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

Abstract

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 ...

Voltage Sag Causes Recognition with Fusion of Sparse Auto-Encoder and Attention Unet

“…For the first method, feature extraction algorithms include S transform [9], wavelet transform [10], fast Fourier transform [11], and Hilbert-Huang transform (HHT) [12]. Machine learning classification algorithms include clustering algorithm, support vector machine [13], principal component analysis [14], decision tree (DT), etc. Reference [9] proposed a voltage sag classification method based on the similarity of standard templates based on S transform.…”

Lifting scheme‐based matched wavelet design for effective characterisation of different types of voltage sag

“…All these events cause a momentary increase in current that results in sag at point of common coupling (PCC). For detection of causes responsible for generating VS, several signal processing algorithms have been employed in the literature, for example, independent component analysis [10], empirical mode decomposition and Hilbert transform [11], discrete wavelet transform (DWT) [12][13][14], fractionally delayed wavelet transform [15,16], S-transform [17,18], wavelet transform (WT) with spectral and statistical analysis [19] and variational mode decomposition [20]. The domain of signal processing techniques employed for spectral analysis of PQ signals is being ruled by wavelets in one or another form because wavelets can capture all information of signal, for example, trends, breakdown points, discontinuities in higher derivatives and self-similarity [21], which cannot be revealed by other signal processing techniques proposed by other research works.…”