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Medium-Voltage Direct-Current (MVDC) power system has been considered as the trending technology for future All-Electric Ships (AES) to produce, convert and distribute electrical power. With the wide employment of highrequency power electronics converters and motor drives in DC system, accurate and fast assessment of system dynamic behaviors , as well as the optimization of system transient performance have become serious concerns for system-level studies, high-level control designs and power management algorithm development. The proposed technique presents a coordinated and automated approach to determine the system adjustment strategy for naval power systems to improve the transient performance and prevent potential instability following a system contingency. In contrast with the conventional design schemes that heavily rely on the human operators and pre-specified rules/set points, we focus on the development of the capability to automatically and efficiently detect and react to system state changes following disturbances and or damages by incooperating different system components to formulate an overall system-level solution. To achieve this objective, we propose a generic model-based predictive management framework that can be applied to a variety of Shipboard Power System (SPS) applications to meet the stringent performance requirements under different operating conditions. The proposed technique is proven to effectively prevent the system from instability caused by known and unknown disturbances with little or none human intervention under a variety of operation conditions. The management framework proposed in this dissertation is designed based on the concept of Model Predictive Control (MPC) techniques. A numerical approximation of the actual system is used to predict future system behaviors based on the current states and the candidate control input sequences. Based on the predictions the optimal control solution is chosen and applied as the current control input. The effectiveness and efficiency of the proposed framework can be evaluated conveniently based on a series of performance criteria such as fitness, robustness and computational overhead. An automatic system modeling, analysis and synthesis software environment is also introduced in this dissertation to facilitate the rapid implementation of the proposed performance management framework according to various testing scenarios.

Shipboard Power System (SPS) is known as an independent controlled small electric network powered by the distributed onboard generation system. Since many electric components are tightly coupled in a small space and the system is not supported with a relatively stronger grid, SPS is more susceptible to unexpected disturbances and physical damages compared to conventional terrestrial power systems. Among different distribution configurations, power-electronic based DC distribution is considered the trending technology for the next-generation U.S. Navy fleet design to replace the conventional AC-based distribution. This research presents appropriate control management frameworks to improve the Medium-Voltage DC (MVDC) shipboard power system performance. Model Predictive Control (MPC) is an advanced model-based approach which uses the system model to predict the future output states and generates an optimal control sequence over the prediction horizon. In this research, at first, a centralized MPC is developed for a nonlinear MVDC SPS when a high-power pulsed load exists in the system. The closed-loop stability analysis is considered in the MPC optimization problem. A comparison is presented for different cases of load prediction for MPC, namely, no prediction, perfect prediction, and Autoregressive Integrated Moving Average (ARIMA) prediction. Another centralized MPC controller is also designed to address the reconfiguration problem of the MVDC system in abnormal conditions. The reconfiguration goal is to maximize the power delivered to the loads with respect to power balance, generation limits and load priorities. Moreover, a distributed control structure is proposed for a nonlinear MVDC SPS to develop a scalable power management architecture. In this framework, each subsystem is controlled by a local MPC using its state variables, parameters and interaction variables from other subsystems communicated through a coordinator. The Goal Coordination principle is used to manage interactions between subsystems. The developed distributed control structure brings out several significant advantages including less computational overhead, higher flexibility and a good error tolerance behavior as well as a good overall system performance. To demonstrate the efficiency of the proposed approach, a performance analysis is accomplished by comparing centralized and distributed control of global and partitioned MVDC models for two cases of continuous and discretized control inputs.

Model predictive control (MPC) for switched reluctance machine (SRM) drives is studied in this thesis. The objective is to highlight the benefits of implementing MPC to overcome the main drawbacks of SRMs and position them as an attractive alternative among electrical drives. A comprehensive literature review of MPC for SRM is presented, detailing its current trends as an application still at an early stage. The different features of MPC are highlighted and paired with the most challenging and promising control objectives of SRMs. A vision of future research trends and applications of MPC-driven SRMs is proposed, thus drawing a road-map of future projects, barriers to overcome and potential developments. Several important applications can take advantage of the improved features that SRM can get with MPC, especially from the possibility of defining a unified control technique with the flexibility to adapt to different system requirements. The most important cluster for SRM drives is the high- and ultrahigh-speed operative regions where conventional machines cannot work efficiently. SRMs with MPC can complement then the existing demand for electrical drives with high performance under challenging conditions. Three techniques based on the finite control set model predictive control (FCS-MPC) approach are developed out of the proposed road-map. The first one defines a virtual-flux current tracking technique that improves the existing ones in operating at different speeds and more than one quadrant operation. The method is validated for low- and high- power SRMs in simulations and diverse types of current waveform, making it easy to adapt to existing current shaping techniques. It is also validated experimentally for different operating conditions and robustness against parameter variation. The second technique proposed a predictive torque control that bases its model on static-maps, thus avoiding complex analytical expressions. It improves its estimation through a Kalman filter. The third technique uses a virtual-flux predictive torque control, similar to the first technique for current tracking. The techniques are validated at a wide speed range, thus evidencing superiority in performance without modification on the control structure. / Thesis / Doctor of Philosophy (PhD)

