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Autonomous state estimation based diagnostic system in smart grid

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3 Author(s)
Sungyun Choi ; School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, 30332-0250, USA ; A. P. Sakis Meliopoulos ; K. Ratnesh

Power system state estimation has been used to filter raw measurement data, providing reliable and accurate real time model, i.e. the operating conditions of the system, mathematical model and network topology. Recently, as the power system operation and control focuses on the distribution system (e.g., Smart Grid) with high penetration of distributed renewable generators and various inverter-based smart devices, the modern power system requires a new approach of state estimation that can adapt to dynamic conditions of grids. We propose a new state estimation approach for this system that operates autonomously. The autonomous state estimation consists of two concepts: 1) a robotic preprocessor that autonomously creates the network connectivity, states and measurement model and 2) state estimation. This paper applies the autonomous state estimation to extract the real-time model of Smart Grid and then to use the real-time model to perform diagnostic of the system. First the real-time model is validated with standard state estimation procedures, i.e. chi-square test for expected errors in the state estimates and confidence level of the validity of the real-time model. The validated real-time model is eventually used to assess whether the system operates within operating limits and to issue diagnostic in case system components operate near limits or exceed limits. This paper also presents the laboratory demonstration of the proposed diagnostic system using the Smart Grid Energy Systems, which include a PV system, a programmable load that can emulate the daily load profile, and an energy storage system that has three operational modes: 1) the standby mode, 2) the inverter mode, and 3) the charger mode. The proposed approach is tested and verified with the Smart Grid Energy Systems, and proper test results are presented.

Published in:

Innovative Smart Grid Technologies (ISGT), 2013 IEEE PES

Date of Conference:

24-27 Feb. 2013