Method for voltage-sag-source detection by investigating slope of the system trajectory

Li, C.; Tayjasanant, Thavatchai; Xu, Wilsun; Liu, X.

doi:10.1049/ip-gtd:20030214

Cited by 95 publications

(67 citation statements)

References 2 publications

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“…The range of C and γ are C = {2 -5 , 2 -4 ,..., 2 4 , 2 5 } and γ is γ = {2 -5 , 2 -4 ,..., 2 4 , 2 5 } respectively with step size of 0.1 for both exponentials.…”

Section: Cross Validation and Testingmentioning

confidence: 99%

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Classification of Voltage Sag Using Multi-resolution Analysis and Support Vector Machine

Ismail¹,

Zakaria²,

Hamzah³

2015

JOCET

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I. INTRODUCTIONVoltage sag disturbance is one of the most frequent power quality problems which occur between a few tens and several hundred times per year [1]. Voltage sags are typically caused by fault conditions [2], in which short-circuit faults and earth faults are found to cause severe voltage sags [3]. In industrial and commercial power systems, faults on one-feeder tend to cause voltage drops on all other feeders in the plant [4]. Identifying the root of voltage sag problem has been in the fore front research area in power system. Support Vector Machine (SVM) has been used in power quality disturbance classification [5]-[8]. Reference [6] proposed a SVM classification for voltage disturbance. Data from voltage disturbances for faults and transformer energizing are used and the triggering point of disturbance, frequency magnitude and the total harmonic distortion (THD) are used as the input for the SVM. The faults in each case have been grouped and testing has been carried out separately to verify the performance of this method [6]. General classification of power quality disturbance has been proposed in [6]-[8] [11] are promising but the voltage sags data are divided into particular groups. This paper presents the classifying of the causes of voltage sag using Multi Resolution Analysis (MRA) and Support Vector Machines (SVM). The wavelet transformation will be utilized as feature extraction based on the MRA coefficient and the SVM will be used to classify the cause of the voltage sag. This method use randomly selected voltage sag data and is not grouped as in [6], [11]. Synthetic data based on two established standard IEEE test systems i.e., the IEEE 30 bus systems and IEEE 34 node distribution system are used to justify the method. II. MULTI-RESOLUTION ANALYSISIn this research MRA is used to develop the representations of the voltage sag signal at various levels of resolutions. The signal will be filtered at each level by employing low pass filter (LPF) and high pass filter (HPF). The signal is denoted by the 0 [] Cn , where n is an integer is distributed in three levels. The high pass filter is denoted by 0 G while the low pass filter is denoted by 0 H as in Fig. 1. Upon filtering, the signal is decomposed starting from level 1 onwards. The decomposition coefficients of MRA analysis which correspond to the decomposition of signal ) (t x is expressed as,In this paper, the voltage sag signals were transformed into six different resolution levels and the decomposition detail level d1 has been chosen as it gives the best accurate beginning time for voltage sag. Fig. 2 shows the original waveform is decomposed into approximate a and detail coefficients from d1 to d6. The first type of features extracted from the detail level 1 (d1) is the minimum and the maximum value of voltage. The second features is the energy extracted from those levels. Fig. 3(a). voltage sag caused by fault. Fig. 3(b). Voltage sag caused by starting of induction motor. Fig. 3(a) and Fig. 3(b) shows the feature extraction of the ...

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“…The range of C and γ are C = {2 -5 , 2 -4 ,..., 2 4 , 2 5 } and γ is γ = {2 -5 , 2 -4 ,..., 2 4 , 2 5 } respectively with step size of 0.1 for both exponentials.…”

Section: Cross Validation and Testingmentioning

confidence: 99%

“…Identifying the root of voltage sag problem has been in the fore front research area in power system. Support Vector Machine (SVM) has been used in power quality disturbance classification [5]- [8]. Reference [6] proposed a SVM classification for voltage disturbance.…”

Section: Introductionmentioning

confidence: 99%

Classification of Voltage Sag Using Multi-resolution Analysis and Support Vector Machine

Ismail¹,

Zakaria²,

Hamzah³

2015

JOCET

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“…The method proposed in [5] (method I) is based on the assumption, that the energy flow at the monitoring point increases during downstream events and decreases during upstream events. The methods proposed in [6] and [7] (methods II and III) are both based on the assumption that currents measured at the monitoring point increase during downstream events and decrease during upstream events. The slope of a current-voltage trajectory is investigated in method II, while a real current component is observed within method III.…”

Section: Introductionmentioning

confidence: 99%

Impact of Asymmetrical Disturbance Events on Voltage Sag Source Detection

Polajžer

Štumberger²,

Seme³

et al. 2024

RE&PQJ

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Abstract. Methods for voltage sag source detection based either on energy or current criteria are discussed in this work. They are tested by applying extensive simulations and field tests. The obtained results show severe impact of asymmetrical disturbance events on voltage sag source detection, particularly in cases of upstream ground faults. However, all discussed methods show very high effectiveness in cases of voltage sags due to heavy motor starting and other symmetrical voltage sags. Based on the performed evaluation it can be concluded, that further development is still needed to increase the degree of confidence in the discussed methods for voltage sag source detection.

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“…There are some studies on voltage sag source location at home and abroad [1][2][3][4][5][6][7][8] : It uses disturbance power and disturbance energy to determine the sag source relative to monitoring points in [1][2][3];In [4], it judged the location from impedance and phase angle change before and after sag ,which seen from the relay installed in lines. The limitations of this method lies in relay direction errors affected by location of faults and other factors; In [5] and [6], It determined the location according to positive and negative condition of the real and imaginary parts of equivalent impedance before and after sag.…”

Section: Introductionmentioning

confidence: 99%

“…The difficulty is that the choice of measurement period before and after sag may affect the outcome's correctness. The method of fitting a straight line to the data of |Ucosθ| and I during sag is used in [7],and the slope of line is the basis of location. But the accuracy is not satisfactory when judging asymmetric fault [8].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Voltage Sag Source Location Based on Wavelet-Multiresolution Method

Xue-meng

2010

2010 Asia-Pacific Power and Energy Engineering Conference

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Abstract-The method of voltage sag source location proposed in this paper is using wavelet-multiresolution to decompose the voltage and current waveforms from monitoring points and analyzing the change of signal energy of three-phase instantaneous power during voltage sag to ascertain the sag source location relative to the monitoring points. The location of voltage sag caused by line fault, transformer energizing and induction motor starting, which are the main reason of sag, are analyzed and tested using this method based on an PSCAD simulation model in this paper. The results show that the method is simple and reliable and can be used to determine sag source location.

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Method for voltage-sag-source detection by investigating slope of the system trajectory

Cited by 95 publications

References 2 publications

Classification of Voltage Sag Using Multi-resolution Analysis and Support Vector Machine

Classification of Voltage Sag Using Multi-resolution Analysis and Support Vector Machine

Impact of Asymmetrical Disturbance Events on Voltage Sag Source Detection

Analysis of Voltage Sag Source Location Based on Wavelet-Multiresolution Method

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