Classification of voltage sag, swell and harmonics using S-transform based modular neural network

Venkatesh, C.; Sarma, D. V. S. S. Siva; Sydulu, M.

doi:10.1109/ichqp.2010.5625388

Cited by 19 publications

(19 citation statements)

References 21 publications

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“…These SP techniques vary, subject to the characteristics of the PQDs. The most used SP techniques in PQ analysis are Fourier Transform (FT) [7]- [9], Short time Fourier transform (STFT) [10]- [14], Wavelet transform (WT) [15]- [23], S-Transform (ST) [24]- [31], etc. Although, the SP techniques are found as most suitable techniques to extract the characteristics of PQDs, but in some cases these methods give erroneous characterization.…”

Section: Signal Processing (Sp) Techniquesmentioning

confidence: 99%

“…The various AI tools which have received extensive attentions by the researchers in the area of PQ and power system analysis are as follows [4]- [6], [31], [37], [46]- [128]:…”

Section: Artificial Intelligence (Ai) Based Techniquesmentioning

confidence: 99%

“…• Artificial Neural Network (ANN) [6], [31], [37], [46]- [75] • Fuzzy Logic (FL)/Adaptive Fuzzy/Neuro-Fuzzy [76]- [111] • Expert Systems [112]- [124] • Genetic Algorithm (GA) [4], [125]- [128] The various applications, merits, and shortfalls of most recommended AI techniques used in the diagnosis of PQDs are highlighted in section below.…”

Section: Artificial Intelligence (Ai) Based Techniquesmentioning

confidence: 99%

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Signal processing and AI based diagnosis of power quality disturbances: A review

Thakur

Singh

2015

2015 International Conference on Energy Economics and Environment (ICEEE)

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The precise diagnosis of power quality disturbances (PQDs) has now become a significant concern among the utility engineers as well as consumers due to the high cost of downtimes associated with it. Numerous methods, such as, Artificial Intelligence (AI), Signal Processing (SP), Space Vector Representation, Symmetrical Component, have been evaluated for the precise diagnosis of PQDs. Among these methods, AI and SP based techniques have received extensive attention by the researchers and industry engineers. This paper discusses the various AI and SP based methodologies currently used for the diagnosis of PQDs. Existing AI and SP based methods are critically reviewed to highlight their applications, merits, and shortfalls. It is revealed that, besides the numerous applications and merits of these methodologies, none of them is found proficient for the precise diagnosis of PQDs. Accurate recognition of PQDs is still a challenging task. Therefore, the need of incorporation of new techniques for the accurate estimation of PQDs has been asserted. Keywords-Artificial intelligence, power quality, power quality disturbance, signal processing tools.I. 978-1-4673-7492-7/15/$31.00 ©2015 IEEE

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Section: Signal Processing (Sp) Techniquesmentioning

confidence: 99%

“…The various AI tools which have received extensive attentions by the researchers in the area of PQ and power system analysis are as follows [4]- [6], [31], [37], [46]- [128]:…”

Section: Artificial Intelligence (Ai) Based Techniquesmentioning

confidence: 99%

Section: Artificial Intelligence (Ai) Based Techniquesmentioning

confidence: 99%

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Signal processing and AI based diagnosis of power quality disturbances: A review

Thakur

Singh

2015

2015 International Conference on Energy Economics and Environment (ICEEE)

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“…Recently, studies on classification or recognition of voltage sag disturbance sources tend to use frequency domain transform methods (such as S transform, Hilbert-Huang transform, wavelet transform and so on) in order to extract features of voltage sags which followed by adopting different classification strategies to identify the disturbance sources. For instance, as for extraction of features for sags, S transform was applied in [2], wavelet transform in [3][4], and Hilbert-Huang transform in [5].…”

Section: Introductionmentioning

confidence: 99%

“…As for classification strategies, neural network was adopted in [2] and support vector machine (SVM) was used in [3][4]. Similarity analysis between measured signal template and standard template is an alternate.…”

Section: Introductionmentioning

confidence: 99%

Classification of voltage sag disturbance sources using fuzzy comprehensive evaluation method

Yang

et al. 2017

CIRED - Open Access Proceedings Journal

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The classification and recognition of voltage sag disturbance sources is the foundation of mitigating voltage sags. This paper analyzed characteristics of various kinds of voltage sags, including sags caused by short-circuit faults, energizing of transformers and starting of large induction motors. According to the features of different sources, an evaluation index set was formed by three-phase voltage unbalance factor, duration ratio and increments of second harmonic. After that, the weight of each element was calculated by Analytic Hierarchy Process (AHP). A novel kind of estimation criterion for fuzzy membership function parameters was proposed, inspired from 95% probability value, based on the method of undetermined coefficient to establish the functions. Consequently, according to it, fuzzy membership functions were created for each index. Further, comprehensive index membership degrees were calculated to judge the disturbance source according to the maximum of the degrees. The presented estimation criterion can efficiently reduce the influence of sample errors and thus improve the accuracy of the whole process, which was verified with the use of data obtained from both simulations and measurements.

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Detection and Classification of Voltage Disturbances in Electrical Power Systems using a Modified Euclidean ARTMAP Neural Network with Continuous Training

Barros

Tonelli‐Neto

Decanini

et al. 2015

Electric Power Components and Systems

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This article presents a method to detect and classify voltage disturbances in electric power distribution systems using a modified Euclidean ARTMAP neural network with continuous training. This decision-making tool accelerates the procedures to restore the normal operation conditions providing security, reliability, and profits to utilities. Furthermore, it allows the diagnosis system to adapt to changes from the constant evolution of the electric system. The voltage signals features or signatures are extracted using discrete wavelet transform, multiresolution analysis, and the energy concept. Results show that the proposed methodology is robust and efficient, providing a fast diagnosis process. The data set used to validate the proposal is obtained by simulations in a real distribution system using ATP software.

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Classification of voltage sag, swell and harmonics using S-transform based modular neural network

Cited by 19 publications

References 21 publications

Signal processing and AI based diagnosis of power quality disturbances: A review

Signal processing and AI based diagnosis of power quality disturbances: A review

Classification of voltage sag disturbance sources using fuzzy comprehensive evaluation method

Detection and Classification of Voltage Disturbances in Electrical Power Systems using a Modified Euclidean ARTMAP Neural Network with Continuous Training

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