Specijalizovani algoritmi za detekciju, identifikaciju i estimaciju loših podataka u elektrodistributivnim mrežama
Specialized algorithms for detection, identification and estimation of bad data inpower distribution networks
Author
Krsman, VladanMentor
Sarić, AndrijaCommittee members
Strezoski, VladimirŠvenda, Goran
Popović, Dragan
Ranković, Aleksandar
Sarić, Andrija
Metadata
Show full item recordAbstract
Doktorskom disertacijom je dokazano da postojeće metode detekcije i identifikacije loših podataka nisu primenjive na distributivne mreže usled njihovih specifičnosti u stepenu redundanse merenja i broja pseudo merenja. Dodatno, razvijeni su algoritmi detekcije loših oblasti primenom dekuplovanog Hi-kvadrat testa, identifikacije loših merenja primenom novo definisanih izbeljenih reziduala, estimacije fazne konektivnosti primenom uslovnih ograničenja u estimatoru stanja, i korekcije pseudo merenja primenom informacija sa pametnih brojila. Navedeni algoritmi su specijalizovani za distributivne mreže i verifikovani primenom na dva test sistema.
The doctoral dissertation has demonstrated that conventional bad data detection and identification methods cannot be efficiently applied in distribution networks, due to their characteristics such as low measurement redundancy, number of pseudo measurements and level of measurements correlation. In addition, the doctoral dissertation described newly developed algorithms for bad area detection based on decoupled Chi-squares test, bad data identification using newly defined whitened residuals, estimation of phase connectivity by extension of state estimation with conditional constraints and correction of pseudo measurements using AMI data. The mentioned algorithms are specialized for distribution networks and verified through simulation on two test systems.