@article{Wang2026, 
author = {Zhenyu Wang and Donglian Qi and Zhenming Li and Jianliang Zhang},
title = {Valid False Data Injection Attacks Under Approximated Power Flow Model in Power Distribution Systems},
year = {2026},
journal = {CSEE Journal of Power and Energy Systems},
volume = {12},
number = {4},
pages = {1952-1961},
keywords = {Cybersecurity, Nonlinear approximation, power distribution system, power flow analysis, state estimation},
url = {https://www.sciopen.com/article/10.17775/CSEEJPES.2022.04560},
doi = {10.17775/CSEEJPES.2022.04560},
abstract = {This paper explores the approximate solutions of AC power flow model with intractable nonlinearities, which significantly relaxes the strong condition of pre-estimated states for false data injection (FDI) attacks in power distribution systems. The main idea is to solve for complex-valued perturbations around a priori determined nominal voltage without neglecting quadratic terms in nonlinear power flow equations. We prove that based on power injection measurements, the approximate perturbations of nodal voltage can be more accurately obtained by a recursive approximation that considers more nonlinear components, compared with those from power-flow measurements. For FDI attacks, the strong requirement of pre-estimated states for constructing attack vector is relaxed by approximate states, while reflecting the intent of attackers and passing a bad data detection scheme in power systems. Simulation results based on a symmetric balanced 56-node testbed verify the performance of the proposed nonlinear approximation in terms of accuracy and FDI attacks effect, compared with the commonly used linear method.}
}