Power Quality Disturbance Classification Using the Inductive Inference Approach

Abdel-Galil, T.K.; Kamel, Mohamed S.; Youssef, Abou-Bakr M.; El-Saadany, E.F.; Salama, M.M.A.

doi:10.1109/tpwrd.2003.822533

Cited by 142 publications

(61 citation statements)

References 14 publications

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“…So for a data set S of instances from m classes C1, C2 … Cm, the information (entropy) is describes in (5), where pk is the probability of Ck in S [14]. (5) In a classification problem of m classes, given a set of training instances S, for a test y with n outcomes, the entropy is given in (6). (6) Information gain is calculated by (7).…”

Section: S-transformmentioning

confidence: 99%

“…The first focusing in obtaining a suitable pattern that allow distinguish clearly each disturbance, by the use of time-frequency transforms for feature extraction, as Wavelet transform (WT) [2] and Stransform (ST) [3]. The second is oriented to use a classifier able to assign each disturbance correctly in its class, so the most of the artificial intelligent techniques have been combined with WT or ST, as Artificial Neural Networks (ANN) [4], [5], Decision Tree [6], [7], Fuzzy Logic [8], Hidden Markov Model [9] or Support Vector Machines [10], [11]. Many works consider power quality disturbances as signals that contain a unique given event in a time interval.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Decision Tree and S-transform based approach for power quality disturbances classification

Rodríguez

Merino

Aguado

et al. 2013

4th International Conference on Power Engineering, Energy and Electrical Drives

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Abstract-In this paper, it is presented an automated classification based on S-transform as feature extraction tool and Decision Tree as algorithm classifier. The signals generated according to mathematical models, including complex disturbances, have been used to design and test this approach, where noise is added to the signals from 40dB to 20dB.Finally, several disturbances, simple and complex, have been considered to test the implemented system. Evaluation results verifying the accuracy of the proposed method are presented.

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Section: S-transformmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Decision Tree and S-transform based approach for power quality disturbances classification

Rodríguez

Merino

Aguado

et al. 2013

4th International Conference on Power Engineering, Energy and Electrical Drives

View full text Add to dashboard Cite

show abstract

“…To resolve the fixed resolution problem of STFT, many researchers have proposed the use of the WT approach to analyzing the power system disturbances (Santoso et al, 1996;Perunicic et al,1998;Santoso et al, 2000a;Ibrahim et al, 2002;Zhu et al, 2004;Abdel-Galil et al, 2004). The WT approach prepares a window that automatically adjusts to give proper resolutions of both the time and the frequency.…”

Section: Features Extractionmentioning

confidence: 99%

Power quality event classification: an overview and key issues

Saxena¹,

Verma²,

Singh³

2010

Int. J. Eng. Sci. Tech

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Power quality (PQ) issue has attained considerable attention in the last decade due to large penetration of power electronics based loads and/or microprocessor based controlled loads. On one hand these devices introduce power quality problem and on other hand these mal-operate due to the induced power quality problems. PQ disturbances/events cover a broad frequency range with significantly different magnitude variations and can be non-stationary, thus, accurate techniques are required to identify and classify these events/disturbances. This paper presents a comprehensive overview of different techniques used for PQ events' classifications. Various artificial intelligent techniques which are used in PQ event classification are also discussed. Major Key issues and challenges in classifying PQ events are critically examined and outlined.

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“…The papers in References [1,35,36] include only seven types of disturbances (exclude transient and flicker) but use same mathematical equations as used in this paper.…”

Section: Performance Comparisonmentioning

confidence: 99%

Wavelet transform and multi-class relevance vector machines based recognition and classification of power quality disturbances

Moravej

Pazoki

Abdoos

2010

Euro. Trans. Electr. Power

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Recognition of power quality events by analyzing the voltage waveform disturbances is a very important task for the power system monitoring. This paper presents an integrated model for recognizing power quality disturbances (PQDs) using a new wavelet multi-class relevance vector machines (WMRVMs). The so-called relevance vector machines (RVMs) is an effective classification tool. The proposed method employs the wavelet transform techniques to extract the most important and significant feature from details and approximation waves. The obtained feature vectors are used for training the RVMs to classify the PQDs. Various transient events are tested, the result shows that the classifier can detect and classify different power quality signal correctly. Finally, sensitivity of the proposed algorithm under noise condition is investigated in this paper.

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Power Quality Disturbance Classification Using the Inductive Inference Approach

Cited by 142 publications

References 14 publications

A Decision Tree and S-transform based approach for power quality disturbances classification

A Decision Tree and S-transform based approach for power quality disturbances classification

Power quality event classification: an overview and key issues

Wavelet transform and multi-class relevance vector machines based recognition and classification of power quality disturbances

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