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 design a 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 quality distur...
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| Дата: | 2019 |
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| Автори: | , , |
| Формат: | Стаття |
| Мова: | English |
| Опубліковано: |
National Technical University "Kharkiv Polytechnic Institute" and State Institution “Institute of Technical Problems of Magnetism of the National Academy of Sciences of Ukraine”
2019
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| Онлайн доступ: | http://eie.khpi.edu.ua/article/view/2074-272X.2019.6.09 |
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| Назва журналу: | Electrical Engineering & Electromechanics |
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Electrical Engineering & Electromechanics| id |
eiekhpieduua-article-188302 |
|---|---|
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eiekhpieduua-article-1883022019-12-20T19:48:41Z UNBALANCED LOAD FLOW WITH HYBRID WAVELET TRANSFORM AND SUPPORT VECTOR MACHINE BASED ERROR-CORRECTING OUTPUT CODES FOR POWER QUALITY DISTURBANCES CLASSIFICATION INCLUDING WIND ENERGY UNBALANCED LOAD FLOW WITH HYBRID WAVELET TRANSFORM AND SUPPORT VECTOR MACHINE BASED ERROR-CORRECTING OUTPUT CODES FOR POWER QUALITY DISTURBANCES CLASSIFICATION INCLUDING WIND ENERGY Rahmani, Ala eddine Slimani, Linda Bouktir, Tarek unbalanced load flow wavelet transform (WT) support vector machines (SVM) power quality disturbance wavelet energy 621.3 несбалансированный поток нагрузки вейвлет-преобразование (WT) машины опорных векторов (SVM) нарушение качества электроэнергии энергия вейвлета 621.3 Purpose. The most common methods to design a 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 quality disturbances 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 to the energy deviation of the discrete wavelet transform. The proposed method 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. Цель. Наиболее распространенные методы построения мультиклассовой классификации заключаются в определении набора двоичных классификаторов и их объединении. В данной статье предложена машина опорных векторов с классификатором выходных кодов исправления ошибок (ECOC-SVM) с целью классифицировать и характеризовать такие нарушения качества электроэнергии, как гармонические искажения, падение напряжения и скачок напряжения, включая генератор ветровых электростанций в системах передачи электроэнергии. Сначала выполняется анализ потока несимметричной нагрузки трех фаз для расчета разностных характеристик электрической сети, уровней напряжения, активной и реактивной мощности. После этого дискретное вейвлет-преобразование объединяется с вероятностной моделью ECOC-SVM для построения классификатора. Наконец, ECOC-SVM классифицирует и идентифицирует тип возмущения в соответствии с отклонением энергии дискретного вейвлет-преобразования. Предложенный метод дает удовлетворительную точность 99,2% по сравнению с хорошо известными методами и показывает, что каждое нарушение качества электроэнергии имеет определенные отклонения от чисто синусоидальной формы волны, что способствует распознаванию и определению типа возмущения, генерируемого ветровым генератором. National Technical University "Kharkiv Polytechnic Institute" and State Institution “Institute of Technical Problems of Magnetism of the National Academy of Sciences of Ukraine” 2019-12-19 Article Article application/pdf http://eie.khpi.edu.ua/article/view/2074-272X.2019.6.09 10.20998/2074-272X.2019.6.09 Electrical Engineering & Electromechanics; No. 6 (2019); 62-69 Электротехника и Электромеханика; № 6 (2019); 62-69 Електротехніка і Електромеханіка; № 6 (2019); 62-69 2309-3404 2074-272X en http://eie.khpi.edu.ua/article/view/2074-272X.2019.6.09/188004 Copyright (c) 2019 Ala eddine Rahmani, Linda Slimani, Tarek Bouktir https://creativecommons.org/licenses/by-nc/4.0 |
| institution |
Electrical Engineering & Electromechanics |
