Comparison of Geometric Optimization Methods with Multiobjective Genetic Algorithms for Solving Integrated Optimal Design Problems

In this paper, system design methodologies for optimizing heterogenous power devices in electrical engineering are investigated. The concept of Integrated Optimal Design (IOD) is presented and a simplified but typical example is given. It consists in finding Pareto-optimal configurations for the motor drive of an electric vehicle. For that purpose, a geometric optimization method (i.e the Hooke and Jeeves minimization procedure) associated with an objective weighting sum and a Multiobjective Genetic Algorithm (i.e. the NSGA-II) are compared. Several performance issues are discussed such as the accuracy in the determination of Pareto-optimal configurations and the capability to well spread these solutions in the objective space

A review of design optimization methods for electrical machines

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. Electrical machines are the hearts of many appliances, industrial equipment and systems. In the context of global sustainability, they must fulfill various requirements, not only physically and technologically but also environmentally. Therefore, their design optimization process becomes more and more complex as more engineering disciplines/domains and constraints are involved, such as electromagnetics, structural mechanics and heat transfer. This paper aims to present a review of the design optimization methods for electrical machines, including design analysis methods and models, optimization models, algorithms and methods/strategies. Several efficient optimization methods/strategies are highlighted with comments, including surrogate-model based and multi-level optimization methods. In addition, two promising and challenging topics in both academic and industrial communities are discussed, and two novel optimization methods are introduced for advanced design optimization of electrical machines. First, a system-level design optimization method is introduced for the development of advanced electric drive systems. Second, a robust design optimization method based on the design for six-sigma technique is introduced for high-quality manufacturing of electrical machines in production. Meanwhile, a proposal is presented for the development of a robust design optimization service based on industrial big data and cloud computing services. Finally, five future directions are proposed, including smart design optimization method for future intelligent design and production of electrical machines

Integrated Optimal Design of a Passive Wind Turbine System: An Experimental Validation

This work presents design and experimentation of a full passive wind turbine system without active electronic part(power and control). The efficiency of such device can be obtained only if the system design parameters are mutually adapted through an Integrated Optimal Design (IOD) method. This approach based on multiobjective optimization, aims at concurrently optimizing the wind power extraction and the global system losses for a given wind speed profile while reducing the weight of the wind turbine generator. It allows us to obtain the main characteristics (geometric and energetic features) of the optimal Permanent Magnet Synchronous Generator (PMSG) for the passive wind turbine. Finally, experiments on the PMSG prototype built from this work show a good agreement with theoretical predictions. This validates the design approach and confirms the effectiveness of such passive device

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

Design optimization of Halbach array permanent magnet motor to achieve sensorless performance using genetic algorithm

Purpose The purpose of this paper is to investigate Halbach array effects in surface mounted permanent magnet machine (SMPM) in terms of both self-sensing and torque capabilities. A comparison between a conventional SMPM, which has radially magnetized rotor, and a Halbach machine has been carried out. Design/methodology/approach The geometric parameters of the two machines have been optimized using genetic algorithm (GA) with looking Pareto. The performance of the machines’ geometry has been calculated by finite element analysis (FEA) software, and two parametric machine models have been realized in Matlab coupled with the FEA and GA toolboxes. Outer volume of the machine, thus copper loss per volume has been kept constant. The Pareto front approach, which simultaneously considers looks two aims, has been used to provide the trade-off between the torque and sensorless performances. Findings The two machines’ results have been compared separately for each loading condition. According to the results, the superiority of the Halbach machine has been shown in terms of sensorless capability compromising torque performance. Additionally, this paper shows that the self-sensing properties of a SMPM machine should be considered at the design stage of the machine. Originality/value A Halbach machine design optimization has been presented using Pareto optimal set which provides a trade-off comparison between two aims without using weightings. These are sensorless performance and torque capability. There is no such a work about sensorless capability of the Halbach type SMPM in the literature

Multi-objective optimization of a magnetically levitated planar motor with multi-layer windings

In this paper, a novel magnetically levitated coreless planar motor with three-layer orthogonal overlapping windings is shown to have higher power density and higher space utilization compared to other coreless planar motors. In order to achieve maximum forces with minimum cost and minimum space, a multi-objective optimization of the novel planar motor is carried out. In order to reduce the computational resources required for finite element analyses, a fast but accurate analytical tool is developed, based on expressions of the flux density of the permanent magnet array, which are derived from the scalar magnetic potential method. The validity and accuracy is verified by 3D FE results. Based on the force formulas and the multi-objective function derived from the analytical models, a particle swarm optimization (PSO) algorithm is applied to optimize the dimensions of the planar motor. The design and optimization of the planar motor is validated with experimental results, measured on a built prototype, thus proving the validity of the analytical tools

A Novel Design Optimization of a Fault-Tolerant AC Permanent Magnet Machine-Drive System

In this dissertation, fault-tolerant capabilities of permanent magnet (PM) machines were investigated. The 12-slot 10-pole PM machines with V-type and spoke-type PM layouts were selected as candidate topologies for fault-tolerant PM machine design optimization problems. The combination of 12-slot and 10-pole configuration for PM machines requires a fractional-slot concentrated winding (FSCW) layout, which can lead to especially significant PM losses in such machines. Thus, a hybrid method to compute the PM losses was investigated, which combines computationally efficient finite-element analysis (CE-FEA) with a new analytical formulation for PM eddy-current loss computation in sine-wave current regulated synchronous PM machines. These algorithms were applied to two FSCW PM machines with different circumferential and axial PM block segmentation arrangements. The accuracy of this method was validated by results from 2D and 3D time-stepping FEA. The CE-FEA approach has the capabilities of calculating torque profiles, induced voltage waveforms, d and q-axes inductances, torque angle for maximum torque per ampere load condition, and stator core losses. The implementation techniques for such a method are presented. A combined design optimization method employing design of experiments (DOE) and differential evolution (DE) algorithms was developed. The DOE approaches were used to perform a sensitivity study from which significant independent design variables were selected for the DE design optimization procedure. Two optimization objectives are concurrently considered for minimizing material cost and power losses. The optimization results enabled the systematic comparison of four PM motor topologies: two different V-shape, flat bar-type and spoke-type, respectively. A study of the relative merits of each topology was determined. An automated design optimization method using the CE-FEA and DE algorithms was utilized in the case study of a 12-slot 10-pole PM machine with V-type PM layout. An engineering decision process based on the Pareto-optimal front for two objectives, material cost and losses, is presented together with discussions on the tradeoffs between cost and performance. One optimal design was finally selected and prototyped. A set of experimental tests, including open circuit tests at various speeds and on-load tests under various load and speed conditions, were performed successfully, which validated the findings of this work