Partial Knowledge Data-Driven Fvent Detection for Power Distribution Networks

被引:83
作者
Zhou, Yuxun [1 ]
Arghandeh, Reza [2 ]
Spanos, Costas J. [1 ]
机构
[1] Univ Calif Berkeley, Dept Elect Engn & Comp Sci, Berkeley, CA 94720 USA
[2] Florida State Univ, Elect & Comp Engn Dept, Tallahassee, FL 32306 USA
基金
新加坡国家研究基金会; 美国国家科学基金会;
关键词
Event detection; phasor measurement unit; distribution; machine learning; FAULT-DETECTION; DIAGNOSIS;
D O I
10.1109/TSG.2017.2681962
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The power system has been incorporating increasing amount of unconventional generations and loads, such as distributed renewable resources, electric vehicles, and controllable loads. The induced dynamic and stochastic power flow require high-resolution monitoring technology and agile decision support techniques for system diagnosis and control. This paper discusses the application of micro-phasor measurement unit (iraNIU) data for power distribution network event detection. A novel data-driven event detection method, namely hidden structure semi-supervised machine (HS3M), is established. HS3M only requires partial expert knowledge: it combines unlabeled data and partly labeled data in a large margin learning objective to bridge the gap between supervised learning, semi-supervised learning, and learning with hidden structures. optimize the non-convex learning objective, a novel global optimization algorithm, namely parametric dual optimization procedure, is established through its equivalence to a concave programming. Finally, the proposed method is validated on an actual distribution feeder with installed gPMUs, and the result justifies the effectiveness of the learning-based event detection framework, as well as its potential to serve as one of the core algorithms for power system security and reliability.
引用
收藏
页码:5152 / 5162
页数:11
相关论文
共 42 条
  • [1] [Anonymous], MICRO SYNCHROPHASOR
  • [2] [Anonymous], 1998, STAT LEARNING THEORY
  • [3] [Anonymous], P16411D3 IEEE STAND
  • [4] [Anonymous], 2010, Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems, SenSys '10
  • [5] [Anonymous], P 5 IFAC S FAULT DET
  • [6] Introduction to semi-supervised learning
    Goldberg, Xiaojin
    [J]. Synthesis Lectures on Artificial Intelligence and Machine Learning, 2009, 6 : 1 - 116
  • [7] [Anonymous], 11592009 IEEE STAND
  • [8] [Anonymous], 2010, 2010 IREP S BULK POW
  • [9] [Anonymous], 2011, ACM T INTEL SYST TEC, DOI DOI 10.1145/1961189.1961199
  • [10] [Anonymous], 000340 ARPAE US DEP