با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
دسته بندی:
داده های بزرگ - big data
سال انتشار:
2017
عنوان انگلیسی مقاله:
A Hybrid Approach for Big Data Outlier Detection from Electric Power SCADA System
ترجمه فارسی عنوان مقاله:
روش ترکیبی برای تشخیص پرت داده های بزرگ سیستم برق SCADA
منبع:
IEEE - IEEE LATIN AMERICA TRANSACTIONS, VOL. 15, NO. 1, JAN. 2017
نویسنده:
W. Alves, D. Martins, U. Bezerra and A. Klautau1
چکیده انگلیسی:
Supervisory control and data acquisition (SCADA)
databases have three main features that identify them as big data
systems: volume, variety and velocity. SCADAs are extremely
important for the safety and secure operation of modern power
system and provide essential online information about the power
system state to system operators. A current research challenge is
to efficiently process this big data, which involves real-time
measurements of hundreds of thousands of heterogeneous
electrical power system physical measurements. Among the
foreseen automation tasks, outlier detection is one of the most
important data mining techniques for power systems. However,
like others data mining techniques, traditional outlier detection
fails when dealing with problems in which the volume and
dimensionality of data are as high as the ones observed in a
SCADA. This work aims at circumventing these restrictions by
presenting a methodology for dealing with SCADA big data that
consists of a pre-processing algorithm and hybrid approach
outlier detectors. The hybrid approach is assessed using real data
from a Brazilian utility company. The results show that the
proposed methodology is capable of identifying outliers
correlated with important events that affect the system.
Keywords: Outlier detection | high dimensionality | massive datasets | SCADA | electric power systems.
قیمت: رایگان
توضیحات اضافی:
تعداد نظرات : 0