From Optimization-Based Machine Learning to Interpretable Security Rules for Operation
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نوع المصدر |
مقال
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بيانات التأليف |
Cremer, Jochen L. (Author)
Konstantelos, Ioannis (Author)
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بيانات الدورية المصدر |
العنوان:
IEEE Transactions on Power Systems
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رقم المجلد/ العدد: 2019/SEP V.34 N.5
رقم الإستدعاء: 621.3105 ITP الموقع: Periodicals & References Hall - 2nd floor
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الوصف المادي |
p 3826 - 3836
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رأس الموضوع/ الواصفات |
Engineering
(39711)
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المستخلص |
Various supervised machine learning approaches have been used in the past to assess the power system security (also known as reliability). This is typically done by training a classifier on a large number of operating points whose postfault status (stable or unstable) has been determined via time-domain simulations. The output of this training process can be expressed as a security rule that is used online to classify an operating point....
المستخلص الكامل
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| المزيد من المقالات من
الدورية:
IEEE Transactions on Power Systems
(3659)
العدد: 2019/SEP V.34 N.5
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