2014 IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific) 2014
DOI: 10.1109/itec-ap.2014.6940964
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Abstract: Voltage sag is a typical power quality disturbance. Identify the type of disturbance source causing voltage sag accurately is one of the important matters in power quality monitoring and management. Due to the correlativity and redundancy of the features, the identification method for voltage sag disturbance source is low accuracy. To resolve the problem, this paper proposes a method of feature reduction of voltage sag disturbance based on principal component analysis (PCA). Through the analysis of single dist… Show more

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Cited by 8 publications
(4 citation statements)
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References 6 publications
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“…By comparing the usage frequency of U 、 R 、 H and considering the effect of classification, according to Table 1, the judgment matrix A can be got as (4) shows. 1 2 2 1 / 2 1 2 1 / 2 1 / 2 2      A (4) Where, the rank of the indexes is U, R, and H. Then eigenvalues of A were calculated and the weight vector was got as w={0.4934 0.3108 0.1958}. The consistency of it satisfied the requirement: CR=CI/0.52=0.0515<0.1.…”
Section: Classification Of Sag Sourcesmentioning
confidence: 99%
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“…By comparing the usage frequency of U 、 R 、 H and considering the effect of classification, according to Table 1, the judgment matrix A can be got as (4) shows. 1 2 2 1 / 2 1 2 1 / 2 1 / 2 2      A (4) Where, the rank of the indexes is U, R, and H. Then eigenvalues of A were calculated and the weight vector was got as w={0.4934 0.3108 0.1958}. The consistency of it satisfied the requirement: CR=CI/0.52=0.0515<0.1.…”
Section: Classification Of Sag Sourcesmentioning
confidence: 99%
“…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%
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“…Pattern recognition uses a classification algorithm to design a classifier for determining the voltage sag causes of the disturbance signals. Common methods include neural network [11][12][13], support vector machine [14], principal component analysis reduction [15], fuzzy comprehensive evaluation [16], label propagation [17], etc. Although these methods have obtained good performance, they require the setting of several threshold levels, human expert knowledge or extracting the fundamental frequency component, which will result in an incomplete description of the data.…”
Section: Introductionmentioning
confidence: 99%
“…[3]- [4]), detection and identification (e.g. [5]-[8]) of voltage sags. In [3], the voltage sag events obtained from surveys in medium and low voltage networks were classified.…”
Section: Introductionmentioning
confidence: 99%