Detekcija malicioznih napada na elektroenergetski sistem korišćenjem sinergije statičkog i dinamičkog estimatora stanja

• Main Author: Živković Nemanja
• Other Authors: Sarić Andrija
• Language: sr
• Published: Univerzitet u Novom Sadu, Fakultet tehničkih nauka u Novom Sadu 2019
• Subjects: Estimacija stanja / Detekcija loših merenja/ Maliciozni napad injektiranjem loših merenja/ Unscented Kalman filter; State Estimation/ Bad Data Detection/ False Data Injection Attack/Unscented Kalman Filter
• Online Access: https://www.cris.uns.ac.rs/DownloadFileServlet/Disertacija154044546094264.pdf?controlNumber=(BISIS)108186&fileName=154044546094264.pdf&id=12220&source=NDLTD&language=en; https://www.cris.uns.ac.rs/record.jsf?recordId=108186&source=NDLTD&language=en

<p>U ovoj doktorskoj disertaciji predložena je nova metoda za detekciju malicioznih napada injektiranjem loših merenja na elektroenergetski sistem. Predloženi algoritam baziran je na sinergiji statičke i dinamičke estimacije stanja, i u stanju je da detektuje ovaj tip napada u realnom vremenu, za najkritičniji scenario gde napadač ima potpuno znanje o sistemu, i neograničen pristup resursima.</p> === <p>This PhD thesis proposes a novel method for detection of malicious false data<br />injection attacks on power system. The proposed algorithm is based on<br />synergy between static and dynamic state estimators, and is capable of<br />detecting the forementioned attacks in real time, for the most critical scenarios,<br />where an attacker has complete knowledge about the compromised power<br />system and unlimited resources to stage an attack.</p>