Visualizing time-varying power quality indices using generalized empirical wavelet transform

Thirumala, Karthik; Shantanu, _; Jain, Trapti; Umarikar, Amod C.

doi:10.1016/j.epsr.2016.10.017

Cited by 47 publications

(27 citation statements)

References 28 publications

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“…It is typically combined with other optimization techniques to enhance its performance, which greatly increases the computational burden. Thirumala et al developed a methodology based on generalized empirical WT to estimate PQ indices . This approach can also be used to identify disturbances, but simultaneous disturbances may leave the filter boundary unknown, which degrades the performance of the approach overall.…”

Section: Introductionmentioning

confidence: 99%

Classification of power quality disturbances using dual strong tracking filters and rule-based extreme learning machine

Chen

Xiao

2018

Int Trans Electr Energ Syst

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SummaryThe classification of single and simultaneous power quality disturbances (PQDs) has become an issue of concern in the power system field. This paper proposes a novel approach based on dual strong tracking filters (STFs) and the rule-based extreme learning machine (ELM) for detecting and classifying single and simultaneous PQDs. Dual STFs are a hybrid structure of a low-order STF and high-order STF. The fading factor of the low-order STF is used to detect sudden changes in PQDs; the fundamental amplitude variation is tracked by the high-order STF. Six distinctive features extracted from the dual STFs serve as the input to the ELM classifier for PQD classification. The rule-based ELM technique, which is equipped with certain decision rules, can improve the ELM classification accuracy when the number of hidden nodes is insufficient. In consideration of special structures of matrices, the real-time computation of the proposed method can be realized. A PQD dataset is generated in MATLAB for simulation experiments; the results show that 20 types of PQDs, including single and simultaneous disturbances, can be accurately classified under the different levels of noise via the proposed method. The method is also tested on a real recorded waveform to verify its effectiveness in PQD classification. | INTRODUCTIONIn recent years, power quality (PQ) has become a critical issue for researchers and developers concerned with power system stability and the propagation of PQ-related effects in the grid. Poor PQ can lead to maloperations or failures of sensitive loads such as embedded systems or electronic devices, ultimately incurring substantial cost for end users. The increased use of unbalanced and nonlinear loads, line faults, switching devices, and power electric converters represents a wide variety of disturbances. Faults among lines to ground, unbalanced, or nonlinear loads can lead to amplitude (AM) variations of the fundamental frequency such as sags, swells, and interruptions. Flickers andThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Introductionmentioning

confidence: 99%

Classification of power quality disturbances using dual strong tracking filters and rule-based extreme learning machine

Chen

Xiao

2018

Int Trans Electr Energ Syst

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“…Son zamanlarda elektrik şebekesinde yenilenebilir enerji kaynaklarının, ileri hızlı kontrol donanımlarının ve karmaşık sistem bağlantılarının kullanımının artması, Güç Kalitesi (GK) bozulmalarında artışa sebep olmakta ve bu durum konut, sanayi ve akademik alanlarda kritik bir sorun olarak ortaya çıkmaktadır [1,2]. GK bozulmaları, elektrik şebekesinin ekonomik işletilmesini olumsuz etkilemektedir [3].…”

Section: Gi̇ri̇ş (Introduction)unclassified

Güç Kalitesi Bozulmalarının 2 Boyutlu Ayrık Dalgacık Dönüşümü ve Torbalama Karar Ağaçları Yöntemi ile Sınıflandırılması

Karasu¹,

Saraç²

2018

Journal of Polytechnic

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“…Moreover, the extensive use of adjustable speed drive systems, computer systems, and precision production lines has brought forward higher requirements for PQ [3][4][5]. Furthermore, with the improvement of electricity market system, diverse PQ selection and corresponding electrovalence mechanisms are required to be provided as an important service to consumers [6]. Traditional power grids have an inability to wrestle with these challenges due to the lack of intelligent PQ monitoring and analysis management platforms.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Modeling Strategy for Smart Grid Power Quality Coupling Assessment Based on Time Series Pattern Matching

Jia

Wang

et al. 2018

Mathematical Problems in Engineering

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This study introduces a data-driven modeling strategy for smart grid power quality (PQ) coupling assessment based on time series pattern matching to quantify the influence of single and integrated disturbance among nodes in different pollution patterns. Periodic and random PQ patterns are constructed by using multidimensional frequency-domain decomposition for all disturbances. A multidimensional piecewise linear representation based on local extreme points is proposed to extract the patterns features of single and integrated disturbance in consideration of disturbance variation trend and severity. A feature distance of pattern (FDP) is developed to implement pattern matching on univariate PQ time series (UPQTS) and multivariate PQ time series (MPQTS) to quantify the influence of single and integrated disturbance among nodes in the pollution patterns. Case studies on a 14-bus distribution system are performed and analyzed; the accuracy and applicability of the FDP in the smart grid PQ coupling assessment are verified by comparing with other time series pattern matching methods.

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Visualizing time-varying power quality indices using generalized empirical wavelet transform

Cited by 47 publications

References 28 publications

Classification of power quality disturbances using dual strong tracking filters and rule-based extreme learning machine

Classification of power quality disturbances using dual strong tracking filters and rule-based extreme learning machine

Güç Kalitesi Bozulmalarının 2 Boyutlu Ayrık Dalgacık Dönüşümü ve Torbalama Karar Ağaçları Yöntemi ile Sınıflandırılması

A Data-Driven Modeling Strategy for Smart Grid Power Quality Coupling Assessment Based on Time Series Pattern Matching

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