본문 바로가기
HOME> 논문 > 논문 검색상세

논문 상세정보

A Hybrid Approach for Big Data Outlier Detection from Electric Power SCADA System

Alves, Wesin ; Martins, Daniel ; Bezerra, Ubiratan ; Klautau, Aldebaro ;
  • 초록  

    SCADA (Supervisory Control and Data Acquisition) databases have three main features that identify them as big data systems: volume, variety and velocity. SCADAs are extremely important for the safety and security 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.


 활용도 분석

  • 상세보기

    amChart 영역
  • 원문보기

    amChart 영역

원문보기

무료다운로드
  • 원문이 없습니다.

유료 다운로드의 경우 해당 사이트의 정책에 따라 신규 회원가입, 로그인, 유료 구매 등이 필요할 수 있습니다. 해당 사이트에서 발생하는 귀하의 모든 정보활동은 NDSL의 서비스 정책과 무관합니다.

NDSL에서는 해당 원문을 복사서비스하고 있습니다. 위의 원문복사신청 또는 장바구니 담기를 통하여 원문복사서비스 이용이 가능합니다.

이 논문과 함께 출판된 논문 + 더보기