An expert system based on S-transform and neural network for automatic classification of power quality disturbances

Uyar, Murat; Yildirim, Selcuk; Gençoğlu, Muhsin Tunay

doi:10.1016/j.eswa.2008.07.030

Cited by 180 publications

(80 citation statements)

References 20 publications

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“…A total of 56 distinctive features are extracted from the feature extractor (24 features for 3-line currents, 24 features for 3-line voltages, and 8 features for zero sequence current). In the ST, feature extraction is carried out by applying standard statistical techniques to the components of the STA matrix as well as directly on the STA matrix contours [21]. These features are useful for detection and classification of relevant parameters of the fault signals.…”

Section: Discrete Fourier Transform Of H(t)mentioning

confidence: 99%

“…WTs have some drawbacks such as selection of the mother wavelet, sensitivity to noise, lack of absolute referenced phase information, production of unsuitable time-scale plots for intuitive visual analysis, and delay due to its batch processing [21][22][23]. In [2] and [8], combined techniques such as WT-fuzzy logic and WT-SVM were also proposed for protection of SCTLs, respectively.…”

Section: Introductionmentioning

confidence: 99%

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A combined protective scheme for fault classification and identification of faulty section in series compensated transmission lines

Çöteli¹

2013

Turk J Elec Eng & Comp Sci

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Abstract:The fault detection process is very difficult in transmission lines with a fixed series capacitor because of the nonlinear behavior of protection device and series-parallel resonance. This paper proposes a new method based on S-transform (ST) and support vector machines (SVMs) for fault classification and identification of a faulty section in a transmission line with a fixed series capacitor placed at the middle of the line. In the proposed method, the fault detection process is carried out by using distinctive features of 3-line signals (line voltages and currents) and zero sequence current. The relevant features of these signals are obtained by using the ST. The obtained features are then used as input to multiple SVM classifiers and their outputs are combined for classifying the fault type and identifying the faulty section. Training and testing samples for the proposed method have been generated with different types of short-circuit faults and different combinations of system parameters in the MATLAB environment. The performance of the proposed method is investigated according to the accuracy of fault classification and faulty section identification. To evaluate the validity of this study, the proposed method is also compared to both ST-neural network and previous studies. The proposed method not only provides a good classification performance for all types of faults, but also detects the faulty section at a high accuracy.

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Section: Discrete Fourier Transform Of H(t)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A combined protective scheme for fault classification and identification of faulty section in series compensated transmission lines

Çöteli¹

2013

Turk J Elec Eng & Comp Sci

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“…Table 1 provides a detailed summary of all disturbances of patterns with the equations and controlling parameters [24,25]. A total of 100 cases of each pattern with different parameters were generated and utilized by MATLAB 7.10.0.…”

Section: Feature Extractionmentioning

confidence: 99%

Recognition and Classification of Power Quality Disturbances on the basis of Pattern Linguistic Values

Liu¹,

Liu²,

Xu³

2016

Journal of Electrical Engineering and Technology

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-This paper presents a new recognition and classification method for power quality (PQ) disturbances on the basis of pattern linguistic values. This method solves the difficulty of recognizing disturbances rapidly and accurately by using fuzzy logic. This method uses classification disturbance patterns to define the linguistic values of fuzzy input variables and used the input variables of corresponding disturbance pattern to set membership functions. This method also sets the fuzzy rules by analyzing the distribution regularities of the input variable values. One characteristic of this method is that the linguistic values of fuzzy input variables and the setting of membership functions are not only related to the input variables but also to the character of classification disturbance and the classification results. Furthermore, the number of fuzzy rules is equal to the number of disturbance patterns. By using this method for disturbance classification, the membership function and design of fuzzy rules are directly related to the objective of classification, thus effectively reducing the complexity of the design process and yielding accurate classification results. The classification results of the simulation and measured data verify the feasibility and effectiveness of this method.

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“…Many studies on the use of WT in signal analysis have shown their ability to filter noise and for accurate detection of abrupt changes and discontinuities in electrical signals, as well as feature extraction [5]. This has led to its application in PQ disturbances identification.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, to ensure a better PQ of a distribution power system, methods for automatic detection, classification, location and data storage became essential [1], [5]. In this sense, continuous data recording (with or without disturbances) are required, which leads to a huge volume of data to be inspected by experts [2], [3].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Threshold for Electrical Disturbances Segmentation

Andrade

Oleskovicz

Fernandes

et al. 2024

RE&PQJ

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Abstract. The detection of the electrical signal discontinuities in the oscillographies recorded in substations and/or points of common coupling allows their segmentation, which is crucial for implementation of automated methods for detection, classification, location and storage by classes of disturbances in electric power systems. In this context, this study provides a way of determining an adaptive threshold that allows the segmentation of voltage or current signals based on Wavelets decomposition. The disturbances considered in this work were the short-duration voltage variations, impulsive and oscillatory transients, and harmonic distortions. The signals were synthetically generated. Moreover, white noise was added on the signals. Thus, a Symlet Wavelet was applied to the signals in order to denoise them. In the sequence, a Daubechies Wavelet was used to decompose the filtered signals. So, to determine the initial and final points of each segment, an adaptive threshold was established based on the energy and entropy of energy for the second level of decomposition. Thus, the number and position of each segment were determined according to the intersections of the detail curves and the thresholds found.

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An expert system based on S-transform and neural network for automatic classification of power quality disturbances

Cited by 180 publications

References 20 publications

A combined protective scheme for fault classification and identification of faulty section in series compensated transmission lines

A combined protective scheme for fault classification and identification of faulty section in series compensated transmission lines

Recognition and Classification of Power Quality Disturbances on the basis of Pattern Linguistic Values

Adaptive Threshold for Electrical Disturbances Segmentation

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