An Improved Hilbert–Huang Method for Analysis of Time-Varying Waveforms in Power Quality

Senroy, Nilanjan; Suryanarayanan, Siddharth; Ribeiro, Paulo F.

doi:10.1109/tpwrs.2007.907542

Cited by 202 publications

(118 citation statements)

References 24 publications

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“…Our research is based on the Hilbert transform (HT), a mathematical tool that has been used in power systems to solve other problems (Browne et al 2008;Senroy et al 2007). HT has not been used previously for the analysis of pulse/square load waveforms.…”

Section: Load Disaggregationmentioning

confidence: 99%

“…The empirical mode decomposition algorithm (EMD) (Huang et al 1998) extracts these monocomponent functions called intrinsic mode functions (IMFs). However, the EMD does not always provide monocomponent signals (Senroy et al 2007). To solve this problem, several methods have been proposed in the literature.…”

Section: Hilbert Transformmentioning

confidence: 99%

“…To solve this problem, several methods have been proposed in the literature. These are mainly based on the addition of a masked signal to force the component extraction M-EMD (Deering and Kaiser 2005;Senroy et al 2007). The main difficulty of masking is that it depends on the heuristic nature of the mask used.…”

Section: Hilbert Transformmentioning

confidence: 99%

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Disaggregation of the electric loads of small customers through the application of the Hilbert transform

et al. 2014

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This paper is intended to explain how the possibilities of enabling technologies (advanced metering infrastructures) can be expanded on to evaluate end uses at the demand-side level. For example, these data allow validating the effective response to market prices (energy markets) or system events (demand response), and besides, the possibilities that energy efficiency offers (in capacity markets), mainly under the supervision of a load aggregator. Hilbert transform properties along with other mathematical tools are used to extract the characteristics of the more suitable uses for demand response policies from the aggregated load demand of the user. This is achieved without complex statistical analysis of the demand loads. The tool filters pulse waveforms (in this case, the components of daily demand) and provides the aggregator the main characteristics of load, both in normal state or under response to system events or market prices.

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Section: Load Disaggregationmentioning

confidence: 99%

Section: Hilbert Transformmentioning

confidence: 99%

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Disaggregation of the electric loads of small customers through the application of the Hilbert transform

et al. 2014

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“…On the other hand, the second task-the decomposition of multi-components is also an important problem in signal processing. The adaptive methods for signal separation should be explored [2,[8][9][10][11][12][13][14][15][16][17][18] especially for the separation of multiple components blended in noise data. That is to say, the separation of signals from real-life observed noisy data includes two steps' work, filtering the signals and then decomposing the signals to multiple independent components.…”

Section: Introductionmentioning

confidence: 99%

“…There have been some methods involving decomposing the superposed AM-FM components, including the frequency domain separation method such as the LFM (linear frequency modulation) component separation via FRFT [17], the empirical mode decomposition (EMD) [3,12], the improved EMD (IEMD) [2,8] (the method in [8] is only for pure FM signal), the masking method [13,14] and the Hilbert transform [1,19] based method [16]. The frequency domain separation method is the most traditional method to separate the superposed AM-FM components in frequency domain or fractional frequency domain [17] through finding the separation points or peak points.…”

Section: Introductionmentioning

confidence: 99%

Time-Varying Bandpass Filter Based on Assisted Signals for AM-FM Signal Separation: A Revisit

Gao¹,

Wang²,

Xu³

et al. 2013

JSIP

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In this paper, a new signal separation method mainly for AM-FM components blended in noises is revisited based on the new derived time-varying bandpass filter (TVBF), which can separate the AM-FM components whose frequencies have overlapped regions in Fourier transform domain and even have crossed points in time-frequency distribution (TFD) so that the proposed TVBF seems like a "soft-cutter" that cuts the frequency domain to snaky slices with rational physical sense. First, the Hilbert transform based decomposition is analyzed for the analysis of nonstationary signals. Based on the above analysis, a hypothesis under a certain condition that AM-FM components can be separated successfully based on Hilbert transform and the assisted signal is developed, which is supported by representative experiments and theoretical performance analyses on a error bound that is shown to be proportional to the product of frequency width and noise variance. The assisted signals are derived from the refined time-frequency distributions via image fusion and least squares optimization. Experiments on man-made and real-life data verify the efficiency of the proposed method and demonstrate the advantages over the other main methods.

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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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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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An Improved Hilbert–Huang Method for Analysis of Time-Varying Waveforms in Power Quality

Cited by 202 publications

References 24 publications

Disaggregation of the electric loads of small customers through the application of the Hilbert transform

Disaggregation of the electric loads of small customers through the application of the Hilbert transform

Time-Varying Bandpass Filter Based on Assisted Signals for AM-FM Signal Separation: A Revisit

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

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