Recognition of Underlying Causes of Power Quality Disturbances Using Stockwell Transform

Kumar, Rajat; Singh, Bhim; Kumar, Raj; Marwaha, Sanjay

doi:10.1109/tim.2019.2926876

Cited by 28 publications

(8 citation statements)

References 21 publications

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“…In [47], high impedance fault detection has been performed using ST which extracts the third harmonic component of phase angle of the current waveform. The authors in [48] recognized various underlying causes and types of PQ disturbances using a computationally efficient S-Transform-based decision tree.…”

Section: Standard Stmentioning

confidence: 99%

“…The accuracy of Standard ST (oldest) [18], Modified ST (newest) [28], and the proposed ST, in phase angle jump (PAJ) estimation, have been investigated for a voltage sag signal. The PAJ is nothing but the shift in voltage zero crossings which is further helpful to determine the cause of a PQD disturbance [48]. It is the largest value of phase angle excluding the transition segments [61].…”

Section: Case Studymentioning

confidence: 99%

“…It is the largest value of phase angle excluding the transition segments [61]. The complete details of PAJ and the different techniques for its estimation have been presented in [48], [61]. Initially, a synthetic voltage sag signal is generated in MATLAB as per the international standards IEEE 1159 [62] and IEC 61000-4-30 [63] with a sampling frequency and aggregation period of 3.2 kHz and 0.2s respectively.…”

Section: Case Studymentioning

confidence: 99%

See 2 more Smart Citations

A Comprehensive Overview on Modified Versions of Stockwell Transform For Power Quality Monitoring

Kumar¹,

Saxena²,

Kumar

et al. 2022

IEEE Access

Self Cite

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The increasing trends toward the accurate identification of power quality disturbances (PQD) via power quality (PQ) monitoring require an appropriate digital signal processing (DSP) technique and a robust classifier. To this end, Stockwell transform (ST), one of the most efficient feature extraction DSP tools, and its several variants play an utmost role in PQ assessment framework. Its time-varying spectral characteristics generally extract the local instantaneous frequency spectrum from the global temporary behavior of PQD signal. However, the Standard ST suffers from thepoor time-frequency resolution because of its frequency-dependent Gaussian window (GW). While the analysis of the statistically time-varying signals requires a suitable balance between time and frequency resolution. To this end, this paper provides a comprehensive literature review on several modified versions of Standard ST for the first time to reduce the computational complexity of the algorithm as well as maximize the energy concentration of the timefrequency plane. A comparative analysis of all the modified STs has been presented in tabular form to provide the key characteristics of each technique. Additionally, a case study has been presented to substantiate the highest accuracy of the proposed algorithm over the other ST variants. Apart from the PQD classification, miscellaneous applications of Standard ST and its modified variants have been indicated. This review paper may provide a valuable resource to the researchers for further improvement of the timefrequency resolution of ST not only in classifying PQD but also for its other wide applications.INDEX TERMSEnergy concentration, power quality monitoring, power quality disturbance, Stockwell transform, time-frequency analysis.

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Section: Standard Stmentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

See 1 more Smart Citation

A Comprehensive Overview on Modified Versions of Stockwell Transform For Power Quality Monitoring

Kumar¹,

Saxena²,

Kumar

et al. 2022

IEEE Access

Self Cite

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“…Then, the PQD can be identified from a graphical perspective. Some other technologies include ensemble empirical mode decomposition (EMD) (Hukampal and Mohanty, 2020), Stockwell transform (ST) (Kumar et al, 2020;Panigrahi et al, 2022), and short-time Fourier transform (STFT) (De Frein and Rickard, 2011). Similar to VMD, the EMD decomposes the PQDs into multiple modes.…”

Section: Introductionmentioning

confidence: 99%

Adaptive compound power quality disturbance detection via OMD and improved networks for renewable energy systems

Wu,

Liu,

Zhu

et al. 2024

Front. Energy Res.

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In the evolving landscape of power systems, the integration of various renewable energy resources (RERs) introduces complex challenges, particularly in maintaining power quality, which are paramount for system stability. To address this issue, an adaptive power quality disturbance (PQD) detection framework is implemented in this paper. First, the optimal mode decomposition (OMD) is developed to decompose the compound PQDs into sub-ingredients to make them more visible based on the optimal energy ratio. Subsequently, we propose an improved attention convolutional neural network (IACNN), an advanced neural network architecture that leverages an enhanced attention mechanism to expedite the identification of PQDs. Importantly, the sub-ingredients can be strengthened based on the established PQD detection framework. Finally, a series of experiments are conducted under different noise levels and various types of PQDs. The results demonstrate that the proposed framework has profound detection effectivity with about 99.2% accuracy under the simulation condition of 20 dB noise level. In addition, the experimental verification analysis proves a satisfactory real-time performance. This underscores the potential of the proposed framework as a significant advancement in the realm of power quality management, offering a robust solution to the challenges posed by the integration of RERs into modern power systems.

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“…The energy (or magnitude) at a specific frequency can be viewed as a function of time. The spectral localisation properties of the ST has led to applications in many fields, such as biomedical engineering [10–16]; geophysics [17, 18]; biometric [19]; electrical engineering [20–26]; image processing [27, 28], etc. The ST has also been extended to design generalised ST [29], fractional ST [30, 31] and canonical ST [32].…”

Section: Introductionmentioning

confidence: 99%

Compact S‐transform for analysing local spectrum

Pradhan

Mansinha

2020

IET signal process.

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The Fourier transform of a N point time series is a N point complex series, while the S‐transform (ST) of the same time series is a N×N 2D time–frequency complex matrix. The computation and storage of N2−N additional points are a major drag on the usage of ST. In this study the compact S‐transform (cST) is presented, with efficiencies brought about through computation of only selected voices (frequencies). The cST spectrum has uncomputed voice gaps that increase in width towards the higher frequencies. Plot of the cST magnitude spectrum is virtually indistinguishable from the ST magnitude plot. Local spectrum at any spot on the cST can be quickly examined in detail through interpolation. The cST requires the computation of approximately 3N voices compared to falsefalse⌊N/2falsefalse⌋+1 for the ST. The proportion of computed voices decrease for larger N. For N=1024, ∼20% of the voices in the time‐frequency spectrum is computed; for N=2048 only 14% of the voices is computed. For applications, such as audio and speech signal processing where segments of one million samples are not uncommon, <1% of the voices are computed, thereby reducing the computation time by ∼99%.

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Recognition of Underlying Causes of Power Quality Disturbances Using Stockwell Transform

Cited by 28 publications

References 21 publications

A Comprehensive Overview on Modified Versions of Stockwell Transform For Power Quality Monitoring

A Comprehensive Overview on Modified Versions of Stockwell Transform For Power Quality Monitoring

Adaptive compound power quality disturbance detection via OMD and improved networks for renewable energy systems

Compact S‐transform for analysing local spectrum

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