The main function of a shipboard power system is to maintain the availability of energy to all connected loads to keep all systems and equipement operational. Under any abnormal condition, only the smallest portion of the electric system should be interrupted. Existing protection and control systems on surface ships have several shortcomings in providing continuous supply under battle and certain major failure conditions. The control strategies that are implemented when these types of damage occue are not effective in isolating or reconfiguring only the loads affected by the damage, and are highly dependent on human intervention to manually reconfigure the distribution system to restore supply to healthy loads.
Today’s Navy is pushing forth general requirements to reduce manning on ships and electrical system requirements to reduce cost, provide uninterrupted power, provide quality power to appropriate loads, provide zonal fight through survivablility, and provide automated reconfigurability.
The principle investigator proposes a unique redefinition of the reconfiguration problem that determines reconfiguration actions to prevent loss of supply as well as to restore power quickly to critical loads and change operating modes. This approach is termed “predictive” reconfiguration by the principle investigator and provides a creative exploration of shipboard power systems. This “predictive” reconfiguration concept includes typical reconfiguartion functions that are feasible because of the advanced technologies available on modern ships. The proposed research brings advanced solutions such as missile detection systems, geographic information systems, and contingency analysis to shipboard power systems with a goal of survivability and reduced manning. This work provides technology to address the Navy’s prevelant need for automated and reconfigurable shipboard power systems.
Principal Investigator: Dr. Karen L. Butler-Purry
Sponsor: Project Duration:
Official of Naval Research 1999-2003
Adeoti Adehran Ujjwal Rajbhandari
Hong Xiao Sanjeev Srivastava
Li Qi Patrick Chinery