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Identification of Power Transformer Currents by Using Random Forest and Boosting Techniques
المؤلفون المشاركون
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-09-18
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
الملخص EN
In this research, a differential protection technique for a power transformer is proposed by using random forest and boosting learning machines.
The proposed learning machines aim to provide a protection expert system that distinguishes between different transformer status which are normal, inrush, overexcitation, CT saturation, or internal fault.
Data for 20 different transformers with 5 operating cases are used in this research.
The utilized random forest and boosting techniques are trained using these data.
Meanwhile, the proposed models are validated by other measures such as out-of-bag error and confusion matrix.
In addition, variable importance analysis that shows signal’s component importance inside a transformer at different instances is provided.
According to the result, the proposed random forest model successfully identifies all of the current cases (100% accuracy for the conducted experiment).
Meanwhile, it is found that it is less accurate as a conditional monitoring element with accuracy in the range of 97%–98%.
On the other hand, the proposed boosting model identifies all of the currents for both cases (100% accuracy for the conducted experiment).
In addition to that, a comparison is conducted between the proposed models and other AI-based models.
Based on this comparison, the proposed boosting model is the simplest and the most accurate model as compared to other models.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Khatib, Tamer& Arar, Gazi. 2020. Identification of Power Transformer Currents by Using Random Forest and Boosting Techniques. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1193098
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Khatib, Tamer& Arar, Gazi. Identification of Power Transformer Currents by Using Random Forest and Boosting Techniques. Mathematical Problems in Engineering No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1193098
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Khatib, Tamer& Arar, Gazi. Identification of Power Transformer Currents by Using Random Forest and Boosting Techniques. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1193098
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1193098
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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