Detection and Classification of Power Quality Disturbances Using Sparse Signal Decomposition on Hybrid Dictionaries

Manikandan, M. Sabarimalai; Samantaray, Sandeep; Kamwa, Innocent

doi:10.1109/tim.2014.2330493

Cited by 178 publications

(99 citation statements)

References 24 publications

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“…To evaluate the simulation performance between the proposed method and other recognition methods, a comparison among [4,6,10,15] is performed. The classification accuracy of [4,6,10,15] is 96.7, 97.1, 93.2, and 95.8%, respectively.…”

Section: Performance Comparison and Discussionmentioning

confidence: 99%

“…The classification accuracy of [4,6,10,15] is 96.7, 97.1, 93.2, and 95.8%, respectively. Besides, the classification accuracy of this study and the mentioned investigations are mixed with the noise value of 20 dB.…”

Section: Performance Comparison and Discussionmentioning

confidence: 99%

“…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%

“…Common methods include short-time Fourier transform (STFT) [1], wavelet transform (WT) [2], S-transform (ST) [3], and sparse signal decomposition (SSD) [4]. Nevertheless, STFT has a single time-frequency resolution and does not satisfy the requirement that various disturbance patterns have different time-frequency resolutions in different frequency domains.…”

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: Performance Comparison and Discussionmentioning

confidence: 99%

Section: Performance Comparison and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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“…To this aim, the proposed automatic system encompasses the adoption of Machine Learning techniques [23]- [24] to solve the problem of data mining from the feature descriptors.…”

Section: Feature Extraction and Classificationmentioning

confidence: 99%

I3DermoscopyApp: Hacking Melanoma thanks to IoT technologies

Leo¹,

Liguori²,

Paciello³

et al. 2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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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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Detection and Classification of Power Quality Disturbances Using Sparse Signal Decomposition on Hybrid Dictionaries

Cited by 178 publications

References 24 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

I3DermoscopyApp: Hacking Melanoma thanks to IoT technologies

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

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