A Robust Transform-Domain Deep Convolutional Network for Voltage Dip Classification

Bagheri, Azam; Gu, Irene Yu‐Hua; Bollen, M.H.J.; Balouji, Ebrahim

doi:10.1109/tpwrd.2018.2854677

Cited by 85 publications

(61 citation statements)

References 40 publications

(55 reference statements)

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“…where x t is the input vector; h t and h t-1 are the state vectors at time t and (t − 1), respectively; and f w is a nonlinear activation function, where w is the weight parameters. A mathematical expression of the unrolling mentioned is given in (3) from [17,18].…”

Section: Bi-lstm Network Layer 321 Lstm Formula Derivation Lstm Comentioning

confidence: 99%

“…Recently, only a small number of works have been published on deep learning methods of the recognition of voltage sag causes [16,17]. Reference [16] proposes a new method for the recognition of voltage sag causes based on Long Short-Term Memory (LSTM), and Ref.…”

Section: Introductionmentioning

confidence: 99%

“…Reference [16] proposes a new method for the recognition of voltage sag causes based on Long Short-Term Memory (LSTM), and Ref. [17] shows that Convolutional Neural Network (CNN) is a suitable tool for recognizing voltage sag causes. They are all useful attempts of deep learning in recognizing the causes of voltage sags and have achieved some success.…”

Section: Introductionmentioning

confidence: 99%

“…For Ref. [17], and how to perform two-dimensional transformation of the original signal without information loss is worth studying.…”

Section: Introductionmentioning

confidence: 99%

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Recognition method of voltage sag causes based on Bi‐LSTM

Zheng

Wang

et al. 2019

IEEJ Transactions Elec Engng

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In recent years, the power quality problem has become more complicated in power grids because of the extensive usage of power electronics and multisource multitransformation features. The method, based on physical characteristics such as time domain, frequency domain and transform domain, is facing challenges in terms of adaptability, algorithm efficiency and accuracy for the recognition of complex disturbance recognition. The bidirectional long short-term memory network is an algorithm in deep learning. It is based on data for characterization learning, which can effectively overcome the problem of information loss and generalization ability of physical methods. Moreover, it has the characteristics of memory, which can simultaneously consider historical information and future information and can better learn data features with time series characteristics. Aiming at the transient voltage sag time series data, this paper proposes a recognition method of the voltage sag causes based on the bidirectional long short-term memory network's extraction eigenvalue, the full-connection layer's high-dimensional feature extraction and the Softmax network layer's classification. The experiment uses simulation data and measured data to prove that the model has good recognition ability and good antinoise performance in the recognition of voltage sag causes and can be reliably applied in practical engineering.

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Section: Bi-lstm Network Layer 321 Lstm Formula Derivation Lstm Comentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…For Ref. [17], and how to perform two-dimensional transformation of the original signal without information loss is worth studying.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Recognition method of voltage sag causes based on Bi‐LSTM

Zheng

Wang

et al. 2019

IEEJ Transactions Elec Engng

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“…Therefore, powerelectronics equipment, whose firing instants are triggered by the phase-angle information, may have adverse impact [4], [13][14]. To this end, several methods are reported in various research works for classification and characterisation of voltage dips [15][16][17][18][19][20][21].In [22], fault-types and fault-locations, which trigger voltage sags and swells, are investigated by capturing fault records. However, this paper proposes an analytical approach for assessment of voltage sags caused by balanced as well as unbalanced faults.…”

Section: Introductionmentioning

confidence: 99%

Analytical expressions for characterising voltage dips and phase‐angle jumps in electricity networks

Begum

Alam

Muttaqi

2018

IET Generation, Transmission & Distribution

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Voltage dips/sags are one of the major concerns for electricity consumers as well as utility service providers. Therefore, the characterisation of voltage dips/sags is required. This study presents a set of mathematical expressions for characterising different types of voltage dips/sags and their associated phase-angle jumps, which are typically found due to faults and/or disturbances in electricity networks. The expressions are derived analytically from the model of the power network containing generators, transmission and/or distribution lines, transformers etc. Four types of voltage dips, namely, A, B, E, and G, which are associated with four major types of faults including balanced three-phase faults, single line-toground, double lineto-ground, and line-to-line faults, are considered to derive the analytical expressions. Dynamic simulation results, using a test distribution system, approve the validity as well as the accuracy of the developed expressions. The influence of fault-types and fault-locations is investigated from the mathematical expressions; further, validation is conducted through a simulation study. The analytical expressions, presented in this study, are a valuable tool in the planning stage since the expressions can be employed to characterise during-fault voltage dips at different buses in electricity network without conducting a large number of repeated dynamic simulations.

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Power Quality

Bollen¹,

Oliveira²

2023

Wiley Encyclopedia of Electrical and Electronics Engineering

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This item starts with a general discussion on power quality, including definitions, terminology, and requirements. It further contains sections on the importance of power quality (which in the end all comes down to reducing the likelihood of interference, i.e., equipment not functioning as it should); some of the subjects that have been historically on the agenda within power quality (going back to harmonics and light flicker, which were on the agenda before the term power quality in its modern meaning was used, voltage dips, and several waves of interest for waveform distortion); the main aspects of power quality monitoring (different for variations and events); the most important power quality disturbances (voltage magnitude variations, fast voltage variations, voltage dips, voltage swells, interruptions, harmonics, interharmonics, supraharmonics, and transients) and an overview of power quality regulation (discussed around the regulatory dilemma).

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A Robust Transform-Domain Deep Convolutional Network for Voltage Dip Classification

Cited by 85 publications

References 40 publications

Recognition method of voltage sag causes based on Bi‐LSTM

Recognition method of voltage sag causes based on Bi‐LSTM

Analytical expressions for characterising voltage dips and phase‐angle jumps in electricity networks

Power Quality

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