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Multivariate Statistics and Supervised Learning for Predictive Detection of Unintentional Islanding in Grid-Tied Solar PV Systems
المؤلفون المشاركون
Vyas, Shashank
Kumar, Rajesh
Kavasseri, Rajesh
المصدر
Applied Computational Intelligence and Soft Computing
العدد
المجلد 2016، العدد 2016 (31 ديسمبر/كانون الأول 2016)، ص ص. 1-10، 10ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2016-03-30
دولة النشر
مصر
عدد الصفحات
10
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الملخص EN
Integration of solar photovoltaic (PV) generation with power distribution networks leads to many operational challenges and complexities.
Unintentional islanding is one of them which is of rising concern given the steady increase in grid-connected PV power.
This paper builds up on an exploratory study of unintentional islanding on a modeled radial feeder having large PV penetration.
Dynamic simulations, also run in real time, resulted in exploration of unique potential causes of creation of accidental islands.
The resulting voltage and current data underwent dimensionality reduction using principal component analysis (PCA) which formed the basis for the application of Q statistic control charts for detecting the anomalous currents that could island the system.
For reducing the false alarm rate of anomaly detection, Kullback-Leibler (K-L) divergence was applied on the principal component projections which concluded that Q statistic based approach alone is not reliable for detection of the symptoms liable to cause unintentional islanding.
The obtained data was labeled and a K -nearest neighbor ( K -NN) binomial classifier was then trained for identification and classification of potential islanding precursors from other power system transients.
The three-phase short-circuit fault case was successfully identified as statistically different from islanding symptoms.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Vyas, Shashank& Kumar, Rajesh& Kavasseri, Rajesh. 2016. Multivariate Statistics and Supervised Learning for Predictive Detection of Unintentional Islanding in Grid-Tied Solar PV Systems. Applied Computational Intelligence and Soft Computing،Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1094899
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Vyas, Shashank…[et al.]. Multivariate Statistics and Supervised Learning for Predictive Detection of Unintentional Islanding in Grid-Tied Solar PV Systems. Applied Computational Intelligence and Soft Computing No. 2016 (2016), pp.1-10.
https://search.emarefa.net/detail/BIM-1094899
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Vyas, Shashank& Kumar, Rajesh& Kavasseri, Rajesh. Multivariate Statistics and Supervised Learning for Predictive Detection of Unintentional Islanding in Grid-Tied Solar PV Systems. Applied Computational Intelligence and Soft Computing. 2016. Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1094899
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1094899
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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