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Unbalanced load flow with hybrid wavelet transform and support vector machine based Error-Correcting Output Codes for power quality disturbances classification including wind energy
Purpose. The most common methods to designa multiclass classification consist to determine a set of binary classifiers and to combine them. In this paper support vector machine with Error-Correcting Output Codes (ECOC-SVM) classifier is proposed to classify and characterize the power qualitydisturbances such as harmonic distortion,voltage sag, and voltage swell include wind farms generator in power transmission systems. Firstly three phases unbalanced load flow analysis is executed to calculate difference electric network characteristics, levels of voltage, active and reactive power. After, discrete wavelet transform is combined with the probabilistic ECOC-SVM model to construct the classifier. Finally, the ECOC-SVM classifies and identifies the disturbance type according tothe energy deviation of the discrete wavelet transform. The proposedmethod gives satisfactory accuracy with 99.2% compared with well known methods and shows that each power quality disturbances has specific deviations from the pure sinusoidal waveform,this is good at recognizing and specifies the type of disturbance generated from the wind
power generator.ΠΠ°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½Π½ΡΠ΅ ΠΌΠ΅ΡΠΎΠ΄Ρ ΠΏΠΎΡΡΡΠΎΠ΅Π½ΠΈΡ ΠΌΡΠ»ΡΡΠΈΠΊΠ»Π°ΡΡΠΎΠ²ΠΎΠΉ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ Π·Π°ΠΊΠ»ΡΡΠ°ΡΡΡΡ Π² ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠΈ Π½Π°Π±ΠΎΡΠ° Π΄Π²ΠΎΠΈΡΠ½ΡΡ
ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΎΡΠΎΠ² ΠΈ ΠΈΡ
ΠΎΠ±ΡΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΠΈ. Π Π΄Π°Π½Π½ΠΎΠΉ ΡΡΠ°ΡΡΠ΅ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π° ΠΌΠ°ΡΠΈΠ½Π° ΠΎΠΏΠΎΡΠ½ΡΡ
Π²Π΅ΠΊΡΠΎΡΠΎΠ² Ρ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΎΡΠΎΠΌ Π²ΡΡ
ΠΎΠ΄Π½ΡΡ
ΠΊΠΎΠ΄ΠΎΠ² ΠΈΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΎΡΠΈΠ±ΠΎΠΊ(ECOC-SVM) Ρ ΡΠ΅Π»ΡΡ ΠΊΠ»Π°ΡΡΠΈΡΠΈΡΠΈΡΠΎΠ²Π°ΡΡ ΠΈ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΠ·ΠΎΠ²Π°ΡΡ ΡΠ°ΠΊΠΈΠ΅ Π½Π°ΡΡΡΠ΅Π½ΠΈΡ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½Π΅ΡΠ³ΠΈΠΈ, ΠΊΠ°ΠΊ Π³Π°ΡΠΌΠΎΠ½ΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΈΡΠΊΠ°ΠΆΠ΅Π½ΠΈΡ, ΠΏΠ°Π΄Π΅Π½ΠΈΠ΅ Π½Π°ΠΏΡΡΠΆΠ΅Π½ΠΈΡ ΠΈ ΡΠΊΠ°ΡΠΎΠΊ Π½Π°ΠΏΡΡΠΆΠ΅Π½ΠΈΡ, Π²ΠΊΠ»ΡΡΠ°Ρ Π³Π΅Π½Π΅ΡΠ°ΡΠΎΡ Π²Π΅ΡΡΠΎΠ²ΡΡ
ΡΠ»Π΅ΠΊΡΡΠΎΡΡΠ°Π½ΡΠΈΠΉ Π² ΡΠΈΡΡΠ΅ΠΌΠ°Ρ
ΠΏΠ΅ΡΠ΅Π΄Π°ΡΠΈ ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½Π΅ΡΠ³ΠΈΠΈ. Π‘Π½Π°ΡΠ°Π»Π° Π²ΡΠΏΠΎΠ»Π½ΡΠ΅ΡΡΡ Π°Π½Π°Π»ΠΈΠ· ΠΏΠΎΡΠΎΠΊΠ° Π½Π΅ΡΠΈΠΌΠΌΠ΅ΡΡΠΈΡΠ½ΠΎΠΉ Π½Π°Π³ΡΡΠ·ΠΊΠΈ ΡΡΠ΅Ρ
ΡΠ°Π· Π΄Π»Ρ ΡΠ°ΡΡΠ΅ΡΠ° ΡΠ°Π·Π½ΠΎΡΡΠ½ΡΡ
Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊ ΡΠ»Π΅ΠΊΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠ΅ΡΠΈ, ΡΡΠΎΠ²Π½Π΅ΠΉ Π½Π°ΠΏΡΡΠΆΠ΅Π½ΠΈΡ, Π°ΠΊΡΠΈΠ²Π½ΠΎΠΉ ΠΈ ΡΠ΅Π°ΠΊΡΠΈΠ²Π½ΠΎΠΉ ΠΌΠΎΡΠ½ΠΎΡΡΠΈ. ΠΠΎΡΠ»Π΅ ΡΡΠΎΠ³ΠΎ Π΄ΠΈΡΠΊΡΠ΅ΡΠ½ΠΎΠ΅ Π²Π΅ΠΉΠ²Π»Π΅Ρ-ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΎΠ±ΡΠ΅Π΄ΠΈΠ½ΡΠ΅ΡΡΡ Ρ Π²Π΅ΡΠΎΡΡΠ½ΠΎΡΡΠ½ΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΡΡ ECOC-SVM Π΄Π»Ρ ΠΏΠΎΡΡΡΠΎΠ΅Π½ΠΈΡ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΎΡΠ°. ΠΠ°ΠΊΠΎΠ½Π΅Ρ, ECOC-SVM ΠΊΠ»Π°ΡΡΠΈΡΠΈΡΠΈΡΡΠ΅Ρ ΠΈ ΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΡΠΈΡΡΠ΅Ρ ΡΠΈΠΏ Π²ΠΎΠ·ΠΌΡΡΠ΅Π½ΠΈΡ Π² ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠΈ Ρ ΠΎΡΠΊΠ»ΠΎΠ½Π΅Π½ΠΈΠ΅ΠΌ ΡΠ½Π΅ΡΠ³ΠΈΠΈ Π΄ΠΈΡΠΊΡΠ΅ΡΠ½ΠΎΠ³ΠΎ Π²Π΅ΠΉΠ²Π»Π΅Ρ-ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ. ΠΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΡΠΉ ΠΌΠ΅ΡΠΎΠ΄ Π΄Π°Π΅Ρ ΡΠ΄ΠΎΠ²Π»Π΅ΡΠ²ΠΎΡΠΈΡΠ΅Π»ΡΠ½ΡΡ ΡΠΎΡΠ½ΠΎΡΡΡ 99,2% ΠΏΠΎ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ Ρ Ρ
ΠΎΡΠΎΡΠΎ ΠΈΠ·Π²Π΅ΡΡΠ½ΡΠΌΠΈ ΠΌΠ΅ΡΠΎΠ΄Π°ΠΌΠΈ ΠΈ ΠΏΠΎΠΊΠ°Π·ΡΠ²Π°Π΅Ρ, ΡΡΠΎ ΠΊΠ°ΠΆΠ΄ΠΎΠ΅ Π½Π°ΡΡΡΠ΅Π½ΠΈΠ΅ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½Π΅ΡΠ³ΠΈΠΈ ΠΈΠΌΠ΅Π΅Ρ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΡΠ΅ ΠΎΡΠΊΠ»ΠΎΠ½Π΅Π½ΠΈΡ ΠΎΡ ΡΠΈΡΡΠΎ ΡΠΈΠ½ΡΡΠΎΠΈΠ΄Π°Π»ΡΠ½ΠΎΠΉ ΡΠΎΡΠΌΡ Π²ΠΎΠ»Π½Ρ, ΡΡΠΎ ΡΠΏΠΎΡΠΎΠ±ΡΡΠ²ΡΠ΅Ρ ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΡ ΠΈ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΡΠΈΠΏΠ° Π²ΠΎΠ·ΠΌΡΡΠ΅Π½ΠΈΡ, Π³Π΅Π½Π΅ΡΠΈΡΡΠ΅ΠΌΠΎΠ³ΠΎ Π²Π΅ΡΡΠΎΠ²ΡΠΌ Π³Π΅Π½Π΅ΡΠ°ΡΠΎΡΠΎΠΌ
Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction
For large, real-world inductive learning problems, the number of training
examples often must be limited due to the costs associated with procuring,
preparing, and storing the training examples and/or the computational costs
associated with learning from them. In such circumstances, one question of
practical importance is: if only n training examples can be selected, in what
proportion should the classes be represented? In this article we help to answer
this question by analyzing, for a fixed training-set size, the relationship
between the class distribution of the training data and the performance of
classification trees induced from these data. We study twenty-six data sets
and, for each, determine the best class distribution for learning. The
naturally occurring class distribution is shown to generally perform well when
classifier performance is evaluated using undifferentiated error rate (0/1
loss). However, when the area under the ROC curve is used to evaluate
classifier performance, a balanced distribution is shown to perform well. Since
neither of these choices for class distribution always generates the
best-performing classifier, we introduce a budget-sensitive progressive
sampling algorithm for selecting training examples based on the class
associated with each example. An empirical analysis of this algorithm shows
that the class distribution of the resulting training set yields classifiers
with good (nearly-optimal) classification performance
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