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Abstract: A Permanent Magnet Synchronous Generator (PMSG) based Wind Energy Conversion System (WECS) holds significant importance in the realm of Renewable Energy Sources (RES) for several reasons. The permanent magnets in the generator eliminate the need for a separate excitation system, leading to improved efficiency in power conversion. This makes PMSG-based WECS an effective and reliable source of wind energy electricity. The motivation behind the proposed conceptual framework stems from the need to overcome the limitations related to the integration of RES into the power grid, specifically focusing on voltage stability and Low Voltage Ride Through (LVRT) capability of PMSG based WECS. A Dynamic Voltage Restorer (DVR), empowered by an energy storage device, is used to mitigate voltage fluctuations and disturbances. The input DC voltage to the DVR is intricately regulated by a Type 2 Adaptive Neuro Fuzzy Inference System (ANFIS) Controller optimized using the Seagull algorithm, exhibiting intelligent adaptability to dynamic conditions. The rectified output from the WECS transforms an Isolated Flyback converter. Subsequently, a 31-Level Cascaded H-Bridge Multilevel Inverter (CHBMLI) along with a Proportional-Integral (PI) controller aids in generating high-quality AC output. By addressing challenges related to voltage stability and the ability to ride through low-voltage conditions, the proposed work contributes to enhanced grid stability. The use of advanced control techniques, including the Type 2 ANFIS Controller optimized by the Seagull algorithm, adds a layer of intelligent adaptability to changing environmental and grid conditions. A lower Total Harmonic Distortion (THD) Value of 1.29% is shown during the validation of the created system utilizing MATLAB/Simulink, assuring significant LVRT capabilities.
Keywords: Permanent Magnet Synchronous Generator (PMSG), Wind Energy Conversion System (WECS), Renewable Energy Sources (RES), Low Voltage Ride Through (LVRT), Type 2 Adaptive Neuro Fuzzy Inference System, 31-Level CHBMLI, Proportional-Integral (PI)
Published in DKUM: 06.11.2025; Views: 0; Downloads: 2
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Abstract: Axial flux permanent magnet machines have garnered significant attention in recent years due to their numerous advantages in various applications, including electric vehicles, wind turbines, and robotics. However, one of the critical challenges associated with these machines is the presence of cogging torque, which can hinder their efficiency and performance. This review article provides a comprehensive overview of the state-of-the-art techniques employed for cogging torque reduction in Axial Flux Permanent Magnet Machines. Different techniques are described, encompassing geometric optimization, magnet placement, and skewing methods. Firstly, the significance of Axial Flux Permanent Magnet Machines is described, as well as the issue of the cogging torque. In the methods section, a review of the strategies for the reduction of cogging torque is described from various articles, and finally, in the discussion section, a list of actions is presented for cogging torque reduction for different topologies. The novelty of the study is that it combines strategies for cogging torque reduction in a single article.
Keywords: cogging torque, axial flux permanent magnet machines, geometric optimization, magnet placement strategies, skewing techniques
Published in DKUM: 06.11.2025; Views: 0; Downloads: 3
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Abstract: This Dissertation focuses on the optimization of an Interior Permanent Magnet (IPM) machine for handheld battery-powered tools, aiming to enhance performance and efficiency. The research integrates multi-physics modeling, including electromagnetic Finite Element Method (FEM) and thermal models, to evaluate machine performance under various operating conditions. The performance is evaluated according to selected Key Performance Indicators (KPIs). Further, different control methods, such as Field Oriented Control and Square-Wave Control, impact the performance significantly and are incorporated into the optimization process. Due to the computational challenges of FEM-based performance evaluations in Multi-Objective Optimization (MOO), this work utilizes Artificial Neural Network (ANN)-based meta-models, to accelerate the optimization process while preserving accuracy. The developed meta-models capture nonlinear machine characteristics from the FEM model. These meta-models are then used to evaluate machine performance through a combination of analytical and numerical post-processing methods. Four MOO scenarios are presented, each aimed at optimizing the cross-sectional design of IPM machines, to enhance performance and efficiency while reducing mass and cost. Additionally, these scenarios modify the machine’s electromagnetic behavior, to ensure better alignment with the selected control method. By comparing the optimization process of Scenario 1, which uses direct FEM-based evaluation without time reduction measures, to the approach incorporating Artificial Neural Network based meta-models, the total number of individual FEM evaluations decreased from 2.35×10^9 to 2.03×10^5, without almost any loss of accuracy. This reduced the computation time from 297 years to 9.07 days on our standard desktop computer. The obtained ANN-base meta-models can be used further for other optimizations without the need for additional FEM evaluations. In all four optimization scenarios, the use of meta-models enabled the generation of a Pareto front of the optimal solutions, leading to improved KPIs compared to the reference design. The highest relative improvement occurred in Scenario 1, where the selected optimized machine design achieved a 30% increase in power density compared to the reference design.
Keywords: Interior Permanent Magnet (IPM) Machine, Artificial Neural Network (ANN), Meta-Modeling, Multi-Objective Optimization (MOO), Finite Element Method (FEM), Multi-Physics Modeling, Field Oriented Control (FOC), Square-Wave Control (SWC)
Published in DKUM: 15.05.2025; Views: 0; Downloads: 148
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Abstract: The Finite Element Method (FEM) is a prominent analysis approach. Although it is applicable for simulation and optimization of electrical machines, FEM is a very time-consuming technique. One of the approaches to shorten the optimization runtime is the use of surrogate models instead of FEM. In this paper, the design and optimization of an outer rotor permanent magnet synchronous motor for a hybrid vehicle are investigated. Response surface methodology (RSM) with four input variables is integrated with a sequential quadratic programming algorithm for optimization. Before the optimization, the performance of the surrogate model in the prediction of untried points is validated. Finally, the optimal motor is simulated by FEM to verify the results of RSM-based optimization, and the outputs of both models are compared.
Keywords: response surface surrogate method, outer rotor permanent magnet synchronous motor, sequential quadratic programming optimization
Published in DKUM: 05.12.2023; Views: 512; Downloads: 282
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Abstract: This article proposes a methodology for the design of double-sided coreless axial flux permanent magnet synchronous machines, which is based on a developed model for calculating the axial component of the magnetic flux density in the middle of the distance between opposite permanent magnets, which also represents the middle of the stator. Values for different geometric parameters represent the input data for the mathematical model in explicit form. The input data are calculated by using a simplified finite element method (FEM), which means that calculations of simplified 3D models are performed. The simplified model consists of two rotor disks with surfacemounted permanent magnets and air between them, instead of stator windings. Such a simplification is possible due to similar values of permeability of the air and copper. For each simplified model of the machine the axial component of the magnetic flux density is analyzed along a line passing through the center of opposite permanent magnets and both rotor disks. Values at the middle of the distance between the opposite permanent magnets are the lowest and are therefore selected for the input data at different stator, rotor disks and permanent magnets (PM) thicknesses. Such input data enable the model to consider the nonlinearity of materials.
Keywords: axial flux, analysis, coreless, development, design, explicit form, finite element method, mathematical models, permanent magnet, synchronous
Published in DKUM: 01.12.2023; Views: 498; Downloads: 40
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