Research Project Proposal. for PhD Thesis

Transcription

1 NTNU Norwegian University of Science and Technology Faculty of Engineering Science and Technology Department of Marine Technology Research Project Proposal for PhD Thesis Title: ENERGY MANAGEMENT OF MARINE ELECTRICAL POWER SYSTEMS CONTROL OF INTEGRATED, AUTONOMOUS POWER SYSTEMS Candidate: DAMIR RADAN Supervisor: Co-advisors: Professor Alf Kåre Ådnanes, Department of Marine Technology Professor Asgeir J. Sørensen, Department of Marine Technology Professor Tor Arne Johansen, Department of Engineering Cybernetics Trondheim, Norway August 2004

2 1 Background This thesis is part of NTNU project ENERGY-EFFICIENT ALL ELECTRIC SHIP (EE-AES) - Faculty of Engineering Science and Technology, Department of Marine Technology and Faculty of Information Technology, Mathematics and Electrical Engineering, Department of Electrical Power Engineering in cooperation with Aker Elektro, ABB Marine, Marintek, Brunvoll, Smart Motor, Norpropeller. Electric propulsion will provide better vessel manouverability, system redundancy and higher flexibility with engine room arrangement, Ådnanes (2003). On vessels where there is a large variation in load demand reduced fuel consumption and optimal power/energy management may be regarded as advantages that are still not fully utilized. In that respect, the new equipment and modern control systems can provide new possibilities for improving present control strategies, performance, and utilization of the installation. It is also expected that an improved control system should provide overall higher level of safety and reliability. The present state of the art type of tools and methods for analyzing combined power systems does only to a limited extent utilize the possibilities for increased knowledge available in the more advanced models and methods developed and used within each of the machinery and electrical engineering disciplines. To be able to analyze increasingly more complex systems of interest, the ability to easily combine models and methods to develop more fundamental insight into the total systems behavior, its characteristics and limitations will be an advantage in design of new systems. According to that it is first necessary to design the power system simulation model which should include mathematical models of electrical and mechanical machinery components to the required level of complexity, Knowles (1990), Ordys et al. (1994), Hansen et al. (1998), Hansen, et al. (2000). Power system simulation model should be used to explore possibilities of energy management system (EMS) as the control from the highest level and to provide means of developing methods of localoptimization and plant control, Sørensen (2004a). Various kinds of simulations should be performed and various algorithms should be tested and verified with the aim of such model, including fault-tolerant control, Blanke et al. (2003). Optimization control should provide means of optimal allocation and distribution of energy onboard ship, Sørensen (2004b), Ådnanes (1999). Optimization algorithm should be part of the Energy Management System (EMS), i.e. control system which will be designed to meet requirements of various operational and environmental conditions. Defining the optimization constraints is the special task of this thesis. Nowadays, various machinery limits and constraints are not properly defined onboard. That might be one of the main reasons for operational difficulties which exist with these highly automated complex electrical systems. That is why risk analysis (RA) and condition based monitoring (CBM) should be applied in optimization algorithm to the proper extent, Leira et al. (2002). Also possibilities to apply onboard diagnosing methods and tools used in automotive and aerospace industry should be explored. The main goal of that part of the research is to improve the resistance to major machinery faults, trips and blackout, and also to improve overall safety and reliability. The last part of the thesis should be to design model based prediction control (MBPC) Clarke et al. (1987) Camacho et al. (1995), Hansen (2000). It is expected that MBPC control should further improve the resistant to faults, safety, reliability and durability of all machinery components, as well as to decrease overall operational costs. 2 Objectives Optimum operation and control of the power system are essential for safe operation with minimum operational costs (fuel consumption). In the next generation systems, marine machinery systems and

