Fault analysis in photovoltaic generation based DC microgrid using multifractal detrended fluctuation analysis

Prasad, Eluri N.V.D.V.; Dash, P.K.

doi:10.1002/2050-7038.12564

Cited by 11 publications

(7 citation statements)

References 30 publications

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“…In the proposed method of fault detection, it is validated that the detection times for various types of faults are less as compared to the detection time in Prasad and Dash (2020) presented in Table 3. So, it can be concluded that the proposed detection technique is fast in detecting various types of faults in utility grid-integrated DC microgrid.…”

Section: Results Analysismentioning

confidence: 92%

“…A current derivative, directional/differential current comparison and an adaptive droop method are used to detect and isolate the fault in standalone DC microgrid (Augustine et al , 2021). Cummulative sum (CuSum) and Teager energy operator-based protection and multifractal detrended fluctuation analysis for classification is presented in Prasad and Dash (2020).…”

Section: Introductionmentioning

confidence: 99%

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Fault detection and classification of DC microgrid based on VMD

Barik

2022

COMPEL

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Purpose This paper aims to present a new fault detection and classification scheme of both DC faults and AC faults on a DC microgrid network. Design/methodology/approach To achieve reliable protection, the derivative of DC current signal is decomposed into several intrinsic modes using variational mode decomposition (VMD), which are then used as inputs to the Hilbert–Haung transform technique to obtain the instantaneous amplitude and frequency of the decomposed modes of the signal. A weighted Kurtosis index is used to obtain the most sensitive mode, which is used to compute sudden change in discrete Teager energy (DTE), indicating the occurrence of the fault. A stacked autoencoder-based neural network is applied for classifying the pole to ground (PG), pole to pole (PP), line to ground (LG), line to line (LL) and three-phase line to ground (LLLG) faults. The effectiveness of the proposed protection technique is validated in MATLAB/SIMULINK by considering different test cases. Findings As the maximum fault detection time is only 5 ms, the proposed detection technique is very fast. A stacked autoencoder-based neural network is applied for classifying the PG, PP, LG, LL and LLLG faults with classification accuracy of 99.1%. Originality/value The proposed technique provides a very fast, reliable and accurate protection scheme for DC microgrid system.

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Section: Results Analysismentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Fault detection and classification of DC microgrid based on VMD

Barik

2022

COMPEL

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“…DFA technique is useful to detect the long‐range correlation of non‐stationary signals in noisy conditions by eliminating the fluctuation in those signals. This is also applicable for weather records, DNA study, geology, solid‐state physics, financial time series, ethnology and so on 15‐17,21 …”

Section: Introductionmentioning

confidence: 99%

Islanding detection in different configurations of multiple photovoltaic distributed generation‐based direct current microgrid using improved mode decomposition technique

Prasad

Dash

2021

Int Trans Electr Energ Syst

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Summary A novel approach is presented in this paper for the detection of islanding disturbances in a multiple photovoltaic distribution generation‐based radial and mesh types DC microgrid using adaptive variational mode decomposition (AVMD) hybridized with detrended fluctuation analysis (DFA). Here, the current signals from the DC bus are extracted and processed through AVMD to yield a set of intrinsic mode functions (IMFs). The optimal selection of VMD parameters like the number of modes K and penalty factor (γ) is achieved using improved particle swarm optimization sine cosine levy flight algorithm that considers minimum mean envelope entropy as its objective function. Further from the set of optimal modes, the most significant IMFs are selected using an effective weighted kurtosis index based on a chosen threshold value. For the classification of islanding and non‐islanding events, DFA takes the significant IMFs as its inputs to yield three scaling exponents (α) values according to the window size setup. The α values play a key role in distinguishing various islanding and non‐islanding events occurring on the DC microgrid under various topological changes in two‐dimensional and three‐dimensional scatter plots. The efficacy and superiority of the proposed system are validated by classification accuracy and relative computational time in comparison to the existing methods. The entire study is carried out in MATLAB/Simulink platform.

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“…The direct connection of DC supply, loads, and storage equipment with the common bus reduces the conversion stages and inefficiency of power delivery chain, 4 with easier interfacing and reduced cost. Also, DC microgrids possess several advantages over AC microgrid related to higher power transfer capability, the requirement of only two leads, cost‐effectiveness, reduced cable losses, and elimination of synchronization problems 5‐7 . A DC microgrid can have AC sources or loads with suitable power conversion using power electronic converters.…”

Section: Introductionmentioning

confidence: 99%

“…Also, DC microgrids possess several advantages over AC microgrid related to higher power transfer capability, the requirement of only two leads, cost-effectiveness, reduced cable losses, and elimination of synchronization problems. [5][6][7] A DC microgrid can have AC sources or loads with suitable power conversion using power electronic converters.…”

Section: Introductionmentioning

confidence: 99%

Enhancing robustness of DC microgrid protection during weather intermittency and source outage for improved resilience and system integrity

Tiwari

Koley

Manohar

et al. 2021

Int Trans Electr Energ Syst

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Despite the advantages pertaining to efficiency, stability, and ease of integration with renewable energy sources, the wider acceptance of DC microgrids has been hindered by the complexity of the protection task. In addition to the absence of zero crossings, the weather-dependent fluctuations in the operation of renewable sources and the high probability of distributed energy resources (DERs) outage further complicate the fault detection and classification task. This paper proposes a reliable DC microgrid protection approach with

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Fault analysis in photovoltaic generation based DC microgrid using multifractal detrended fluctuation analysis

Cited by 11 publications

References 30 publications

Fault detection and classification of DC microgrid based on VMD

Fault detection and classification of DC microgrid based on VMD

Islanding detection in different configurations of multiple photovoltaic distributed generation‐based direct current microgrid using improved mode decomposition technique

Enhancing robustness of DC microgrid protection during weather intermittency and source outage for improved resilience and system integrity

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