Wavelet Ridge Technique Based Analysis of Power System Dynamics Using Measurement Data

Jha, Rajiv; Senroy, Nilanjan

doi:10.1109/tpwrs.2017.2783347

Cited by 8 publications

(3 citation statements)

References 32 publications

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“…Also warranted is a deeper evaluation WMS performance under varying noisy conditions, along with data from social interactions with/without clear transitions and perhaps even, social interactions with controllable virtual partners ( Fairhurst et al , 2013 ; Dumas et al , 2014 ). With such data, wavelet ridge detection ( Jha and Senroy, 2018 ), real-time change detection ( Hoover et al , 2012 ) and/or non-linear prediction error algorithms ( Kantz and Schreiber, 2003 ; Gorman et al , 2019 ) could be utilized for marking critical transitions in the observed coordination values. Applying these in such a multiscale fashion would also be novel and could potentially allow for detecting critical and meaningful changes in interpersonal synchrony.…”

Section: Potential Approaches For Improving and Generalizing Wmsmentioning

confidence: 99%

Windowed multiscale synchrony: modeling time-varying and scale-localized interpersonal coordination dynamics

Likens

Wiltshire

2020

Social Cognitive and Affective Neuroscience

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Social interactions are pervasive in human life with varying forms of interpersonal coordination emerging and spanning different modalities (e.g., behaviors, speech/language, and neurophysiology). However, during social interactions, as in any dynamical system, patterns of coordination form and dissipate at different scales. Historically, researchers have used aggregate measures to capture coordination over time. While those measures (e.g., mean relative phase, cross-correlation, coherence) have provided a wealth of information about coordination in social settings, some evidence suggests that multiscale coordination may change over the time course of a typical empirical observation. To address this gap, we demonstrate an underutilized method, windowed multiscale synchrony, that moves beyond quantifying aggregate measures of coordination by focusing on how the relative strength of coordination changes over time and the scales that comprise social interaction. This method involves using a wavelet transform to decompose time series into component frequencies (i.e., scales), preserving temporal information and then quantifying phase synchronization at each of these scales. We apply this method to both simulated and empirical interpersonal physiological and neuromechanical data. We anticipate that demonstrating this method will stimulate new insights on the mechanisms and functions of synchrony in interpersonal contexts using neurophysiological and behavioral measures.

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Section: Potential Approaches For Improving and Generalizing Wmsmentioning

confidence: 99%

Windowed multiscale synchrony: modeling time-varying and scale-localized interpersonal coordination dynamics

Likens

Wiltshire

2020

Social Cognitive and Affective Neuroscience

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“…To extract the wavelet phase of a signal x ( t ), first, the continuous wavelet transform (CWT) of x ( t ) with a Morlet wavelet function

ψ ()(, t)

should be obtained as follows:

W_{x, ψ} ()(, τ, s) = \int_{- normal∞}^{+ normal∞} x (t) \frac{1}{\sqrt{s}} ψ^{*} ()(, \frac{t - τ}{s}) normald t

The cross‐WT of two signals x and y are then defined by the equation below:

W_{x y, ψ} ()(, τ, s) = W_{x, ψ} ()(, τ, s) W_{y, ψ}^{*} ()(, τ, s)

Therefore, the WPD of two signals x and y , which is calculated using (13), is used to identify the coherency between x and y . In this regard, two coherent generators have the phase difference of zero between the CWTs of their associated swing curves, whereas for two non‐coherent generators this value will be π or – π [99]

ϕ_{x y, ψ} ()(, τ, s) = \tan^{- 1} \frac{Im ()(, W_{x y, ψ} ()(, τ, s))}{Re ()(, W_{x y, ψ} ()(, τ, s))}

A relatively similar approach is also presented in [100], where instead of using Morlet function, the Morse wavelet function is utilised for obtaining the analytic wavelet transform of signal x ( t ). Then, by obtaining a scalogram for the transform and finding the ridge curves, these curves can be helpful in representing the analytical signal in the following form: …”

Section: Coherency Detection In Power Systemsmentioning

confidence: 99%

Taxonomy of coherency detection and coherency‐based methods for generators grouping and power system partitioning

Koochi

Esmaeili

Ledwich

2019

IET Generation, Transmission & Distribution

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“…Song et al (2016) proposed a wavelet-based scheme to generate the individual forecaster. Jha and Senroy (2018) used the wavelet ridge method to analyze the dynamic characteristics of the power system. Sabouri et al (2017) adopted the orthogonal discrete wavelet transform (ODWT) to research the plasma electrolytic oxidation (PEO) of aluminum at various periods during the electrolysis process.…”

Section: Introductionmentioning

confidence: 99%

Establishing the energy consumption prediction model of aluminum electrolysis process by genetically optimizing wavelet neural network

et al. 2022

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Nowadays, it is very popular to employ genetic algorithm (GA) and its improved strategies to optimize neural networks (i.e., WNN) to solve the modeling problems of aluminum electrolysis manufacturing system (AEMS). However, the traditional GA only focuses on restraining the infinite growth of the optimal species without reducing the similarity among the remaining excellent individuals when using the exclusion operator. Additionally, when performing arithmetic crossover or Cauchy mutation, a functional operator that conforms to the law of evolution is not constructed to generate proportional coefficients, which seriously restricted the exploitation of the hidden potential in genetic algorithms. To solve the above problems, this paper adopts three new methods to explore the performance enhancement of genetic algorithms (EGA). First, the mean Hamming distance (H-Mean) metric is designed to measure the spatial dispersion of individuals to alleviate selection pressure. Second, arithmetic crossover with transformation of the sigmoid-based function is developed to dynamically adjust the exchange proportion of offspring. Third, an adaptive scale coefficient is introduced into the Gauss-Cauchy mutation, which can regulate the mutation step size in real time and search accuracy for individuals in the population. Finally, the EGA solver is employed to deeply mine the optimal initial parameters of wavelet neural network (EGAWNN). Moreover, the paper provides the algorithm performance test, convergence analysis and significance test. The experimental results reveal that the EGAWNN model outperforms other relevant wavelet-based forecasting models, where the RMSE in test sets based on EGAWNN is 305.72 smaller than other seven algorithms.

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Wavelet Ridge Technique Based Analysis of Power System Dynamics Using Measurement Data

Cited by 8 publications

References 32 publications

Windowed multiscale synchrony: modeling time-varying and scale-localized interpersonal coordination dynamics

Windowed multiscale synchrony: modeling time-varying and scale-localized interpersonal coordination dynamics

Taxonomy of coherency detection and coherency‐based methods for generators grouping and power system partitioning

Establishing the energy consumption prediction model of aluminum electrolysis process by genetically optimizing wavelet neural network

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