Authors: Wei Zhang; Weifeng Shi; Jinbao Zhuo
Addresses: Department of Electrical Engineering and Automation, Shanghai Maritime University, 1550 Haigang Ave, Shanghai 201306, China ' Department of Electrical Engineering and Automation, Shanghai Maritime University, 1550 Haigang Ave, Shanghai 201306, China ' Department of Electrical Engineering and Automation, Shanghai Maritime University, 1550 Haigang Ave, Shanghai 201306, China
Abstract: This paper presents a BDI (Belief-Desire-Intention)-agent-based Quantum-Behaved Particle Swarm Optimisation (QPSO) reconfiguration method for shipboard zonal power systems. Shipboard zonal power systems are founded on navy ships, and desired to be highly reconfigurable. Since shipboard power system reconfiguration may change its topology, and load priority should be taken into consideration, this makes shipboard reconfiguration into a non-linear distributed optimisation problem. Specially, switches in the shipboard zonal power system are modelled as intelligent BDI agents. This paper uses swarm intelligence to realise BDI-agent reasoning and optimise its reconfiguration objective. To verify the effectiveness of the proposed approach, comparative simulations are conducted on the method without reconfiguration and reconfiguration method based on PSO/QPSO. Simulation results show that the reconfiguration strategy can achieve success in realising shipboard power system service restoration cases which demonstrate the feasibility and the advantages of the proposed BDI-agent-based QPSO reconfiguration method.
Keywords: shipboard power systems; zonal distribution; service restoration; BDI agents; belief desire intention; quantum-behaved PSO; QPSO; particle swarm optimisation; power system reconfiguration; agent-based systems; multi-agent systems; MAS; modelling; intelligent agents; swarm intelligence; metaheuristics; simulation; shipping; maritime industry.
International Journal of Computer Applications in Technology, 2017 Vol.55 No.1, pp.4 - 11
Received: 10 Apr 2015
Accepted: 11 Oct 2015
Published online: 14 Feb 2017 *