Islanding and power quality disturbance monitoring in microgrid using adaptive cross variational mode decomposition and reduced kernel ridge regression

Dash, P.K.; Satapathy, Prachitara; Nayak, Pravati

doi:10.1002/2050-7038.12364

Cited by 9 publications

(3 citation statements)

References 36 publications

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“…To evaluate the performance of the proposed algorithm presented in this paper, it is compared with recently published articles in [3,7,9,13,17,18,20,23,[49][50][51][52]. e comparison results are shown in Table 6.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

“…Classification results of nine types of PQDs with no noise and with 20 dB noise are obtained as 99.70% and 99.10%, respectively. In [50], the authors present the detection and classification of islanding and PQDs for multiple distributed generation systems using an adaptive cross variational mode decomposition (XVMD) with reduced kernel ridge regression (RKRR). e classification rate of twelve types of PQDs is achieved as 99.2%.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

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Classification of Power Quality Disturbances in Solar PV Integrated Power System Based on a Hybrid Deep Learning Approach

Eristi

Eri̇şti̇

2022

International Transactions on Electrical Energy Systems

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Nowadays, due to the increase in the demand for electrical energy and the development of technology, the electrical devices have a more complex structure. This situation has increased the importance of concept of the power quality in the electrical power system. This paper presents a deep learning-based system to recognize the power quality disturbances (PQDs) in the solar photovoltaic (SPV) plant integrated with power system networks. The PQDs are analyzed using continuous wavelet transform (CWT) and image files are obtained from scalograms of CWT. Then, these image files are used to recognize PQDs with the help of a hybrid deep learning approach based on convolutional neural network (CNN), neighbor component analysis (NCA), and support vector machine (SVM). In this hybrid deep learning approach, the image files are given as input to AlexNet and GoogLeNet. The NCA is applied to the features obtained from the last dropout layer of each architecture. The distinctive features obtained from the NCA process are classified using the SVM algorithm. In order to evaluate the proposed approach, PQD data are obtained from a modified IEEE 13-bus test system including the SPV system. Several analyses and comparisons are carried out to verify the success of the proposed approach. It has been found that the proposed hybrid deep learning approach has the ability to accurately recognize the PQDs even if the SPV plant integrated power system has a negative effect on power quality.

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Section: Comparison With Other Methodsmentioning

confidence: 99%

Section: Comparison With Other Methodsmentioning

confidence: 99%

Classification of Power Quality Disturbances in Solar PV Integrated Power System Based on a Hybrid Deep Learning Approach

Eristi

Eri̇şti̇

2022

International Transactions on Electrical Energy Systems

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show abstract

“…The modes are of limited frequency bandwidth and are sparse and compact around a centre pulsation. 28 The theoretical progression of VMD can essentially be put in the form of the following steps:…”

Section: Basic Theory Of Vmdmentioning

confidence: 99%

Variational mode decomposition ‐ subspace‐K‐nearest neighbour based islanding detection in distributed generation system

Patnaik

Mishra

Jena

2021

Int Trans Electr Energ Syst

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A novel extraction method for useful component of vibration signal combining variational mode decomposition and relative entropy

Zhu

Wang

et al. 2021

AIP Advances

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In condition monitoring and prognostics health management, it is very important to extract the useful components of equipment state signals. In this paper, combining variational mode decomposition (VMD) and relative entropy (RE), a novel approach is proposed for extracting signal useful components. By using VMD, the original vibration signal can be adaptively decomposed, and its effective constituents can be acquired through the assessment of RE. The proposed method is further applied into some simulated and measured signals of a hydraulic axial piston pump. The effectiveness and feasibility of the proposed method are demonstrated through the numerical and tested vibration signals. The results show that the proposed method possesses laudable capability to extract the effective component of vibration signals for a hydraulic axial piston pump under normal state, slipper wear, and slipper luxation. The interference of background noise is effectively overcome. Furthermore, the expected useful signals are precisely reconstituted.

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Islanding and power quality disturbance monitoring in microgrid using adaptive cross variational mode decomposition and reduced kernel ridge regression

Cited by 9 publications

References 36 publications

Classification of Power Quality Disturbances in Solar PV Integrated Power System Based on a Hybrid Deep Learning Approach

Classification of Power Quality Disturbances in Solar PV Integrated Power System Based on a Hybrid Deep Learning Approach

Variational mode decomposition ‐ subspace‐K‐nearest neighbour based islanding detection in distributed generation system

A novel extraction method for useful component of vibration signal combining variational mode decomposition and relative entropy

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