| collection |
OJS |
| language |
English |
| topic |
unbalanced load flow wavelet transform (WT) support vector machines (SVM) power quality disturbance wavelet energy 621.3 несбалансированный поток нагрузки вейвлет-преобразование (WT) машины опорных векторов (SVM) нарушение качества электроэнергии энергия вейвлета 621.3 |
| spellingShingle |
unbalanced load flow wavelet transform (WT) support vector machines (SVM) power quality disturbance wavelet energy 621.3 несбалансированный поток нагрузки вейвлет-преобразование (WT) машины опорных векторов (SVM) нарушение качества электроэнергии энергия вейвлета 621.3 Rahmani, Ala eddine Slimani, Linda Bouktir, Tarek UNBALANCED LOAD FLOW WITH HYBRID WAVELET TRANSFORM AND SUPPORT VECTOR MACHINE BASED ERROR-CORRECTING OUTPUT CODES FOR POWER QUALITY DISTURBANCES CLASSIFICATION INCLUDING WIND ENERGY |
| topic_facet |
unbalanced load flow wavelet transform (WT) support vector machines (SVM) power quality disturbance wavelet energy 621.3 несбалансированный поток нагрузки вейвлет-преобразование (WT) машины опорных векторов (SVM) нарушение качества электроэнергии энергия вейвлета 621.3 |
| format |
Article |
| author |
Rahmani, Ala eddine Slimani, Linda Bouktir, Tarek |
| author_facet |
Rahmani, Ala eddine Slimani, Linda Bouktir, Tarek |
| author_sort |
Rahmani, Ala eddine |
| title |
UNBALANCED LOAD FLOW WITH HYBRID WAVELET TRANSFORM AND SUPPORT VECTOR MACHINE BASED ERROR-CORRECTING OUTPUT CODES FOR POWER QUALITY DISTURBANCES CLASSIFICATION INCLUDING WIND ENERGY |
| title_short |
UNBALANCED LOAD FLOW WITH HYBRID WAVELET TRANSFORM AND SUPPORT VECTOR MACHINE BASED ERROR-CORRECTING OUTPUT CODES FOR POWER QUALITY DISTURBANCES CLASSIFICATION INCLUDING WIND ENERGY |
| title_full |
UNBALANCED LOAD FLOW WITH HYBRID WAVELET TRANSFORM AND SUPPORT VECTOR MACHINE BASED ERROR-CORRECTING OUTPUT CODES FOR POWER QUALITY DISTURBANCES CLASSIFICATION INCLUDING WIND ENERGY |
| title_fullStr |
UNBALANCED LOAD FLOW WITH HYBRID WAVELET TRANSFORM AND SUPPORT VECTOR MACHINE BASED ERROR-CORRECTING OUTPUT CODES FOR POWER QUALITY DISTURBANCES CLASSIFICATION INCLUDING WIND ENERGY |
| title_full_unstemmed |
UNBALANCED LOAD FLOW WITH HYBRID WAVELET TRANSFORM AND SUPPORT VECTOR MACHINE BASED ERROR-CORRECTING OUTPUT CODES FOR POWER QUALITY DISTURBANCES CLASSIFICATION INCLUDING WIND ENERGY |
| title_sort |
unbalanced load flow with hybrid wavelet transform and support vector machine based error-correcting output codes for power quality disturbances classification including wind energy |
| title_alt |
UNBALANCED LOAD FLOW WITH HYBRID WAVELET TRANSFORM AND SUPPORT VECTOR MACHINE BASED ERROR-CORRECTING OUTPUT CODES FOR POWER QUALITY DISTURBANCES CLASSIFICATION INCLUDING WIND ENERGY |
| description |
Purpose. The most common methods to design a 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 quality disturbances 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 to the energy deviation of the discrete wavelet transform. The proposed method 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. |
| publisher |
National Technical University "Kharkiv Polytechnic Institute" and State Institution “Institute of Technical Problems of Magnetism of the National Academy of Sciences of Ukraine” |
| publishDate |
2019 |
| url |
http://eie.khpi.edu.ua/article/view/2074-272X.2019.6.09 |
| work_keys_str_mv |
AT rahmanialaeddine unbalancedloadflowwithhybridwavelettransformandsupportvectormachinebasederrorcorrectingoutputcodesforpowerqualitydisturbancesclassificationincludingwindenergy AT slimanilinda unbalancedloadflowwithhybridwavelettransformandsupportvectormachinebasederrorcorrectingoutputcodesforpowerqualitydisturbancesclassificationincludingwindenergy AT bouktirtarek unbalancedloadflowwithhybridwavelettransformandsupportvectormachinebasederrorcorrectingoutputcodesforpowerqualitydisturbancesclassificationincludingwindenergy |
| first_indexed |
2024-06-01T14:39:26Z |
| last_indexed |
2024-06-01T14:39:26Z |
| _version_ |
1800670037162328064 |