Power quality data analysis: from raw data to knowledge using knowledge discovery approach

Santoso, Surya; Lamoree, J.

doi:10.1109/pess.2000.867593

Cited by 16 publications

(12 citation statements)

References 1 publication

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“…The information is generated at several locations in local utility's service territory, asynchronously during long periods of time. This information is continuously monitored and registered, causing a great volume of data [2]. Consequently, the amount of data is increasing daily, due to the advances in the data storage technologies too.…”

Section: B the Problemmentioning

confidence: 99%

A survey on voltage dip events in power systems

Barrera-Núñez¹,

Meléndez-Frigola²,

Herraiz-Jaramillo³

2008

REPQJ

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A general view of voltage dip analysis is presented in this paper. The objectives to perform voltage dip analysis, activities inside of this analysis and methodological aspects are described from a data mining perspective. Basic data mining principles were taken as the basis to identify similar steps in power quality works involving classification and knowledge discovery tasks. This paper is centred in voltage dip event diagnosis in order to reduce the study and focus the analysis.

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Section: B the Problemmentioning

confidence: 99%

A survey on voltage dip events in power systems

Barrera-Núñez¹,

Meléndez-Frigola²,

Herraiz-Jaramillo³

2008

REPQJ

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“…The electric power industry has currently a reasonably sophisticated logistics to acquire and store time series (waveforms) corresponding to power quality (PQ) [1,2] events. A typical example is the oscillography equipments [3] that store waveforms along with additional information such as date and time, in cases where the amplitude differs from its nominal value.…”

Section: Introductionmentioning

confidence: 99%

An Experimental Evaluation Of Automatic Classification Of Sequences Representing Short Circuits In Transmission Lines

Morais

Pires

Cardoso

et al. 2016

Anais Do 8. Congresso Brasileiro De Redes Neurais

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This work concerns automatic classification of short circuits in transmission lines. These faults are responsible for the majority of the disturbances and cascading blackouts. Each short circuit is represented by a sequence (time-series) and both online (for each short segment) and offline (taking in account the whole sequence) classification are investigated. Results with different preprocessing (e.g., wavelets) and learning algorithms are presented, which indicate that decision trees and neural networks outperform the other methods. Another contribution of this work is to promote the adoption of a public and comprehensive labeled dataset with short circuit sequences, which allows to properly compare the algorithms and reproduce the results.

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“…Electric parameters are permanently monitored and registered, producing huge volume of data. The end-user has a large amount of data that can be used to assess and reduce the power quality problems [2] [5]. Hence, statistical techniques have to be exploited to take advantage of information contained in those registers.…”

Section: Introductionmentioning

confidence: 99%

Unusual voltage sag event detection in power systems

Núñez

Meléndez

Herraiz

et al. 2008

2008 IEEE/PES Transmission and Distribution Conference and Exposition: Latin America

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Abstract-This paper is organized in twelve sections. In the second section the features extracted from sag waveforms are described. Next, we present the registers of voltage sags. In the fourth to sixth sections, PCA, FA and DA are briefly explained. Later, in the seventh to ninth sections, the results of each technique are shown. Finally, comparison results, conclusion and future works are discussed. II. FEATURES OF VOLTAGE SAGSWe have extracted, from the sags waveforms, a set of features useful to describe duration, magnitude and unbalance degree of sags. They are summarized in Table I This is a new feature we propose in this paper. Gain of zero sequence voltage corresponds as the integral of the RMS zero sequence voltage during the event. It has been computed using the equation 2:(2) 4) I ratio : Ratio between the fundamental frequency component of the currents during and before of voltage sag event. It was previously defined in [6]. 5) T:Voltage sag event duration measured in samples. 6) N seg :Nseg/2 corresponds to number of sag transitory states.Duration, magnitude and unbalanced grade were selected as sag characteristics in order to determine this unusualness. III. DESCRIPTION OF VOLTAGE SAGS:The study has been carried out with 69 registers of sags. All the registers contain 40 cycles, sampled at 128 samples per cycle (50Hz).

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Power quality data analysis: from raw data to knowledge using knowledge discovery approach

Cited by 16 publications

References 1 publication

A survey on voltage dip events in power systems

A survey on voltage dip events in power systems

An Experimental Evaluation Of Automatic Classification Of Sequences Representing Short Circuits In Transmission Lines

Unusual voltage sag event detection in power systems

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