Design Optimization and Analysis of Radial Flux Permanent Magnet Generator Using Local Optimization Method

This paper deals with the influence of AC-DC-DC converter on permanent magnet (PM) wind power generators. Differently from AC load conditions, since the phase current in PM generators with the converter contains harmonic components, their influence on generators' performance is investigated. In particular, in this paper, two PM generator types, radial flux PM generator and axial flux PM generator, are considered for the comparative investigation. The generators are connected to a mechanical energy storage system consisted of a permanent magnet synchronous motor with a heavy wheel, and the generator performance characteristics are dealt with while the mechanical energy storage system is being operated.

In this paper, the design for an electromagnetic structure and reduction cogging torque of a dual stator structured radial flux permanent magnet generator, which is a combination of the inner- and outer-rotor types, has been proposed. We call this a dual stator radial flux permanent magnet generator. To reduce the cogging torque, firstly, stator tooth pairing was designed; secondly, stator displacement was designed and and finally, stator tooth pairing and stator displacement were carried out simultaneously. We found the optimal design condition about stator tooth pairing angle combination and stator displacement angle for cogging torque minimization. As a result, a cogging was reduced by 93.3[%] by this study.

In this paper, the no-load operational characteristics of radial-flux permanent magnet generators with two contra-rotating rotors are addressed. Three different radial flux machine topologies have been qualitatively studied through a sequence of stationary two dimensional finite element field solutions. It is shown that the peak value of the induced phase electromotive force during synchronous operation depend on the angular positions of the two rotors relative a fixed reference in the stator winding.

The potential of wind energy resources is propelled fast and power extraction is increasing considerably due to the development of reliable and cost-effective wind turbine generators. Various permanent magnet (PM) wind generators have been implemented for wind power generation, among which, the conventional transverse-flux permanent magnet generator (TFPMG) is popular due to their higher torque density and simple coil design, but it has some disadvantages in comparison with radial flux PM generator, i.e., higher flux leakages and complex 3D flux pattern. In this article, a fall-back inner rotor transverse-flux PM wind generator FB-TFPMG has been investigated to override the disadvantages of conventional TFPMG. It employs half of the total PMs on the rotor and U-shaped stator cores. An analytical model of the proposed FB-TFPMG has derived using magnetic equivalent reluctance circuit (MERC) to predict the performance of the generator, i.e., flux densities in the air-gap, under aligned and unaligned conditions of rotor PMs with U-shaped stator cores. The results of the MERC model is verified with the results obtained through 3D finite element analysis.

Description: The implementation of well testing models for dynamic characterization of Naturally Fractured Vuggy Reservoirs (NFVR) commonly involves inverse modeling. Thus, this work introduces new results about dynamic description of NFVR by using a triple porosity-dual permeability model. The values of the inter-porosity flow parameters, storativity ratios, permeability ratio between fractures and vugs, wellbore storage coefficient, skin and total permeability, are automatically identified in Laplace Space by using a gradient Newton-type global optimization method named The Tunneling. This method offers more reliable results in comparison to the local optimization methods commonly used in parameters estimations. Applications: The precision and computational efficiency of the Tunneling Method are demonstrated through the reliable results obtained from the automatic analysis of synthetic examples, where a very comprehensive and large set of synthetic noisy data was used. In all cases, the Tunneling Method obtained optimal solutions. Likewise, the analysis of several field well test data cases recorded in NFVR are shown to confirm the benefits above mentioned. Results, Observations, and Conclusions: The parameters determination in the problem here addressed is non-unique due to the ill-posedness problem forcing the local optimization methods to stop in non-optimal solutions. Likewise, local methods require many realizations until converge into an optimal solution. Thus, the superiority of the Tunneling Method for overcoming these challenges is demonstrated through the reliable analysis of several synthetic and field data cases. Performing the optimization in the Laplace space makes the procedure very efficient in computing time. To moderate or eliminate the non-uniqueness problem, information about storativity ratios obtained from core or well log analysis, can be used through the optimization process. Significance of Subject Matter: Currently, the well test data analysis is the only available procedure to estimate the dynamic parameters which characterize NFVRs. Likewise, the approach introduced in this work allows obtaining more reliable estimations of these parameters than local optimization methods.

The demand for lightweight, thermally stable, and energy-efficient power systems in aerospace applications has prompted the investigation of advanced magnetic materials beyond traditional alloys, such as Hiperco 50. This study aims to address the limitations of high core loss, poor efficiency, and thermal instability in conventional aerospace generators. A high-speed (10,000 RPM, 500 Hz) radial flux permanent magnet generator (RFPMG) was designed using Finemet as the stator material, soft magnetic composites (SMC) for the rotor, and Litz wire windings to minimise eddy current and copper losses. Electromagnetic and thermal simulations were conducted using ANSYS Maxwell and Motor-CAD to ensure adherence to aerospace standards (IEC 60034-1, SAE ARP4990, IEEE 1812-2014). The results indicate that the proposed Finemet–SMC design achieves a 59% increase in output power (7.49 kW vs. 4.71 kW), an 11% improvement in efficiency (96.19% vs. 86.47%), and a 60% reduction in total losses, with stator and winding temperatures reduced by up to 45%. The torque ripple decreased by 44%, ensuring a smoother performance. These findings demonstrate that the proposed configuration not only surpasses standard aerospace requirements but also establishes a lightweight, cost-effective, and sustainable generator design suitable for next-generation hybrid and electrified aircraft propulsion systems.

