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Detection and classification of power quality disturbances using parallel neural networks based on discrete wavelet transform
المؤلفون المشاركون
Jarusi, Maryam Rahmati
Shakarami, Mahmud Rida
Namdari, Farhad
المصدر
العدد
المجلد 12، العدد 1 (31 مارس/آذار 2016)، ص ص. 158-173، 16ص.
الناشر
تاريخ النشر
2016-03-31
دولة النشر
الجزائر
عدد الصفحات
16
التخصصات الرئيسية
الموضوعات
الملخص EN
In this paper, a new method for the detection and classification of all types of power quality disturbances is presented.
In addition to separating the disturbance signals, the proposed method is able to determine the type of disturbances.
Disturbance waveforms are generated based on IEEE 1159 standard and they are de-noised using discrete wavelet transform.
To detect the sinusoidal signals from disturbance signals, new criteria have been proposed.
By introducing these new criteria, the classification algorithm is not active for non-disturbance signals.
Therefore, the computation time is reduced.
If a signal has disturbance, to extract the required information, it is analyzed using discrete wavelet transform.
Using this information, the appropriate feature vectors are introduced.
Parallel neural networks structures are proposed for the classification of disturbances.
The inputs of these networks are the introduced feature vectors.
The proposed method is done for all power quality disturbances including DC offset, flicker, interrupt, sag, swell, harmonic, notching, impulsive transient, oscillatory transient and eight combinations of these including the harmonics with transient, harmonic with flicker, harmonic with sag, harmonic with swell, sag with flicker, swell with flicker, transient with sag and transient with swell.
The performance of this algorithm is compared with a single neural network structure.
The results indicate using the parallel neural networks structure, computational time is much reduced and the accuracy of classification of power quality disturbances is significantly increased.
Comparison the obtained results by the method with other methods, represents very high performance of the proposed method with precision %99.53.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Jarusi, Maryam Rahmati& Shakarami, Mahmud Rida& Namdari, Farhad. 2016. Detection and classification of power quality disturbances using parallel neural networks based on discrete wavelet transform. Journal of Electrical Systems،Vol. 12, no. 1, pp.158-173.
https://search.emarefa.net/detail/BIM-667736
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Jarusi, Maryam Rahmati…[et al.]. Detection and classification of power quality disturbances using parallel neural networks based on discrete wavelet transform. Journal of Electrical Systems Vol. 12, no. 1 (2016), pp.158-173.
https://search.emarefa.net/detail/BIM-667736
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Jarusi, Maryam Rahmati& Shakarami, Mahmud Rida& Namdari, Farhad. Detection and classification of power quality disturbances using parallel neural networks based on discrete wavelet transform. Journal of Electrical Systems. 2016. Vol. 12, no. 1, pp.158-173.
https://search.emarefa.net/detail/BIM-667736
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references : p. 172-173
رقم السجل
BIM-667736
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
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