Detection of Power Quality Event using Histogram of Oriented Gradients and Support Vector Machine

Kapoor, Rajiv; Gupta, Rashmi; Sơn, Lê Hoàng; Jha, Sudan; Kumar, Raghvendra

doi:10.1016/j.measurement.2018.02.008

Cited by 78 publications

(23 citation statements)

References 24 publications

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“…Hu et al 201 proposed an automatic PQD&C technique using wavelet packet energy entropy and weighted SVM. 57 Kapoor et al 207 proposed a novel technique to differentiate PQ disturbances using the histogram of oriented gradients (HOG) and SVM. 50,57,[202][203][204][205] A PQD&C technique based on WPT and multiclass SVM was presented by Zhang et al 60 to classify PQ disturbances.…”

Section: Svm-based Approachmentioning

confidence: 99%

“…An effective smart PQ detection technique comparing DWT for feature extraction, KAM algorithm for optimal FS, and LS-SVM for classification process was given in Erişti. 57 Kapoor et al 207 proposed a novel technique to differentiate PQ disturbances using the histogram of oriented gradients (HOG) and SVM.…”

Section: Svm-based Approachmentioning

confidence: 99%

“…In addition to the aforementioned techniques (Sections 3.3.1-3.3.6), there are few other PRTs extensively utilized for the automatic recognition of the PQDs. Such PRTs for PQ disturbance classification are binary feature matrix (BFM), 74 Waikato Environment for Knowledge Analysis (WEKA), 160 support vector data description (SVDD), 224 k-nearest neighbour (KNN) algorithm, 225 hidden Markov models (HHMs), 50,66,226 random forest (RF) classifier, 207 and group data handling. 227…”

Section: Miscellaneous Prt-based Approachmentioning

confidence: 99%

See 2 more Smart Citations

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

Mishra

2019

Int Trans Electr Energ Syst

130

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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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Section: Svm-based Approachmentioning

confidence: 99%

Section: Svm-based Approachmentioning

confidence: 99%

Section: Miscellaneous Prt-based Approachmentioning

confidence: 99%

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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

130

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“…is goal requires estimating the aircraft direction in the remote sensing image. Given the typical symmetry of aircraft fuselage, we can draw a histogram of oriented gradients (HOG) by this feature [6,7]. e direction of the gradient vector distribution is the direction of aircraft fuselage.…”

Section: Target Direction Estimationmentioning

confidence: 99%

Remote Aircraft Target Recognition Method Based on Superpixel Segmentation and Image Reconstruction

Chen

Wang

2020

Mathematical Problems in Engineering

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Satellite images are always with complex background and shadow areas. These factors can lead to target segmentation break up and recognition with a low accuracy. Aiming at solving these problems, we proposed an aircraft recognition method based on superpixel segmentation and reconstruction. First, we need to estimate the orientation of an aircraft by using histograms of oriented gradients. And then, an improved Simple Linear Iterative Cluster (SLIC) superpixel segmentation algorithm is provided. By comparing texture feature instead of color feature space, we cluster the pixels that are with the same features. Last, through target template images and orientation, we reconstruct the superpixels. Also, the lowest error matching ratio is the recognized target. The test results show that the algorithm is robust to noise and recognize more aircrafts. Especially, when the satellite images with complex background and shadow areas, our method recognizes accuracy better than other methods. It can satisfy the demand of satellite image aircraft recognition.

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“…The use of low-level visual features fails to capture image semantics. A histogram of oriented gradients (HOG) [3] is related to the scale-invariant feature transform (SIFT) and the context of shape differs from the computed dense grid of regular images. The HOG requires local contrast normalization to improve accuracy.…”

Section: Introductionmentioning

confidence: 99%

Greedy Learning of Deep Boltzmann Machine (GDBM)’s Variance and Search Algorithm for Efficient Image Retrieval

Ghrabat

Abduljabbar

et al. 2019

IEEE Access

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Despite extensive research on content-based image retrieval, challenges such as low accuracy, incapability to handle complex queries and high time consumption persist. Initially, a preprocessing technique is introduced in this study, a technique that uses a median filter to remove noise to achieve improved accuracy and reliability. Then, Fourier and circularity descriptors are extract in an effective manner correspondent to the texture and affine shape adaptation features. In addition, various descriptors, such as color histogram, color moment, color autocorrelogram and color coherency vector, are extracted as the invariant color features. The multiple ant colony optimization (MACOBTC) approach is implemented with whole features to find relevant features. Finally, the relevant features are utilized for the greedy learning of deep Boltzmann machine classifier (GDBM). The proposed approach obtains effective performance and accurate results on four datasets and is analyzed with various parameters such as accuracy, precision, recall, Jaccard, Dice, and Kappa coefficients. The GDBM provides a 25% increase in accuracy compared with existing techniques, such as the a priori classification algorithm.

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Detection of Power Quality Event using Histogram of Oriented Gradients and Support Vector Machine

Cited by 78 publications

References 24 publications

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

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

Remote Aircraft Target Recognition Method Based on Superpixel Segmentation and Image Reconstruction

Greedy Learning of Deep Boltzmann Machine (GDBM)’s Variance and Search Algorithm for Efficient Image Retrieval

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