In modern railway transportations, electric locomotives and electric multiple units(EMU) become increasingly popular due to the advantages of high efficiency, high speed, environment friendly, high reliability and low maintenance cost. However, electric locomotives and EMU require electric railways and the stable large capacity power supply. Thus, internal combustion (IC) locomotives and multiple units(MU)are still predominant in economically undeveloped countries and areas. Furthermore, considering electric line repair need for the electrified transportation and the national security and strategy, IC locomotives and MU are still promising in terms of market occupancy. Modern IC locomotives and MU use fuel oil to provide energy and are driven by electric traction, while the generator will generate electricity via diesel engine (diesel engine and the generator constructing the IC power pack). Electrically excited (EE) generators are adopted in conventional IC power packs. With the development of power electronics and control techniques, novel IC power packs with permanent magnet (PM) generators rise. Since PM generators have the merits of simple structure, high efficiency, small volume, low weight, high reliability and low maintenance cost, the next generation of IC power packs more and more adopt PM generators. This paper focuses on the PM generators used in novel railway transportation IC power packs. The comparison between conventional EE and PM generators, two rectification methods for PM generators and the research and applications of PM generators are discussed. All of these can provide the reference for novel IC power packs in railway transportation development and applications.

*† ‡ § An efficient and high-fidelity design approach is proposed by combining global and local optimization methods for wing planform and surface design. For enhanced design results, aerodynamic shape optimization process is carried out via 2-stage with different optimization strategy. In the first stage, global optimization techniques are applied to planform design with a few geometric design variables. In the second stage, local optimization techniques are used for wing surface design with a lot of design variables to maintain a sufficient design space with high DOF (Degree of Freedom) geometric change. For global optimization, meta-modeling techniques such as RS (Response Surface) and Kriging methods are used in conjunction with Genetic Algorithm (GA). For local optimization, a discrete adjoint variable method is used. By the successive combination of global and local optimization techniques, drag minimization is performed for a multi-body aircraft configuration while maintaining the baseline lift and the wing weight at the same time. Through the design process, performances of the test models are remarkably improved in comparison with the single stage design approach. The capability of proposed design framework including wing planform design variables can be evaluated by the drag decomposition method which can provide improvement of induced drag and wave drag, respectively.

Multivariate quartic polynomial optimization problems, as a special case of the general polynomial optimization, have a lot of practical applications in real world and are proved to be NP-hard. In this paper, some necessary local optimality conditions and some necessary global optimality conditions for quartic polynomial optimization problems with mixed variables are established. Then some local optimization methods, including weakly local optimization methods for general problems with mixed variables and strongly local optimization methods for quartic polynomial optimization problems with mixed variables, are proposed by exploiting these necessary local optimality conditions and necessary global optimality conditions. A global optimization method is proposed for quartic polynomial optimization problems by combining these local optimization methods together with some auxiliary functions. Some numerical examples are also given to illustrate that these approaches are very efficient.

In order to minimize the number of evaluations of high-fidelity (“fine”) model in the optimization process, to increase the optimization speed, and to improve optimal solution accuracy, a robust and computational-efficient multi-fidelity local surrogate-model optimization method is proposed. Based on the principle of response surface approximation, the proposed method exploits the multi-fidelity coarse models and polynomial interpolation to construct a series of local surrogate models. In the optimization process, local region modeling and optimization are performed iteratively. A judgment factor is introduced to provide information for local region size update. The last local surrogate model is refined by space mapping techniques to obtain the optimal design with high accuracy. The operation and efficiency of the approach are demonstrated through design of a bandpass filter and a compact ultra-wide-band (UWB) multiple-in multiple-out (MIMO) antenna. The response of the optimized design of the fine model meet the design specification. The proposed method not only has better convergence compared to an existing local surrogate method, but also reduces the computational cost substantially.

