A Data-Driven Approach to Linearize Power Flow Equations Considering Measurement Noise

The nonlinearity of power flow (PF) equations challenges the analysis and optimization of power systems. Both model-based and data-driven approach was recently applied to linearize the PF equations. The data-driven approach relies heavily on the quality of the measurement data, where measurement noi...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on smart grid 2020-05, Vol.11 (3), p.2576-2587
Main Authors: Liu, Yuxiao, Wang, Yi, Zhang, Ning, Lu, Dan, Kang, Chongqing
Format: Article
Language:English
Subjects:
Constraints
Covariance matrices
data-driven
Flow equations
Iterative methods
Jacobi matrix method
Jacobian matrices
Jacobian matrix
linearization
Mathematical model
Measurement noise
Noise
Noise measurement
Optimization
optimization methods
Power flow
Power measurement
Power systems
Quadratic programming
regression
Regression analysis
Regression models
Voltage measurement
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The nonlinearity of power flow (PF) equations challenges the analysis and optimization of power systems. Both model-based and data-driven approach was recently applied to linearize the PF equations. The data-driven approach relies heavily on the quality of the measurement data, where measurement noise may cause large modeling errors. This paper tackles the challenges of the hidden measurement noise in the data-driven PF linearization problem. We transform the problem into a regression model where the structure of the AC power flow equations is exploited. Jacobian matrix guided constraints are added to shrink the search space greatly. This regression model is formulated as three linearly constrained quadratic programming problems and is solved in an iterative manner. The effectiveness of the proposed approach is demonstrated through case studies on several IEEE standard test systems and a practical provincial system.
ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2019.2957799