Multiagent autonomous energy management

The objective of this thesis is to design distributed software agents for reliable operation of integrated electric power systems of modern electric warships. The automatic reconfiguration of electric shipboard power systems is an important step toward improved fight-through and self-healing capabilities of naval warships. The improvements are conceptualized by redesigning the electric power system and its controls. This research focuses on a new scheme for an energy management system in the form of distributed control/software agents. Multiagent systems provide an ideal level of abstraction for modeling complex applications where distributed and heterogeneous entities need to cooperate to achieve a common goal. The agents\u27 task is to ensure supply of the various load demands while taking into consideration system constraints and load and supply path priorities. A self-stabilizing maximum flow algorithm is investigated to allow implementation of the agents\u27 strategies and find a global solution by only considering local information and a minimum amount of communication. (Abstract shortened by UMI.)

Software tools for real-time simulation and control

The objective of this thesis is to design and simulate a multi-agent based energy management system for a shipboard power system in hard real-time environment. The automatic reconfiguration of shipboard power systems is essential to improve survivability. Multi-agent technology is used in designing the reconfigurable energy management system using a self-stabilizing maximum flow algorithm. The agent based energy management system is designed in a Matlab/Simulink environment. Reconfiguration is performed for several situations including start-up, loss of an agent, limited available power, and distribution to priority ranked loads. The number of steps taken to reach the global solution and the time taken are very promising. With the growing importance of timing accuracy in simulating control systems during design and development, there is an increased need for these simulations to run in a real-time environment. This research further focuses on software tools that support hard real-time environment to run real-time simulations. A detailed survey has been conducted on freely available real-time operating systems and other software tools to setup a desktop PC supporting real-time environment. Matlab/Simulink/RTW-RTAI was selected as real-time computer aided control design software for demonstrating real-time simulation of agent based energy management system. The timing accuracy of these simulations has been verified successfully

Research and development of diagnostic algorithms to support fault accommodating control for emerging shipboard power system architectures

The U.S. Navy has proposed development of next generation warships utilising an increased amount of power electronics devices to improve flexibility and controllability. The high power density finite inertia network is envisioned to employ automated fault detection and diagnosis to aid timely remedial action. Integration of condition monitoring and fault diagnosis to form an intelligent power distribution system is anticipated to assist decision support for crew while enhancing security and mission availability. This broad research being in the conceptual stage has lack of benchmark systems to learn from. Thorough studies are required to successfully enable realising benefits offered by using increased power electronics and automation. Application of fundamental analysis techniques is necessary to meticulously understand dynamics of a novel system and familiarisation with associated risks and their effects. Additionally, it is vital to find ways of mitigating effects of identified risks. This thesis details the developing of a generalised methodology to help focus research into artificial intelligence (AI) based diagnostic techniques. Failure Mode and Effects Analysis (FMEA) is used in identifying critical parts of the architecture. Sneak Circuit Analysis (SCA) is modified to provide signals that differentiate faults at a component level of a dc-dc step down converter. These reliability analysis techniques combined with an appropriate AI-algorithm offer a potentially robust approach that can potentially be utilised for diagnosing faults within power electronic equipment anticipated to be used onboard the novel SPS. The proposed systematic methodology could be extended to other types of power electronic converters, as well as distinguishing subsystem level faults. The combination of FMEA, SCA with AI could also be used for providing enhanced decision support. This forms part of future research in this specific arena demonstrating the positives brought about by combining reliability analyses techniques with AI for next generation naval SPS.The U.S. Navy has proposed development of next generation warships utilising an increased amount of power electronics devices to improve flexibility and controllability. The high power density finite inertia network is envisioned to employ automated fault detection and diagnosis to aid timely remedial action. Integration of condition monitoring and fault diagnosis to form an intelligent power distribution system is anticipated to assist decision support for crew while enhancing security and mission availability. This broad research being in the conceptual stage has lack of benchmark systems to learn from. Thorough studies are required to successfully enable realising benefits offered by using increased power electronics and automation. Application of fundamental analysis techniques is necessary to meticulously understand dynamics of a novel system and familiarisation with associated risks and their effects. Additionally, it is vital to find ways of mitigating effects of identified risks. This thesis details the developing of a generalised methodology to help focus research into artificial intelligence (AI) based diagnostic techniques. Failure Mode and Effects Analysis (FMEA) is used in identifying critical parts of the architecture. Sneak Circuit Analysis (SCA) is modified to provide signals that differentiate faults at a component level of a dc-dc step down converter. These reliability analysis techniques combined with an appropriate AI-algorithm offer a potentially robust approach that can potentially be utilised for diagnosing faults within power electronic equipment anticipated to be used onboard the novel SPS. The proposed systematic methodology could be extended to other types of power electronic converters, as well as distinguishing subsystem level faults. The combination of FMEA, SCA with AI could also be used for providing enhanced decision support. This forms part of future research in this specific arena demonstrating the positives brought about by combining reliability analyses techniques with AI for next generation naval SPS

Design and real-time control of shipboard power system testbed

The objective of this thesis is to design and test a small scale testbed for the all-electric shipboard power distribution system. Shipboard power system is increasingly becoming more reconfigurable, and multi-agent systems are developed to automate routine operation and emergency reconfiguration. Underlying algorithms of these systems have been verified using software simulation tools. However, these simulators run in soft realtime by using simple mathematical models to represent the physical system. These models do not incorporate every aspect of the physical system. A testbed provides a cost effective physical environment to verify these algorithms and control techniques in the real world. This testbed, based on the Navy\u27s notional all electric ship, keeps characteristic features of the Office of Naval Research\u27s Integrated Power System. It provides a platform for testing local and distributed controls. Local embedded controllers on the testbed run in hard real-time, and a CAN bus builds the communication networking among them. Performance of the controllers has been verified successfully, and the platform provides an environment that allows prototyping and testing agent-based higher-level controls and decision making entities

Gas turbine control and load sharing of a shipboard power system

The objective of this research is to design a controller for a gas turbine of an Electric Shipboard Power System (ESPS) and to develop a load sharing strategy for its energy management. A suitable model for the gas turbine is selected and the effects of the dynamics are investigated for the different loads of the ESPS. The gas turbine controller is a Proportional Integral Derivative (PID) controller, whose parameters are tuned using the Particle Swarm Optimization (PSO) technique. The load on the system has three components: a propulsion load, a pulsed load to simulate a high energy weapon system and a power supply load for the remaining loads such as pumps, lighting systems, etc. Load sharing is inevitable when demand exceeds the available power supply. In this case, based on the priorities of the loads and the available power, a strategy is presented to supply power to the most critical loads. To illustrate this, a load allocation algorithm is developed using stateflow diagrams. The potential of this algorithm is demonstrated by two case studies performed using the three loads, with the highest priority assigned to the propulsion load in case 1, and power supply load in case 2. The results of this research can be further extended to real time applications