State estimation plays a vital role in sustaining the normal operation of islanded microgrids (MG). Constraints on the systems arose from external disturbance or generation and load constraints inside microgrids. The issue of detecting bade data injections occurring in communication channels of islanded microgrids is studied in this project. A novel intelligent filtering scheme is developed to reduce uncertainty over communication channels between microgrids (Generators). The operation performances of MG may be severely degraded in the existence of attacks. The proposed filtering algorithm can improve online state estimation utilizing two-step filtering and estimating to detect bade data injections and to attenuate the influences of system constraints. A historical data of the estimated state is utilized by a bad data detection scheme to detect the presence of bad data injections by predict the new estimated states. Typically, grid-connected microgrids are quasi-static systems that are constant or are varying slowly, so we could assume the current estimated state would be a copy of the preceding estimated. The problem of detecting bade data injections occurring in communication channels of islanded microgrids is more difficult due to timevarying load demand and communication constraints, which motivates this project. Bad data injections of false measurements can conceivably promote to improper control actions endanger the security and reliability of power transmission networks. Our aim is to develop a novel filter to handle communication constraints and to deal with bad data injection attacks to accomplish the prescribed performances. The developed filtering technique will sustain the reliability and the performance of the multi-microgrids in the occurrence of cyber-attacks and communication constraints. Hence, the new intelligent filtering algorithm comprises of two-step filter algorithms, when the primary (local) filter algorithm is estimated the states, an online test detection scheme would be is automatically used to detect the bad data injections based on the current and predictive values of the estimated states. A secondary (remote) filter algorithm can be used to isolate the bad data injection attacker and to reconfigure the power grid when the attack test is positive. If the identification of attacker is negative, the local filter would be reset and rebooted again using the updated state estimation from the secondary level estimator or when the attackers effects is completely isolated.
|Effective start/end date||1/01/20 → 1/12/20|
Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.