Distributed Adaptive Learning Framework for Wide Area Monitoring of Power Systems Integrated with Distributed Generations

Kang Li, Yuanjun Guo, David Laverty, Haibo He, Minrui Fei

1School of Electronics, Electrical Engineering and Computer Science, Queens University Belfast, Belfast, UK 2UK-China Science Bridge Joint Laboratory on Energy and Automation, Shanghai University, China and Queens University Belfast, UK.
Department of Electrical, Computer, and Biomedical Engineering University of Rhode Island, USA.
School of Electronics, Electrical Engineering and Computer Science, Queens University Belfast, Belfast, UK.
School of Mechatronics and Automation Shanghai Uni-versity, China.

DOI: 10.4236/epe.2013.54B185                

Abstract

This paper presents a preliminary study of developing a novel distributed adaptive real-time learning framework for wide area monitoring of power systems integrated with distributed generations using synchrophasor technology. The framework comprises distributed agents (synchrophasors) for autonomous local condition monitoring and fault detection, and a central unit for generating global view for situation awareness and decision making. Key technologies that can be integrated into this hierarchical distributed learning scheme are discussed to enable real-time information extraction and knowledge discovery for decision making, without explicitly accumulating and storing all raw data by the central unit. Based on this, the configuration of a wide area monitoring system of power systems using synchrophasor technology, and the functionalities for locally installed open-phasor-measurement-units (OpenPMUs) and a central unit are presented. Initial results on anti-islanding protection using the proposed approach are given to illustrate the effectiveness.

Keywords

Smart Grid; Anti-islanding Protection; Distributed Agents; Incremental Learning; Monitoring and Control

Conflicts of Interest

The authors declare no conflicts of interest.

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