Optimized Operation of Interleaved Motor Drive Inverter by means of Model Predictive Control

This thesis studies the challenges and benefits of interleaving in the context of drive inverter systems for induction machines. For this purpose, interleaved inverter topologies are introduced and analyzed. A detailed modeling of the favored interleaved inverter system in combination with the anticipated load is conducted. An optimal control strategy in form of finite control set model predictive control is developed and evaluated in simulation. Several optimization goals for the drive systems, such as control quality, efficiency in terms of losses and hardware effort, and reduction of undesirable side-effects owing to inverter-feeding are considered. The acquirable performance is compared to conventional two-level inverter realizations. A real-time controller implementation and hardware-based proof of concept is carried out. Deviations between simulation results and hardware measurements are discussed. Guidelines for optimized controller and hardware designs, as well as for future research topics are provided

Design and Control of Power Converters 2019

In this book, 20 papers focused on different fields of power electronics are gathered. Approximately half of the papers are focused on different control issues and techniques, ranging from the computer-aided design of digital compensators to more specific approaches such as fuzzy or sliding control techniques. The rest of the papers are focused on the design of novel topologies. The fields in which these controls and topologies are applied are varied: MMCs, photovoltaic systems, supercapacitors and traction systems, LEDs, wireless power transfer, etc

Survey of FPGA applications in the period 2000 – 2015 (Technical Report)

Romoth J, Porrmann M, Rückert U. Survey of FPGA applications in the period 2000 – 2015 (Technical Report).; 2017.Since their introduction, FPGAs can be seen in more and more different fields of applications. The key advantage is the combination of software-like flexibility with the performance otherwise common to hardware. Nevertheless, every application field introduces special requirements to the used computational architecture. This paper provides an overview of the different topics FPGAs have been used for in the last 15 years of research and why they have been chosen over other processing units like e.g. CPUs

GPU Implementation of DPSO-RE Algorithm for Parameters Identification of Surface PMSM Considering VSI Nonlinearity

In this paper, an accurate parameter estimation model of surface permanent magnet synchronous machines (SPMSMs) is established by taking into account voltage-source-inverter (VSI) nonlinearity. A fast dynamic particle swarm optimization (DPSO) algorithm combined with a receptor editing (RE) strategy is proposed to explore the optimal values of parameter estimations. This combination provides an accelerated implementation on graphics processing unit (GPU), and the proposed method is, therefore, referred to as G-DPSORE. In G-DPSO-RE, a dynamic labor division strategy is incorporated into the swarms according to the designed evolutionary factor during the evolution process. Two novel modifications of the movement equation are designed to update the velocity of particles. Moreover, a chaotic-logistic-based immune RE operator is developed to facilitate the global best individual (gBest particle) to explore a potentially better region. Furthermore, a GPU parallel acceleration technique is utilized to speed up parameter estimation procedure. It has been demonstrated that the proposed method is effective for simultaneous estimation of the PMSM parameters and the disturbance voltage (Vdead) due to VSI nonlinearity from experimental data for currents and rotor speed measured with inexpensive equipment. The influence of the VSI nonlinearity on the accuracy of parameter estimation is analyzed

Measurements, Models, Systems and Design

531 s.

Efficient Computational Methodologies for Multi-Objective Optimization of Distributed Energy Resources (DER) Inverters

The paralleling of power converters connected to the grid for power-sharing is a widely used technique. In this context, the design framework for a low-cost, lightweight, compact, and high-performance optimum configuration is an open research problem. This thesis proposes an innovative Multi-Objective Hierarchical Optimization Design Framework (MO-HO-DF) for an Alternating Current (AC) grid interface with N interleaved H-bridges, each with M parallel ``to-be-determined'' switches, connected through coupling inductances (Lf). A total of eight Figures of Merit (FOMs) were identified for the design framework optimization. A rigorous model of the power electronic system is presented. Next, a highly computationally efficient algorithm for the estimation of the required frequency modulation ratio (mf) to meet current harmonic performance requirements for any given configuration is proposed. Then, the concept and implementation of the algorithm are presented for the MO-HO-DF. The effectiveness of the design optimization framework is demonstrated by comparing it to a base case solution. Finally, the design calculations are validated via Piecewise Linear Electrical Circuit Simulation (PLECS) software with manufacturer-provided Three-Dimensional (3D) power semiconductor models that include thermal modelling. In particular, when an H-bridge is interfaced with a single-phase grid, it requires controllers to regulate the voltages and currents in the system. In this context, the static optimization of controllers responsible for Direct Current (DC) bus voltage regulation and AC regulation, considering time-domain and frequency-domain behaviours, is an open research problem. Firstly, this thesis proposes a method to obtain FOMs with the use of inbuilt functions in MATLAB software. Then for the Type-II Proportional+Integral (PI) controller, a single-variable two-objective convex optimization is proposed. Next, for the Proportional+Multi-Resonant (PMR) controller, three-variable five-objective convex optimization is proposed. The design of the PMR controller is a multi-variable problem that can inherit the principles of a hierarchical framework and leverage the effect of a design variable on the final optimization result. Thus, the work on PMR controller design optimization is extended to a three-level hierarchical design framework and evaluates all six possible paths for optimization. Finally, enhanced macro-model-based MATLAB simulation results are provided to verify the performance of controller designs and generate statistical insights