Features of several well-known global and local optimization methods were discussed and their robustness and efficiency were tested on several artificial test functions in Matlab environment. The tested local methods were the interior-point, the quasi-Newton method, Nelder–Mead simplex, the pattern search, the NEWUOA and the BOBYQA methods. The global methods were the genetic algorithm (GA), the simulated annealing (SA), the particle swarm optimization (PSO), and the covariance matrix adaptation evolutionary strategy (CMA-ES) methods (see subsections 2.2 and 2.3 for their details). Furthermore, a novel global optimization method, called FOCusing robusT Optimization with Uncertainty-based Sampling (FOCTOPUS), which proved to be very efficient in the optimization of constrained and highly correlated parameters of combustion kinetic models, was also tested. The test functions were selected in such a way that they had a variety of features: uni-modal and multi-modal, differentiable and non-differentiable, separable and non-separable, low dimensional and high dimensional. The following test functions were used: the 20D Alpine function, the 4D Ackley function, the Cross-in-tray 2D function, the Hartmann-6D function, the Holder table 2D function, the 5D Rastrigin function, the 5D Rosenbrock function, the 4D modified Rosenbrock function, the 20D Zakharov function and a typical 2D multi-modal function. The general conclusion here is that, the global methods performed well on the multi-modal and high-dimensional test functions while the local methods were superior in the case of low-dimensional and uni-modal test functions. For the highly multi-modal test functions, the GA was better than all the other methods. The FOCTOPUS method proved to be inferior to GA for most of the test functions, thus its application cannot be generally recommended.

With the improvement of vehicles electrical equipment, the existing silicon rectification generator and permanent magnet generator cannot meet the requirement of the electric power consumption of the modern vehicles electrical equipment. It is difficult to adjust the air gap magnetic field of the permanent magnet generator. Consequently, the output voltage is not stable. The silicon rectifying generator has the problems of low efficiency and high failure rate. In order to solve these problems, a new type of hybrid excitation generator is developed in this paper. The developed hybrid excitation generator has a double-radial permanent magnet, a salient-pole electromagnetic combined rotor, and a fractional slot winding stator, where each rotor pole corresponds to 4.5 stator teeth. The equivalent magnetic circuit diagram of permanent magnet rotor and magnetic rotor is established. Magnetic field finite element analysis (FEA) software is used to conduct the modeling and simulation analysis on double-radial permanent magnet magnetic field, salient-pole electro-magnetic magnetic field and hybrid magnetic field. The magnetic flux density mold value diagram and vector diagram are obtained. The diagrams are used to verify the feasibility of this design. The designed electromagnetic coupling regulator controller can ensure the stable voltage export by changing the magnitude and direction of the excitation current to adjust the size of the air gap magnetic field. Therefore, the problem of output voltage instability in the wide speed range and wide load range of the hybrid excitation generator is solved.

This paper proposes and describes a high power factor AC-AC converter for naval applications using Permanent Magnet Generator (PMG). The three-phase output voltages of the PMG vary from 260 Vrms (220 Hz) to 380 Vrms (360 Hz), depending on load conditions. The proposed converter consists of a Y-/ΔY power transformer, which provides electrical isolation between the PMG and remaining stages, and a twelve-pulse uncontrolled rectifier stage directly connected to a single-phase inverter stage, without the use of an intermediary DC-DC topology. This proposal results in more simplicity for the overall circuitry, assuring robustness, reliability and reduced costs. Furthermore, the multipulse rectifier stage is capable to provide high power factor and low total harmonic distortion for the input currents of the converter. The single-phase inverter stage was designed to operate with wide range of DC bus voltage, maintaining 120 Vrms, 60 Hz output. The control philosophy, implemented in a digital signal processor (DSP) which also contains protection routines, alows series connections between two identical converters, achieving 240 Vrms, 60 Hz total output voltage. Measured total harmonic distortion for the AC output voltage is lower than 2% and the input power factor is 0.93 at 3.6kW nominal load. © 2010 IEEE.

The study focuses on the design of an axial flux permanent magnet generator meant for low power electricity generation up to 100Watts. The design of the permanent magnet generator is made with open-source technology concept. This entails that the mechanical design is made in such a way that majority of materials and processes are readily available where the machine will be used, in this case in the rural areas where renewable source of energies are available such as wind and hydro. A prototype permanent magnet generator was made following the processes intended as an open-source technology. This entails some compromise in the performance of the generator as small tolerance cannot be achieved with simple manufacturing processes. The permanent magnet generator is then tested in a custom-made DC-motor driven dynamometer that was able to measure the torque input to the generator. Electrical load was applied to the generator through a simple rectifier-capacitor circuit and current and voltage output was measured. The result shown a maximum efficiency of 72% at a rotational speed of 955 rpm. The study proved that even for a simple permanent magnet generator design using modest tools and processes for manufacturing is possible. With optimization in the design, higher efficiency can be achieved and still maintaining simple manufacturing processes.

Simulation optimization studies the problem of optimizing simulation-based objectives. Simulation optimization is a new and hot topic in the field of system simulation and operational research. To improve the search efficiency, this paper presents a hybrid approach which combined genetic algorithm and local optimization technique for simulation optimization problems. Through the combination of genetic algorithms and with the local optimization method, it can maximally use the good global property of random searching and the convergence rate of a local method. This study considers the sampling procedure based on orthogonal design and quantization technology, the use of orthogonal genetic algorithm with quantization for the global exploration, and the application of local optimization technique for local exploitation. The final experimental results demonstrated that the proposed approach can find optimal or close-to-optimal solutions, and is superior to other recent algorithms in simulation optimization. Key words: Simulation optimization, genetic algorithms, local optimization, orthogonal design.