TY - GEN
T1 - A feature-based diagnosis framework for power plant model validation
AU - Wu, Meng
AU - Xie, Le
N1 - Publisher Copyright:
© 2018 Power Systems Computation Conference.
PY - 2018/8/20
Y1 - 2018/8/20
N2 - There has been increasing recognition of the importance of utilizing phasor measurement units (PMUs) for power plant model validation (PPMV). In North America, the PMU-based PPMV is typically performed manually by utilities and independent system operators (ISOs) for diagnosing and calibrating power plant model problems. In order to guarantee the accuracy of the calibration results, engineers need to provide manual judgment to the mismatched PPMV cases, so that the type of the modeling problem (such as wrong machine parameters, missing governor models, etc.) can be determined before the model is sent for a detailed calibration. This manual judgment process has become the major bottleneck for automating the PMU-based PPMV. To overcome this difficulty, this paper proposes a feature-based diagnosis framework to determining the types of the power plant model problems. It mimics the human engineering judgment process via a supervised learning approach. Instead of applying purely curve fitting or sensitivity analysis, this approach uses the engineering experience extracted from the labeled historical PPMV cases, establish the critical feature space, and then perform artificial learning to determine the type of a power plant model problem. The proposed framework could serve as a screening tool for the PPMV engineering judgment process, which could potentially help automate the entire PMU-based PPMV applications.
AB - There has been increasing recognition of the importance of utilizing phasor measurement units (PMUs) for power plant model validation (PPMV). In North America, the PMU-based PPMV is typically performed manually by utilities and independent system operators (ISOs) for diagnosing and calibrating power plant model problems. In order to guarantee the accuracy of the calibration results, engineers need to provide manual judgment to the mismatched PPMV cases, so that the type of the modeling problem (such as wrong machine parameters, missing governor models, etc.) can be determined before the model is sent for a detailed calibration. This manual judgment process has become the major bottleneck for automating the PMU-based PPMV. To overcome this difficulty, this paper proposes a feature-based diagnosis framework to determining the types of the power plant model problems. It mimics the human engineering judgment process via a supervised learning approach. Instead of applying purely curve fitting or sensitivity analysis, this approach uses the engineering experience extracted from the labeled historical PPMV cases, establish the critical feature space, and then perform artificial learning to determine the type of a power plant model problem. The proposed framework could serve as a screening tool for the PPMV engineering judgment process, which could potentially help automate the entire PMU-based PPMV applications.
KW - Machine learning
KW - Phasor measurement unit
KW - Power plant model validation
KW - Problem diagnosis
UR - http://www.scopus.com/inward/record.url?scp=85054039185&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85054039185&partnerID=8YFLogxK
U2 - 10.23919/PSCC.2018.8442668
DO - 10.23919/PSCC.2018.8442668
M3 - Conference contribution
AN - SCOPUS:85054039185
SN - 9781910963104
T3 - 20th Power Systems Computation Conference, PSCC 2018
BT - 20th Power Systems Computation Conference, PSCC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th Power Systems Computation Conference, PSCC 2018
Y2 - 11 June 2018 through 15 June 2018
ER -