Detection and classification of multi-complex power quality events in a smart grid using Hilbert–Huang transform and support vector machine

Hemapriya, C. K.; Suganyadevi, M. V.; Krishnakumar, Conjeevaram

doi:10.1007/s00202-020-00987-8

Cited by 15 publications

(9 citation statements)

References 24 publications

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“…Papers [5,16] both apply most of the techniques discussed in this review, and [17] exposes a selection of additional techniques, such as KF, HHT, RMS, and HOS. Other authors prefer the combined use of AI with one another technique; this is what happens in the case of [10] with HHT, [18] with RMS, [19] with THD, [6] with HOS, or [8] with ST. Papers [7,20,21] omit the use of other complementary techniques. Indeed, Figure 2 depicts a timeline relationship among analysis procedures and application fields.…”

Section: Resultsmentioning

confidence: 99%

Current Status and Future Trends of Power Quality Analysis

et al. 2022

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In this article, a systematic literature review of 153 articles on power quality analysis in PV systems published in the last 20 years is presented. This provides readers with an overview on PQ trends in several fields related to instrumental techniques that are being used in the smart grid to visualize the quality of the energy, establishing a solid literature base from which to start future research. A preliminary appreciation allows us to intuit that higher-order statistics are not implemented in measurement equipment and that traditional instrumentation is still used for the performance of measurement campaigns, not yielding the expected results since the information processed does not come from an electrical network from 20 years ago. Instead, current networks contain numerous coupled load effects; thus, new disturbances are not simple; they are usually complex events, the sum of several types of disturbances. Likewise, depending on the type of installation, the objective of the PQ analysis changes, either by detecting certain events or simply focusing on seeing the state of the network.

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Section: Resultsmentioning

confidence: 99%

Current Status and Future Trends of Power Quality Analysis

et al. 2022

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“…The input layer, pooling layer, convolution layer, fully connected layer and the output layer are included in the CNN model. The convolution layer is a signal processing model that captures low level features in the input data and translates into high level or global features.The signal processing operation in convolution layer is a filtering process that generates feature maps h k considering the inputs x by simple mathematical operation given as in (6). The parameter W k and b k are the weights and biases of the convolution layer and f(.)…”

Section: Cnn Modelmentioning

confidence: 99%

“…An appreciable amount of effort is recorded in the literature for analyzing PQ event classification. Classification of PQ events entails the use of feature extraction methods like "Fourier transforms (FT), S transforms (ST), Hilbert Huang transforms (HHT), and Wavelet transforms (WT)" based methods are reported in [6]- [9]. Power quality disturbance (PQD) classification is also heavily influenced by artificial intelligence techniques such as "support vector machine (SVM), artificial neural networks (ANN), fuzzy logic (FL), genetic algorithm (GA), deep learning" based methods are reported in [10]- [12].…”

Section: Introductionmentioning

confidence: 99%

Power quality event classification using complex wavelets phasor models and customized convolution neural network

Ramalingappa¹,

Manjunatha²

2022

IJECE

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Origin and triggers of power quality (PQ) events must be identified in prior, in order to take preventive steps to enhance power quality. However it is important to identify, localize and classify the PQ events to determine the causes and origins of PQ disturbances. In this paper a novel algorithm is presented to classify voltage variations into six different PQ events considering the space phasor model (SPM) diagrams, dual tree complex wavelet transforms (DTCWT) sub bands and the convolution neural network (CNN) model. The input voltage data is converted into SPM data, the SPM data is transformed using 2D DTCWT into low pass and high pass sub bands which are simultaneously processed by the 2D CNN model to perform classification of PQ events. In the proposed method CNN model based on Google Net is trained to perform classification of PQ events with default configuration as in deep neural network designer in MATLAB environment. The proposed algorithm achieve higher accuracy with reduced training time in classification of events than compared with reported PQ event classification methods.

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“…Moreover, it can also complete high-precision analysis in both time domain and frequency domain. Owing to HHT's good performance, it has been widely used in low-frequency vibration signal analysis, power quality analysis, mechanical structure fault diagnosis, and other fields [26,27].…”

Section: Hilbert-huang Transformmentioning

confidence: 99%

A Hilbert–Huang Transform-Based Adaptive Fault Detection and Classification Method for Microgrids

Lin

Niu

et al. 2021

Energies

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Fault detection in microgrids is of great significance for power systems’ safety and stability. Due to the high penetration of distributed generations, fault characteristics become different from those of traditional fault detection. Thus, we propose a new fault detection and classification method for microgrids. Only current information is needed for the method. Hilbert–Huang Transform and sliding window strategy are used in fault characteristic extraction. The instantaneous phase difference of current high-frequency component is obtained as the fault characteristic. A self-adaptive threshold is set to increase the detection sensitivity. A fault can be detected by comparing the fault characteristic and the threshold. Furthermore, the fault type is identified by the utilization of zero-sequence current. Simulations for both section and system have been completed. The instantaneous phase difference of the current high-frequency component is an effective fault characteristic for detecting ten kinds of faults. Using the proposed method, the maximum fault detection time is 13.8 ms and the maximum fault type identification time is 14.8 ms. No misjudgement happens under non-fault disturbance conditions. The simulations indicate that the proposed method can achieve fault detection and classification rapidly, accurately, and reliably.

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Detection and classification of multi-complex power quality events in a smart grid using Hilbert–Huang transform and support vector machine

Cited by 15 publications

References 24 publications

Current Status and Future Trends of Power Quality Analysis

Current Status and Future Trends of Power Quality Analysis

Power quality event classification using complex wavelets phasor models and customized convolution neural network

A Hilbert–Huang Transform-Based Adaptive Fault Detection and Classification Method for Microgrids

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