3 the control management systems are an integrated part of the system to an even higher level than found today. The main objective is to develop higher-level energy management system (EMS) which monitors and has the overall control functionality of the power system and which will be the integrating element in a totally integrated power, automation, and positioning system. EMS should provide means of optimal allocation and distribution of energy onboard ship subject to operating conditions, safety requirements and operational availability. The high-level EMS have to be well integrated functionally with low level controllers which interacts with producers and consumers of electrical energy, as well as control and protection of the distribution system. A key activity will be to develop and establish a complete power system simulation model for overall system performance verifications. Such a model can, to a large extent, be based on models (modules) already available at the various research groups, but there is a need to adapt and combine the various models into one unified system model, as described in Sørensen et al. (2003). A main challenge in this respect is to create a functional and modular system model where all critical components are adequately represented. This means that each type of component in the system, ranging from thermal engines and mechanical drive trains to purely electrical loads and converters, is represented by models with the same level of complexity from an integrated system point of view. The system model will serve a number of purposes, including: Design and testing of the overall control system including Energy management system and power management system (EMS/PMS) and individual controllers for the local power consumers and power generating units. The targeted ships and applications will be selected in cooperation with ABB Marine. Design of failure mode and effect analysis (FMEA) module. Design of optimization algorithms which can be utilized to optimize steady state performance, but also to include analysis of dynamic behavior in extreme operational cases for the total system including low level control, plant control and energy management systems (local optimization). Development and testing of the model based prediction control (MBPC) algorithm for hardware in the loop simulation. Development of fault-tolerant control which would include onboard diagnostic methods and tools, similar to those being used in automotive and aerospace industry. 3 Scope The work to be carried out can be summarized as follows: Specify basic and overall requirements and characteristics of present and expected future marine electrical power and propulsion systems. Study the characteristics and utilization possibilities of present control strategies and power management system (PMS) levels. Explore the possibilities for extending present power management systems to energy management systems (EMS). Design the power system simulation model for the overall system performance verifications. Design power systems module which can be part of the multy-disciplinary Marine Cybernetics Simulator (MC-Sim) developed on the Department of Marine Technology, NTNU, Trondheim, Norway. Design dynamic optimization algorithm stationary and transient states. Explore present optimization philosophy regarding fuel consumption and power/energy management. Improve goal function and optimization constraints according to increased knowledge available in the

4 more advanced models and methods developed and used within each of the machinery and electrical engineering disciplines. Design fault-tolerant control algorithm. Analyze the optimization constraints regarding to safety control limits. Apply risk analysis (RA) and condition based monitoring (CBM) in the optimization algorithm. Identify the weak areas of present control strategies regarding safety and reliability. Improve the resistance to major machinery faults, trips and blackout. Use dynamic settings to move safety control limits. Explore the possibilities to apply onboard diagnosing methods and tools used in automotive and aerospace industry. That would further improve fault-tolerant control and optimization with respect to energy management. Design failure mode and effect analysis module (FMEA), which will be used to test control system performance. Develop the methods to decrease power oscillation on the network by use of advanced optimization methods. Explore the possibilities of energy management system to prevent machinery overload based on integrated vessel risk analysis and overall reliability. Develop the methods for hardware in the loop simulation. 4 Research Methodology 4.1 Modeling All machinery power consumers and power producers will be modeled to the required level of complexity. It may be hard to predict which level can be suitable for the required tasks defined under objectives and scope. Modular principle in simulation descibed in Sørensen et al. (2003) will provide adequate level during the process since some older simple modules can be substituted with new ones which have higher level of complexity. Therefore, mathematical modeling of power systems should be performed iteratively. Proper fault simulation and FMEA would require models where, besides electrical machinery models, more complex models of diesel engines and/or gas turbines may be required. In that respect some simple models can be substituted with more accurate models, which should provide more variables that could be optimized, or which could change feasible region in the real-time optimization process. MATLAB/SIMULINK is the most suitable software for all required tasks. 4.2 Control system design Control system design is the final task and the last process in the thesis. It can be achieved after complete analysis and research of the different control strategies and optimization approaches. Design of control system is specific task since control system represents the highest level of control onboard ship. Therefore, it is a combined process with modeling and simulations. 4.3 Simulations A vessel simulator, called Marine Cybernetics Simulator (MCSim) is being developed as part of the project work, in cooperation with related activities at the Dept. of Marine Technology and the Dept. of Engineering Cybernetics at NTNU, Sørensen et al. (2003). The simulator will be used in testing of vessel models and vessel control systems, as well as thruster models, local thruster control systems and other relevant applications. The simulator will be highly modular, and is intended for use by students and employees at NTNU. Modules developed by other students/employees will be integrated in the simulator. MCSim will also be used for rapid control prototyping, in order to generate control code to be used in the model tests. MCSim currently consists of the following modules:

5 Environmental module, containing a linear wave model, a surface current model and a wind model including gust. Vessel dynamics module, consisting of a LF and a WF model. The LF model is based on the standard 6DOF vessel dynamics, with load models from wind, current and wave-drift. The WF model is based on standard motion transfer functions. Thruster module, containing a simple thrust allocation routine for non-rotating thrusters, thruster dynamics and local thruster control. Currently, only a ducted main propeller is implemented. The dynamics consists of a nominal 4 quadrant thruster model, as well as loss model due to wave- and current-induced inline and transverse water velocities, ventilation and water exit. The local thruster control schemes are speed, torque and power control, and also preliminary versions of an anti-spin system. Vessel control module, consisting of a nonlinear output feedback PID controller for DP. Proposed further work is: Electrical power system simulation model. Various machinery simulation models including non-electrical 4 stroke diesel engine, gas-, steam-turbine, combined power plant, etc. Control system module for energy/power management systems (EMS/PMS). Optimization algorithms for energy management systems (EMS). MBPC model based prediction control algorithm. 5 Expected results Developing new and complex machinery systems should be carefully analyzed using relevant theoretical methods for performance, safety and reliability. The types of the future required methods for analysis are not only to optimize steady state performance, but also to include analysis of dynamic behavior in extreme operational cases for the total system including low level control and energy management systems. However, the present state of the art type of tools and methods for analyzing such combined systems does only to a limited extent utilize the possibilities for increased knowledge available in the more advanced models and methods developed and used within each of the machinery and electrical engineering disciplines. To be able to analyze increasingly more complex systems of interest, the ability to easily combine models and methods to develop more fundamental insight into the total systems behavior, its characteristics and limitations will be an advantage in design of new systems. The safety and automation system required to monitor and control the power plant, propulsion and thruster system, and the process plant, becomes of increasing importance for a reliable and optimum use of the installation, Sørensen et al. (1997). The interconnecting point for all installed power equipment is the power distribution system. By starting and inrush transients, load variations, and network disturbances from harmonic effects the load and generators are interacting and influencing each other. Optimum operation and control of the power system is essential for safe operation with a minimum of fuel consumption. Energy control system (energy management system) monitors and has the overall control functionality of the power system. The trend is towards hierarchy-implemented control, monitoring, and protection systems of the power and propulsion plant systems, where physical and functional integration is a vital design philosophy, Ådnanes et al. (1998), Ådnanes (1999), Sørensen (2004a) and Sørensen et al. (2003). The system level controllers are implemented in control stations or PLCs. They can be centralized or distributed computers, depending on design philosophy for the vessel. In these one will find the energy management functions, such as power management, blackout prevention functions, start-up and reconfiguration sequence control. Due to the need for separate testing and clear responsibility, there will be a need for low level fast-response control, monitoring, and protection of devices and

6 components. Here are the fast control functions and safety functions implemented. These are linked to the system control level by hard-wired or field-bus signal interface. The purpose of the Power Management System (PMS) is to ensure that there is sufficient available power for the actual operating condition. This is obtained by monitoring the load and status of the generator sets and the power system. While the PMS only consider the power flow, the Energy Management System (EMS) monitors and controls the energy flow in a way that utilizes the installed and running equipment with optimum fuel efficiency, Sørensen (2004a). The energy management system s main functions can be grouped in: Power generation management Load management Distribution management The new generation production vessels and also drill ships/rigs have a complex power system configuration with advanced protection and relaying philosophies. There are close connections between the functional design and performance of the energy control system (power management system) and the power protection system functions. A special responsibility lies with the system integration responsible, and experience has shown that it might be a challenging work to coordinate the overall system robustness and optimization, Mariani et al. (1997). 6 Work plan 6.1 Schedule Project start: Project termination: Coursework Modeling Control system designs Simulation Thesis writing Dissertation Reporting The research will be documented in a doctoral thesis as well as in papers published in international conferences and journals. 6.3 Advisors Supervisor: Professor Alf Kåre Ådnanes, Department of Marine Technology Advisors: Professor Asgeir J. Sørensen, Department of Marine Technology Professor Tor Arne Johansen, Department of Engineering Cybernetics

