the extracted transient, if the goal is to separate the transient from the fundamental component, because the harmonics are mixed with transient, then the same problem of the HPF method can be pointed out to the NF method.…”

Section: Discussionmentioning

confidence: 99%

Extracting the transient events from power system signals by independent component analysis

Ferreira

Seixas

Cerqueira

et al. 2015

Int. Trans. Electr. Energ. Syst.

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Summary This paper presents a method for extracting transient events from the power system signal based on independent component analysis. The extraction of transients is important to diagnose the system in terms of power quality, because the computation of power quality indices from the extracted events is more efficient and easier to be carried out. Furthermore, the proposed method can be applied as a preprocessing tool for transient characterization in disturbance detection, classification, and identification systems. Both qualitative and quantitative analyses demonstrate the efficiency and the advantages of the proposed method in comparison with other approaches commonly used for the same purpose. Copyright © 2015 John Wiley & Sons, Ltd.

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Section: Discussionmentioning

confidence: 99%

Extracting the transient events from power system signals by independent component analysis

Ferreira

Seixas

Cerqueira

et al. 2015

Int. Trans. Electr. Energ. Syst.

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“…Common classification techniques include support vector machines, decision trees, neural networks and k-nearest neighbour. Applications include classification of faults in power distribution networks (Lazzaretti et al 2013), classification of power quality disturbances (Barbosa and Ferreira 2013) and transmission line protection (Carvalho et al 2014).…”

Section: Adaptation As Classificationmentioning

confidence: 99%

A k-Nearest Neighbour Technique for Experience-Based Adaptation of Assembly Stations

Scrimieri

Ratchev

2014

J Control Autom Electr Syst

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We present a technique for automatically acquiring operational knowledge on how to adapt assembly systems to new production demands or recover from disruptions. Dealing with changes and disruptions affecting an assembly station is a complex process which requires deep knowledge of the assembly process, the product being assembled and the adopted technologies. Shop-floor operators typically perform a series of adjustments by trial and error until the expected results in terms of performance and quality are achieved. With the proposed approach, such adjustments are captured and their effect on the station is measured. Adaptation knowledge is then derived by generalising from individual cases using a variant of the k-nearest neighbour algorithm. The operator is informed about potential adaptations whenever the station enters a state similar to one contained in the experience base, that is, a state on which adaptation information has been captured. A case study is presented, showing how the technique enables to reduce adaptation times. The general system architecture in which the technique has been implemented is described, including the role of the different software components and their interactions.

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“…In the real power system network, multiple power quality (MPQ) disturbances have been occurred due to power failure, capacitors switching, power electronic circuits, etc . Many methods have been revealed for the detection and classification of single PQD signal .…”

Section: Introductionmentioning

confidence: 99%

“…In Liu et al, an automatic classification algorithm was presented for classification of single and multiple PQD based on wavelet norm entropy features and probabilistic neural network (PNN). Decision tree‐based method was proposed for the classification of multiple and single PQD . Kirshna et al proposed an image pattern recognition procedure for the classification of multiple PQD.…”

Section: Introductionmentioning

confidence: 99%

Classification of multiple power quality events via compressed deep learning

Liu

Hussain

Shen

et al. 2019

Int Trans Electr Energ Syst

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Summary This paper presents a recently established compressed sensing (CS) and sparse autoencoder (SAE) based on deep learning (DL) method for classification of single and multiple power quality disturbances (PQDs). The CS technique is paying considerable attention in recent years due to below sampling rate comparatively Nyquist sampling. Initially, the CS technique is applied to extract the features of PQD waveforms. The extracted features are applied as inputs to the sparse autoencoder based on DL for classification of nine single and 22 combined classes of PQDs. The DL helps to remove a redundant feature and improves classification performance. Finally, backpropagation is applied to fine‐tune the entire network. The effectiveness of the proposed algorithm has been tested with more than 6580 numbers of real and synthetic single and multiple PQD data, and the results are recorded. High correct classification rate is obtained with noise and without noise level. Noise level was considered from 20 to 50 dB. The performance of the proposed technique has been assessed by comparing the results against recently reported methods. Results show that the proposed CS‐ and SAE‐based DL algorithms can be efficiently used for single and multiple PQDs classifications.

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Classification of Multiple and Single Power Quality Disturbances Using a Decision Tree-Based Approach

Cited by 20 publications

References 26 publications

Extracting the transient events from power system signals by independent component analysis

Extracting the transient events from power system signals by independent component analysis

A k-Nearest Neighbour Technique for Experience-Based Adaptation of Assembly Stations

Classification of multiple power quality events via compressed deep learning

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