Engine control becomes increasingly important in newer cars. It is therefore interesting to investigate if a relatively new control method as Model Predictive Control (MPC) can be useful in engine control in the future. One of the advantages of MPC is that it can handle contraints explicitly. In this thesis basics on turbocharged engines and the underlying theory of MPC is presented. Based on a nonlinear mean value engine model, linearized at multiple operating points, we then implement both a linear and a nonlinearMPC strategy and highlight implementation issues. The implemented MPC controllers calculate optimal wastegate position in order to track a requested torque curve and still make sure that the constraints on turbocharger speed and minimum and maximum opening of the wastegate are fulfilled.

Multilevel inverters (MLI) have been widely applied in medium and high voltage applications for their advantages in high quality of output waveforms. Among various multilevel topologies, cascaded H-bridge (CHB) inverters have attracted more attentions for its modular structure, which simplifies the design and implementation. In addition, the modularity of CHB also expands diverse power ratings without many changes in the hardware setup. In a CHB inverter, the AC output voltage can be produced at different voltage levels depending on the number power cells that are cascaded at the output. To produce the AC output voltage, different modulation schemes and control algorithms have been studied and applied to the CHB inverter. Model predictive control (MPC) has been widely employed among all control algorithms in multilevel topologies due to their advantages such as good dynamic performance, multiple control targets, inclusion of nonlinearity, and flexibility to add more performance objectives. However, one disadvantage of the MPC is that the switching frequency is variable compared with other modulation schemes. Therefore, a new MPC method called modulated model predictive control (M2PC) has been researched to obtain a fixed switching frequency, which improves the harmonic spectrum of load currents and simplifies the filter design. In the modulated model predictive control, the mathematical model is obtained by electrical model of the system. It means that the operation of the M2PC algorithm relies on the accuracy of the given parameters and model. If there is an error in parameters and model, the performance of the control will be affected negatively. To solve this problem, modulated model-free predictive control (M2FPC) algorithm has been introduced. With this method, the mathematical model is established with measured values instead of given values and model. Reliability is one of the most important issues in the design of power converters. However, the failure of power switches will lead to the distortion of load currents and voltage waveforms. Also, the distortion in load currents and voltage waveforms causes power imbalance between faulty and healthy phases. To reduce the negative effects of IGBT failure in power converters, the faulty power cells should be found and isolated. Therefore, fault detection and localization algorithm (FDL) should be introduced to detect the fault in power converters and localize the faulty power switches. FDL algorithm based on the given M2PC scheme is proposed in this thesis for the CHB inverter to make the system more reliable. The FDL algorithm utilizes the phase voltages and load currents to detect the open fault in the CHB inverter and localize the single and multiple open switches by measuring the expected and actual phase voltages. With the faulty information, the faulty power cell can be isolated, and the fault-tolerant control can be applied to make the system work normally even though there is an open fault. In this thesis, without losing the generality, a seven-level CHB inverter is considered where there are three power cells in each phase. The M2PC algorithm was introduced to obtain the fixed switching frequency with the design of possible voltage vector set and carrier phase-shifting modulation. Based on the proposed M2PC algorithm, the FDL algorithm is designed to detect and localize the open switches to improve the system reliability. The theoretical analysis and simulation results validate the feasibility of the proposed M2PC algorithms and open fault diagnosis scheme. All possible open-circuit scenarios in power cells are discussed and the M2PC-based FDL algorithm has been verified. Experimental results verify the feasibility of the proposed M2PC. The experimental result of M2PC algorithm is presented to verify its operation. Also, diverse open scenarios can be diagnosed in the experiments. / Thesis / Master of Applied Science (MASc)

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第24743号 / 情博第831号 / 新制||情||139(附属図書館) / 京都大学大学院情報学研究科システム科学専攻 / (主査)教授 大塚 敏之, 教授 加納 学, 教授 東 俊一 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

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