7 6.4 List of courses Compulsory courses: Course Course title Exam Course Credits number period type TK8103 Advanced Nonlinear Systems 2004+H DR 7.5 TK8101 Optimal Control of Dynamics Systems 2006+V? DR 7.5 TET4190 Power Electronics for Renewable Energy 2003 H? ORD 7.5 TK 8102 Nonlinear Observer Design 2006+V? DR 7.5 Self study courses: TMR Marine Control Systems TMA Linear Methods TTK Linear System Theory 7 References Ådnanes, A.K. (2003). Maritime Electrical Installations and Diesel Electric Propulsion, Tutorial Report, ABB AS Marine. Ådnanes, A. K. (1999). Optimization of Power and Station Keeping Installations by a Total System Design Approach. Dynamic Positioning Conference DPC, October 1999, Houston. Ådnanes, A. K., A. J. Sørensen and T. Lauvdal (1998). Variable Speed Thruster Drives: The Means to Utilize the Features of a Fully Integrated Electrical Power, Automation and Positioning System for the Marine, Oil and Gas Market. Proceedings of Second International Conference on Diesel Electric Propulsion (DEP 98), pp. 1-22, Helsinki, Finland. Blanke, M., M. Kinnaert, J. Lunze and M. Staroswiecki (2003). Diagnostics and Fault-Tolerant Control. Springer-Verlag, Berlin, Germany. Camacho, E.F. and C. Bordons (1995). Model Predictive Control in the Process Industry. Advances in Industrial Control. Springer-Verlag. Clarke, D.W., C. Mohtadi and P.S. Tuffs (1987). Gereralized Predictive Control. Automatica 23(2), Hansen, J.F., A.K. Ådnanes and T.I. Fossen (1998). Modelling, simulation and multivariable model-based predictive control of marine power generation system. Proceeding of IFAC Conference: Control Applications in Marine Systems, CAMS 98, pp , Fukuoka, Japan. Hansen, J.F., A.K. Ådnanes and T.I. Fossen (2000). Mathematical modelling of diesel-electric propulsion systems for marine vessels. Int. Journal of Mathematical and Computer Modelling of Dynamical Systems. Hansen, J.F. (2000). Modeling and Control of Marine Power Systems, PhD thesis, Report 2000:9-W, Department of Engineering Cybernetics, the Norwegian University of Science and Technology- NTNU, Trondheim, Norway.

8 Knowles, J.B. (1990). Simulation and Control of Electrical Power Stations. John Wiley & Sons. Leira, B. J., A. J. Sørensen and C. M. Larsen (2002). Reliability-Based Schemes for Control of Riser Response and Dynamic Positioning of Floating Vessels. In Proceedings of OMAE'02, paper OMAE , 21st International Conference on Offshore Mechanics and Arctic Engineering, Oslo, Norway. Mariani, E. and S.S. Murthy (1997). Control of Modern Integrated Power Systems. Advances in Industrial Control. Springer-Verlag. Ordys, A.W., A.W. Pike, M.A. Johnson, R.M. Katebi and M.J. Grimble (1994). Modeling and Simulation of Power Generation Plants. Advances in Industrial Control. Springer-Verlag. Sørensen, A. J. (2004a). Structural Issues in the Design of Marine Control Systems. Key Note Lecture, In Proceedings of the IFAC Conference on Control Applications in Marine Systems (CAMS2004), 7 9 July, Ancona, Italy. Sørensen, A. J. (2004b). Marine Cybernetics: Modeling and Control. Lecture Notes, Fourth Edition, UK-04-76, Department of Marine Technology, the Norwegian University of Science and Technology- NTNU, Trondheim, Norway. Sørensen, A. J., E. Pedersen and Ø. Smogeli (2003). Simulation-Based Design and Testing of Dynamically Positioned Marine Vessels. In Proceedings of International Conference on Marine Simulation And Ship Maneuverability, MARSIM'03, August 25-28, Kanazawa, Japan. Sørensen, A. J. and A. K. Ådnanes (1997). High Performance Thrust Allocation Scheme in Positioning of Ships Based on Power and Torque Control. Dynamic Positioning Conference, Dynamic Positioning Committee, Marine Technology Society, pp. 1-14, Session 9, Houston, Texas. Sørensen, A. J., A. K. Ådnanes, T. I. Fossen and J. P. Strand (1997). A New Method of Thruster Control in Positioning of Ships Based on Power Control. Proceedings of 4th IFAC Conference on Maneuvering and Control of Marine Craft (MCMC 97), pp , Brijuni, Croatia.