By Topic

Optimal Control of Voltage and Power in a Multi-Zonal MVDC Shipboard Power System

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Padmavathy Kankanala ; Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS, USA ; Suresh C. Srivastava ; Anurag K. Srivastava ; Noel N. Schulz

The Multi-Zonal Medium Voltage DC (MVDC) Shipboard Power System (SPS) architecture, proposed by the U.S. Navy for their future combatant system, consists of several voltage source converters (VSCs). The proposed architecture is tightly-coupled, power-limited and its performance needs to be evaluated for security, reliability, and survivability. Following system damage or a fault, the current flow pattern in the DC network may change, which may result in the failure of VSCs due to overvoltage developed across them in certain operating conditions. For a given MVDC system, DC voltage reference setting for one of the VSCs operating in the voltage regulator mode, and the optimal power reference settings of the remaining VSCs in the power dispatcher mode have to be pre-determined. These settings and control modes of VSCs are needed to maintain the DC voltage within desired margins (usually 5% around the nominal DC voltage), both in “pre-fault” and “post-fault outage” conditions. The problem has been formulated as an optimization problem with three different objective functions. Computational intelligence techniques have been applied for solving the optimization problem. These include the genetic algorithm (GA) and biogeography based optimization (BBO) methods. The results have been compared with a conventional Lagrange multiplier based method.

Published in:

IEEE Transactions on Power Systems  (Volume:27 ,  Issue: 2 )