Identification of Ferroresonance based on wavelet transform and artificial neural network

Mokryani, Geev; Haghifam, Mahmoud‐Reza; Esmaeilpoor, J.

doi:10.1002/etep.234

Cited by 13 publications

(7 citation statements)

References 15 publications

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“…Some solutions for the detection of ferroresonance oscillations can be found in [8][9][10][11][12][13]. They are mostly based on analysis of frequency components of voltage in open delta VT connection or phase currents and voltages.…”

Section: Introductionmentioning

confidence: 99%

“…They are mostly based on analysis of frequency components of voltage in open delta VT connection or phase currents and voltages. In the solutions presented in [8][9][10][11][12][13], the Wavelet analysis for various wavelet forms and S-transforms are mostly used. Additionally, sophisticated decision-making algorithms, e.g., artificial neural network (ANN), kernel principal component analysis, and support vector machine (SVM), are adopted and high sampling frequencies (at least 10 kHz) are used.…”

Section: Introductionmentioning

confidence: 99%

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Detection of Ferroresonance Oscillations in Medium Voltage Networks

2020

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This paper presents a new algorithm for the detection of ferroresonance oscillations in medium voltage (MV) power systems. The proposed method is based on an analysis of estimated flux resulting from the voltage in open delta voltage transformer (VT) connection. The testing results proved that the new solution is capable of identifying quickly and reliably ferroresonance oscillations and it does not maloperate during faults. In addition, this method is simple for hardware implementation. The developed solution has been thoroughly tested on simulation runs (from the MATLAB2019b/Simulink program), as well as recorded signals.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection of Ferroresonance Oscillations in Medium Voltage Networks

2020

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“…Angrisani et al [4] used discrete wavelet transform as a time-frequency analysis tool to obtain time-frequency characteristics of waveforms in different frequency bands. Mokryani et al [5] evaluated the characteristic expression ability of various Daubechies series wavelet in overvoltage fault identification. In order to identify the overvoltage situation, Wang et al [7] adopted S-transformation to extract overvoltage characteristics and construct six different features, which are input into fuzzy expert systems and support vector machine (SVM) to identify eight types of overvoltage.…”

Section: Introductionmentioning

confidence: 99%

Internal Overvoltage Identification of Distribution Network via Time-Frequency Atomic Decomposition

et al. 2019

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Internal overvoltage accidents in the distribution network are likely to cause an equipment insulation breakdown and result in system power outages and economic losses. Therefore, an internal overvoltage identification method based on the time-frequency atomic decomposition is investigated in this study. Firstly, the overvoltage waveforms are divided into four time periods. Then, the waveforms during these four time periods are decomposed by the atomic decomposition algorithm to obtain the effective atoms from the waveforms. Moreover, the root-mean-square (RMS) value of the zero-sequence voltage, the dominant atom frequency, the total relative matching degree, and the effective atom frequency are extracted as major features. In addition, the layered identification of the overvoltage types can be realized by combining the corresponding identification criteria. The salient advantage is the features with low dimension and a high degree of discrimination. Various overvoltage types can be identified just by the corresponding thresholds, and it is easier to deploy in the field than conventional methods based on classifier training. The effectiveness of the proposed method is verified by experimental results, and it concludes that the proposed algorithm has high accuracy and strong adaptability.

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“…A lot of research has been done on the identification and classification of the ferroresonance [3,4], ground faults [5] and other kinds of over-voltages [6], but there are still a lot of research challenges that need to be resolved. In some cases, if the grounding resistance is comparatively high, the voltage features of single phase-to-ground may appear extremely similar to the fundamental ferroresonance.…”

Section: Introductionmentioning

confidence: 99%

Classification of Fundamental Ferroresonance, Single Phase-to-Ground and Wire Breakage Over-Voltages in Isolated Neutral Networks

et al. 2011

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This paper proposes a simple and effective method for classification of fundamental ferroresonance, single phase-to-ground and wire breakage over-voltages. In isolated neutral networks, power frequency over-voltages due to ferroresonance, single phase-to-ground and wire breakage may exist for a long time, and have very highly similar voltage features. If the improper suppression operation is applied due to incorrect identification, the accident is likely to worsen further. In this paper, the voltage and current features of these faults are analyzed, and a new effective classification criterion for fundamental ferroresonance and single phase-to-ground based on zero sequence current is proposed. A comprehensive identification method based on voltage, current and zero sequence current features is proposed, which is feasible and promising for real applications.

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Identification of Ferroresonance based on wavelet transform and artificial neural network

Cited by 13 publications

References 15 publications

Detection of Ferroresonance Oscillations in Medium Voltage Networks

Detection of Ferroresonance Oscillations in Medium Voltage Networks

Internal Overvoltage Identification of Distribution Network via Time-Frequency Atomic Decomposition

Classification of Fundamental Ferroresonance, Single Phase-to-Ground and Wire Breakage Over-Voltages in Isolated Neutral Networks

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