Cybersecurity of distribution networks real-time monitoring: a parameter error correction model against false data injection attacks

As Smart Grid technologies are deployed, the many advantages of new metering, controls, and analysis come with added technical challenges. Specifically, the digitalization of the power grid and the increasing dependence on communications systems makes the network real-time monitoring more vulnerable to cyber-attacks. Cyber-attacks, if not detected and accurately corrected, can lead to system operator misinformation. Current state-of-the-art models for real-time monitoring cybersecurity, hypothesizes that the measurement model is correct, without error. While this assumption might be acceptable for systems and devices not dependent on communication networks, this can be considered a strong hypothesis for power grids real-time monitoring. False data injection attacks on the measurement model are also possible. This work presents a parameter correction model against false data injection attacks. False data injection attacks on measurements and measurement models are simultaneously considered. A chi-squared hypothesis test is used for detection of cyberattacks, while a normalized composed measurement error test is used for cyberattack identification. Parameter correction model is then used if a modelling error is identified, otherwise a measurement correction model is used if a measurement error is identified. Easy-to-implement model, built of the classical quasi-static state estimator, without hard-to-design parameters, suggest potential for real-life applications.