Diagnosis of Power Quality Events Based on Detrended Fluctuation Analysis

Das, Santanu; Pradhan, A. K.; Kedia, Ayush; Dalai, Sovan; Chatterjee, Biswendu; Chakravorti, S.

doi:10.1109/tie.2018.2795559

Cited by 47 publications

(22 citation statements)

References 30 publications

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“…In other words, the value of

e ()(, l)

provides information about the degree of deviation between the integrated signal

y false(l false)

and the local trend signal given by

y_{w} false(l false)

. The value of

e false(l false)

corresponding to a small window size represents high‐frequency fluctuations, whereas the value of

e ()(, l)

corresponding to a larger window size represents low‐frequency fluctuations [24]. Depending on the nature of the test signal, the size of windows can be adjusted from small size to large size.…”

Section: Application Of Dfamentioning

confidence: 99%

“…Finally, the root mean square error has been given by (7) corresponding to a particular window:

f ()(, k) = \sqrt{\frac{1}{K . k} \sum_{w = 1}^{K} \sum_{l = k ()(, w - 1) + 1}^{w . k} {false(e false(l false) false)}^{2}}

It is observed that with an increase in window size, the value of

f false(k false)

increases. Available literature suggests that the relationship between window size K and function

f false(k false)

can be modelled by the relationship of power law and can be expressed as

f false(k false) ≃ k^{γ}

, where γ represents the scaling parameter [24]. The value of γ gives the type of fluctuation in the original signal

x false(j false)

.…”

Section: Application Of Dfamentioning

confidence: 99%

See 1 more Smart Citation

Estimation of de‐trapped charge for diagnosis of transformer insulation using short‐duration polarisation current employing detrended fluctuation analysis

et al. 2020

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Researchers have shown that the value of charge carriers, de‐trapped from the oil–paper interface of power transformer insulation, is useful in carrying out the diagnosis. However, the evaluation of the de‐trapped charge requires the analysis of polarisation–depolarisation currents. Being an off‐line time‐consuming process, the measurement and analysis of polarisation and depolarisation current (PDC) data are not practically advantageous. The study presents a detrended fluctuation analysis‐based technique to estimate the magnitude of normalised de‐trapped charge using the polarisation current measured for a short duration. Using the proposed technique, the requirement of measuring the complete PDC data, for diagnosis purposes, can be eliminated. Further, the technique also eliminates the requirement of depolarisation current which in turn facilitates a reduction in equipment shutdown time. The applicability of the proposed technique is tested on the data obtained from several real‐life power transformers.

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“…In other words, the value of

e ()(, l)

provides information about the degree of deviation between the integrated signal

y false(l false)

and the local trend signal given by

y_{w} false(l false)

. The value of

e false(l false)

corresponding to a small window size represents high‐frequency fluctuations, whereas the value of

e ()(, l)

Section: Application Of Dfamentioning

confidence: 99%

“…Finally, the root mean square error has been given by (7) corresponding to a particular window:

f ()(, k) = \sqrt{\frac{1}{K . k} \sum_{w = 1}^{K} \sum_{l = k ()(, w - 1) + 1}^{w . k} {false(e false(l false) false)}^{2}}

It is observed that with an increase in window size, the value of

f false(k false)

increases. Available literature suggests that the relationship between window size K and function

f false(k false)

can be modelled by the relationship of power law and can be expressed as

f false(k false) ≃ k^{γ}

, where γ represents the scaling parameter [24]. The value of γ gives the type of fluctuation in the original signal

x false(j false)

.…”

Section: Application Of Dfamentioning

confidence: 99%

Estimation of de‐trapped charge for diagnosis of transformer insulation using short‐duration polarisation current employing detrended fluctuation analysis

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…The PQ event data can be obtained through repeatedly monitoring of power signal at various plants for a long period of time. However, the uncertainty in occurrence of PQ events in power distribution system result in inadequacy of real time data [28]. To avoid time lose due to the scarcity of real time data collection, the researchers have employed the mathematical model given in IEEE Standard 1159 for generating the PQ disturbances signals, for their analysis work.…”

Section: A Voltage Variation Signal Typesmentioning

confidence: 99%

Voltage Variation Signals Source Identification and Diagnosis Method

Tee¹,

Yusoff²,

Abdullah³

et al. 2019

IJACSA

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Power Quality (PQ) problem has become an important issue for generating bad impact to the users nowadays. It is important to detect and identify the source of the PQ problem. This paper presents a voltage variation signals source identification and diagnosis method by determining the average time frequency representation (TFR) phase power of the impedance. The signals focused in this study are the voltage variation signals, which include voltage sag, swell and interruption. The voltage variation signals from different source location (upstream, downstream as well as up and downstream) according to the IEEE Standard 1159 by using the mathematical models. The signals are first analyzed by using the Spectrograms which act as the feature producing tool. Then, the average power TFR of phase domain of each signal is calculated and tabulated. Finally, the performance of the method is identified by using support vector machine (SVM) and k-nearest neighbor (kNN). The results show that this method is an effective and suitable technique for identifying the source of voltage variation.

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“…These techniques include artificial neural networks, fuzzy systems, support vector machine and extreme learning machines and their variants [16] etc. These methods are fairly complex and involve large execution times and therefore in this paper a simple but efficient detrended fluctuation analysis (DFA) has been used to detect and classify various islanding and non‐islanding disturbances in DC micro grid [21]. DFA is useful for detecting the long correlation range of a non‐stationary time series under noise by removing the fluctuation in the signals.…”

Section: Introductionmentioning

confidence: 99%

Islanding detection in photovoltaic based DC micro grid using adaptive variational mode decomposition and detrended fluctuation analysis

Eluri

Dash

Dhar

2020

IET Generation Trans & Dist

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This study presents a novel approach using adaptive variational mode decomposition with detrended fluctuation analysis to detect the islanding disturbances for photovoltaic based DC micro grid. DC parameters are simple to estimate in comparison to AC profile. Thus DC parameters are recorded under islanding scenario, and processed through proposed adaptive variational mode decomposition which decomposes the signals into intrinsic mode functions. These segregated intrinsic mode functions are further selected optimally by choosing the significant weighted kurtosis index. This optimal selection (maximisation of weighted kurtosis index) is ensured by modified particle swarm optimisation in terms of number of modes (K) and penalty factor (σ). For detection and monitoring (D&M) accurate islanding scenario the significant intrinsic mode functions are subjected to detrended fluctuation analysis, where power exponent (α) values are utilised for correct detection (i.e. distinguishing islanding out of other grid contingencies by two and three dimensional scattering plots). The effectiveness of the proposed D&M for DC micro grid is established in this paper in terms of classification accuracy and relative computational time. The proposed DC side islanding D&M method is less complex (as compared to AC signals) to be implemented. Fastness and accuracy of proposed D&M is established and performed in MATLAB/Simulink platforms. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Diagnosis of Power Quality Events Based on Detrended Fluctuation Analysis

Cited by 47 publications

References 30 publications

Estimation of de‐trapped charge for diagnosis of transformer insulation using short‐duration polarisation current employing detrended fluctuation analysis

Estimation of de‐trapped charge for diagnosis of transformer insulation using short‐duration polarisation current employing detrended fluctuation analysis

Voltage Variation Signals Source Identification and Diagnosis Method

Islanding detection in photovoltaic based DC micro grid using adaptive variational mode decomposition and detrended fluctuation analysis

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