Multi-fidelity Gaussian Process for Distribution System Voltage Probabilistic Estimation with PVs
- Resource Type
- Conference
- Authors
- Zhang, Jinxian; Zhao, Junbo; Ye, Ketian; Ding, Fei
- Source
- 2022 IEEE 6th Conference on Energy Internet and Energy System Integration (EI2) Energy Internet and Energy System Integration (EI2), 2022 IEEE 6th Conference on. :3088-3093 Nov, 2022
- Subject
- Power, Energy and Industry Applications
Voltage measurement
Uncertainty
Fuses
Estimation
Gaussian processes
System integration
Probabilistic logic
Distribution system estimation
Gaussian process
renewable energy integration
distribution system visibility
- Language
The increasing penetration of behind-the-meter PVs causes challenges to maintain voltage security due to the lack of distribution system visibility. This paper proposes a nonlinear autoregressive Gaussian process (NARGP) approach to fuse limited number of SCADA/AMI data together with historical pseudo measurements for distribution node voltage probabilistic estimation. The high-fidelity SCADA data are fused with the low-fidelity AMI and pseudo measurements by the autoregressive algorithm embedded in the Gaussian process. This allows us to use multi-fidelity data to achieve entire distribution system voltage visibility. Numerical results carried out on the IEEE 123node system demonstrate that the NARGP method is able to obtain high accuracy in estimating bus voltage and quantifying estimation uncertainties as compared to other approaches.