Disturbance Classification Using Hidden Markov Models and Vector Quantization

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

doi:10.1109/tpwrd.2004.843399

Cited by 52 publications

(32 citation statements)

References 14 publications

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“…Finally, to evaluate the practical performance of the proposed algorithm, a comparison among each method in Table 4 is performed, except for [4,9,10,22,23], by using measured data. The classification accuracy of [6,14,15] for measured data is 98.3, 96.3, and 95.2%, respectively, whereas the classification accuracy of this study is 98.6%.…”

Section: Performance Comparison and Discussionmentioning

confidence: 99%

“…As abovementioned, the number of linguistic values in its universe is 11, corresponding to the values of F1 for all patterns of disturbances. Therefore, depending on the minimum and maximum features of each pattern, the membership function of this linguistic value can be calculated by (9). For example, the minimum and maximum features of sag (shown as the ordinate value 1 in Fig.…”

Section: Membership Functionmentioning

confidence: 99%

“…After obtaining the features that represent the essential information of disturbance signals, methods such as support vector machine (SVM) [5][6][7], binary feature matrix [8], and hidden Markov model (HMM) [9] should be used to confirm the disturbance patterns of the signal samples. These methods have successfully realized the automatic recognition of PQ disturbances.…”

Section: Introductionmentioning

confidence: 99%

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Recognition and Classification of Power Quality Disturbances on the basis of Pattern Linguistic Values

Liu¹,

Liu²,

Xu³

2016

Journal of Electrical Engineering and Technology

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-This paper presents a new recognition and classification method for power quality (PQ) disturbances on the basis of pattern linguistic values. This method solves the difficulty of recognizing disturbances rapidly and accurately by using fuzzy logic. This method uses classification disturbance patterns to define the linguistic values of fuzzy input variables and used the input variables of corresponding disturbance pattern to set membership functions. This method also sets the fuzzy rules by analyzing the distribution regularities of the input variable values. One characteristic of this method is that the linguistic values of fuzzy input variables and the setting of membership functions are not only related to the input variables but also to the character of classification disturbance and the classification results. Furthermore, the number of fuzzy rules is equal to the number of disturbance patterns. By using this method for disturbance classification, the membership function and design of fuzzy rules are directly related to the objective of classification, thus effectively reducing the complexity of the design process and yielding accurate classification results. The classification results of the simulation and measured data verify the feasibility and effectiveness of this method.

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Section: Performance Comparison and Discussionmentioning

confidence: 99%

Section: Membership Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Recognition and Classification of Power Quality Disturbances on the basis of Pattern Linguistic Values

Liu¹,

Liu²,

Xu³

2016

Journal of Electrical Engineering and Technology

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“…(6) Information gain is calculated by (7). (7) Split information is calculated by (8) (8) Information gain ratio is determined by (9), the split with maximum gain ratio selected. (9) Power quality disturbances can be classifies using a deterministic decision tree by sorting them from the root to some leaf node; this node identifies a class value (disturbance).…”

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%

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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Power quality disturbance detection and classification using signal processing and soft computing techniques: A comprehensive review

Mishra

2019

Int Trans Electr Energ Syst

137

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Summary Power quality (PQ) studies have gained huge attention from the academics and the industry over the past three decades. The main objective of this article is to provide a comprehensive review on the state‐of‐the‐art techniques based on digital signal processing (DSP) and machine learning for automatic recognition of PQ events. It is aimed to present extensive information on the status of detection and classification of PQ events to the academics following a line of investigation on the similar domain. On the other hand, microgrid is one of the emerging architecture under the umbrella of smart grid infrastructure. In microgrid environment, the integration of renewable energy sources and distributed generators boosts the application of power electronic technology, which is the main cause of various PQ issues. Therefore, various PQ detection and classification (PQD&C) schemes for microgrid application using DSP and machine learning techniques have been reviewed in this article. Moreover, a comparative assessment on various PQD&C techniques has been carried out in details considering several criteria, such as type of data used (synthetic or real world), type of PQ disturbance studied (single or multiple), and performance in terms of accuracy (both noiseless and noisy environment). Hence, several types of research work in PQD&C area, such as the detection principles, benefits, and weaknesses of former works related to each PQD&C technique, are tinted in this manuscript. Therefore, from this review, the opportunities in PQD&C research domain in the power system can be explored further.

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Disturbance Classification Using Hidden Markov Models and Vector Quantization

Cited by 52 publications

References 14 publications

Recognition and Classification of Power Quality Disturbances on the basis of Pattern Linguistic Values

Recognition and Classification of Power Quality Disturbances on the basis of Pattern Linguistic Values

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

Power quality disturbance detection and classification using signal processing and soft computing techniques: A comprehensive review

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