Teager-Huang based Fault Detection in Inverter-interfaced AC Microgrid

Sahoo, Animesh; Arunan, Anusuya; Mahmud, Khizir; Ravishankar, Jayashri; Nizami, Mohammad Sohrab Hasan; Hossain, M. J.

doi:10.1109/eeeic.2019.8783453

Cited by 8 publications

(5 citation statements)

References 18 publications

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“…The existing feature extraction methods for microgrid protection are classified into time domain, frequency domain and time-frequency approach. A few important methods of feature extraction for microgrid protection are DWT and Hilbert Huang transform (HHT; Manohar et al, 2017; COMPEL 42,2 Sahoo et al, 2019). The problem associated with the moving window function and computational complexity is the major drawback of the DWT method.…”

Section: Classificationmentioning

confidence: 99%

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: Classificationmentioning

confidence: 99%

Fault detection and classification of DC microgrid based on VMD

Barik

2022

COMPEL

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“…Hilbert-Huang transform hybrid with TEO (Teager energy operator) is proposed for faster detection of a fault in inverter-based AC microgrid. 140 The efficiency of Teager-Huang based technique for fault location and classification can be scrutinized. The reachability and diversity of solutions provided by HHT based method to tackle the challenges are tabulated in Table 14.…”

Section: Hilbert-huang Transform (Hht)based Protection Schemementioning

confidence: 99%

“…Quicker fault detection can limit the damage to the microgrid. Hilbert‐Huang transform hybrid with TEO (Teager energy operator) is proposed for faster detection of a fault in inverter‐based AC microgrid 140 . The efficiency of Teager‐Huang based technique for fault location and classification can be scrutinized.…”

Section: Other Miscellaneous Schemesmentioning

confidence: 99%

Review of distributed generator integratedACmicrogrid protection: issues, strategies, and future trends

Sarangi

Sahu

Rout

2021

Intl J of Energy Research

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In the recent power system scenario, the concept of microgrid is evolving rapidly. The architecture should be robust enough to cater the complexity of integration of distributed generation sources, demand-side management, and storage. The fast growth of embedded generation with other advanced power electronic interfaces and components along with energy storage devices change the operating pattern of the integrated power system scenario. In this context, protection is the vital aspect for qualitative uninterrupted power and making the system resilient to adverse, and faulty conditions. Many associated issues, particularly related to AC microgrid systems like the large difference in the short-circuit level between integrated and isolated mode of operation, bidirectional power flow, unsynchronized reclosing, the blindness of protection, lack of natural zero-crossing current, and false tripping are needed to be focused. In light of the above factors, it motivates us to survey the previous research works on the existing protection strategies to redesign and present the critical analysis of the predicted protection issues of microgrid protection with upcoming advanced technology and methodology. This article presents a critical review of the issues and protection schemes for the DG integrated AC microgrid extensively. Here, the merits, and demerits of each protection scheme, and visible possibilities for any advancement in protection strategies to enhance the reliability, selectivity, and security of AC microgrid is emphasized keeping in mind the different connections and configurations. In addition, this work also focuses on integrating the intelligent approaches/devices that help to elevate the current protection schemes to be smarter and efficient.

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“…Its specialty of identifying faults in a signal by using only very few samples of IF and IA makes it different from other traditional techniques. Because of easy calculation, effective demodulation and avoidance of negative frequency, TEO enhances the accuracy and transient impact composition 22‐24 . The purpose of using the proposed combinational approach is that CuSum will detect the faulty signal with low impedance while TEO will detect both low and high impedance faulty signals.…”

Section: Introductionmentioning

confidence: 99%

“…Computational intelligence methods such as decision tree (DT), 8 support vector machine (SVM) 25 and fuzzy expert system 26 have been considered for detecting and classifying the faults in a power system networks utilizing the relevant features extracted using the well‐known signal processing techniques like DWT, S‐Transform, Empirical Mode Decomposition (EMD) and Hilbert Huang Transform, 1,7,24 etc. The drawbacks of these feature extraction methods are dimensionality reduction, loss of information and mode mixing.…”

Section: Introductionmentioning

confidence: 99%