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ogy can achieve the online adaptive identifica‐
tion for control in real time power plant opera‐
tion and offline identification for fault diagnosis.
Enacheanu et al [21] proposed a distribution sys‐
tem architecture that can make the reconfigura‐
tion in the power easy to achieve. The reconfigu‐
ration in that work is to locate and isolate the
faults in the power system. A remote agent is
used in that work as a central controller for the
reconfiguration of power systems.
Nagata et al [22] proposed MAS based restora‐
tion methodology for power systems. The MAS
proposed was composed of bus agents and a
single facilitator agent. The bus agent decides a
suboptimal target configuration after faults oc‐
cur. A facilitator agent was developed to act as a
manager for the decision process. The existence
of the facilitator agents make the methodology
centralized. Liu et al [37] put forward another
restoration method for the power system. How‐
ever, this method is also centralized because the
restoration decision is made with the help of
coordinating agents that have global information
in the MAS.
Nagata et al [50] improved the method proposed
in [22]. In the MAS proposed in [50], the coordi‐
nation functions were distributed to several fa‐
cilitator agents instead of one facilitator agent.
The facilitator agents coordinate with each other
autonomously. However, each facilitator agent
works as centralized coordinator in the local
area. So the MAS proposed in that work is not
completely decentralized. The proposed restora‐
tion can only be applied to a radial power sys‐
tem. Also, the reconfiguration method was
tested on a small power system simulated on a
PC. The agents’ performance in the restoration
for a large power system was not provided.
Wang et al [24] proposed a fuzzy logic and evolu‐
tionary programming based reconfiguration
methodology for distribution systems. In this
MIMET Technical Bulletin Volume 1 (2) 2010
research, a fuzzy mutation controller is imple‐
mented to adaptively update the mutation rate
during the evolutionary process. The objective of
the reconfiguration is to reduce the power loss
in the distribution system. Zhou et al [25] put
forward another fuzzy logic based reconfigura‐
tion methodology for distribution system. A
fuzzy logic based reconfiguration was developed
for the purpose of restoration and load balanc‐
ing in a real‐time operation environment. Kuo
and Hsu [26] proposed a service restoration
methodology using fuzzy logic approach. In this
research, the fuzzy logic based approach was
estimated the loads in a distribution system and
devised a proper service restoration plan follow‐
ing a fault.
Various methods have been applied to the re‐
configuration process of the terrestrial power
system. However, most of the reconfiguration
methodologies are centralized. A central control‐
ler is a requirement to gather data from the
power system, make reconfiguration decisions
after calculation and analysis.
Shipboard Power System Reconfiguration
Compared to the terrestrial power systems, the
SPS has its unique characteristics. Based on the
unique characteristics of the SPS, some recon‐
figuration methods have been proposed. Some
of the significant literature of the SPS reconfigu‐
ration process of the SPS is reviewed below.
Butler and Sarma [27] propose a heuristics based
general reconfiguration methodology for
AC radial SPSs. The reconfiguration process is
applied to the SPS for service restoration. The
reconfiguration process is based on the initial
configuration and desired configuration details
of the system, such as the list of load con‐
nected /disconnected to the SPS, list of available
component (cables, circuit breakers, etc) in the
SPS, etc.
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