TY - GEN
T1 - Autonomous state estimation based diagnostic system in smart grid
AU - Choi, Sungyun
AU - Sakis Meliopoulos, A. P.
AU - Ratnesh, K.
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - Laboratories power distribution
KW - power system analysis computing
KW - power system modeling
KW - smart grids
KW - state estimation
UR - http://www.scopus.com/inward/record.url?scp=84876934570&partnerID=8YFLogxK
U2 - 10.1109/ISGT.2013.6497877
DO - 10.1109/ISGT.2013.6497877
M3 - Conference contribution
AN - SCOPUS:84876934570
SN - 9781467348942
T3 - 2013 IEEE PES Innovative Smart Grid Technologies Conference, ISGT 2013
BT - 2013 IEEE PES Innovative Smart Grid Technologies Conference, ISGT 2013
T2 - 2013 IEEE PES Innovative Smart Grid Technologies Conference, ISGT 2013
Y2 - 24 February 2013 through 27 February 2013
ER -