Robust Model-Based Fault Diagnosis for DC Zonal Electrical Distribution System

Abstract

A key element of the U.S. Navy's transition to an electric naval force is an Integrated Power System (IPS) that provides continuity of service to vital systems despite combat damage. In order to meet subsequent survivability standards under a reduced manning constraint, the IPS system must include a fault tolerant control scheme, capable of achieving automated graceful degradation despite major disruptions involving cascading failures. Toward this objective, online modelbased residual generation techniques are proposed, which identify explicitly defined faults within a stochastic DC Zonal Electrical Distribution System (DC ZEDS). Two novel polynomial approaches to the design of unknown input observers (UIO) are developed to estimate the partial state and, under certain conditions, the unknown input. These methods are shown to apply to a larger class of systems compared to standard projection based approaches where the UIO rank condition is not satisfied. It is shown that the partial-state estimate is sufficient to the computation of residuals for fault diagnosis, even in such cases where full-state estimation is not possible. In order to reduce the complexity of the system, a modular approach to Fault Detection and Isolation (FDI) is presented. Here, the innovations generated from a bank of Kalman filters (some of them UIOs) act as a structured residual set for the stochastic DC ZEDS subsystem modules and are shown to detect and isolate various classes of faults. Certain mathematical models are also shown to effectively identify input/output consistency of systems in explicitly defined fault conditions. Numerical simulation results are based on the well-documented Office of Naval Research Control Challenge benchmark system, which represents a prototypical U.S. Navy shipboard IPS power distribution system.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2007
Accession Number
ADA473418

Entities

People

  • John D. Stevens

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Advanced Electronics
  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Computational Science
  • Control Systems Engineering
  • Electrical Engineering
  • Engineers
  • Failure Mode And Effect Analysis
  • Information Processing
  • Information Science
  • Inverters
  • Mathematical Models
  • Multiple Input Multiple Output
  • Neural Networks
  • Nonlinear Dynamics
  • Power Electronics
  • Power Supplies
  • Signal Processing
  • Switched Mode Power Supplies

Fields of Study

  • Engineering

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