System optimization by multiobjective genetic algorithms and analysis of the coupling between variables, constraints and objectives

This paper presents a methodology based on Multiobjective Genetic Algorithms (MOGA’s) for the design of electrical engineering systems. MOGA’s allow to optimize multiple heterogeneous criteria in complex systems, but also simplify couplings and sensitivity analysis by determining the evolution of design variables along the Pareto-optimal front. A rather simplified case study dealing with the optimal dimensioning of an inverter – permanent magnet motor – reducer – load association is carried out to demonstrate the interest of the approach

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

Recombination and Self-Adaptation in Multi-objective Genetic Algorithms

This paper investigates the influence of recombination and self-adaptation in real-encoded Multi-Objective Genetic Algorithms (MOGAs). NSGA-II and SPEA2 are used as example to characterize the efficiency of MOGAs in relation to various recombination operators. The blend crossover, the simulated binary crossover and the breeder genetic crossover are compared for both MOGAs on multi-objective problems of the literature. Finally, a self-adaptive recombination scheme is proposed to improve the robustness of MOGAs

Comparison of Direct Multiobjective Optimization Methods for the Design of Electric Vehicles

"System design oriented methodologies" are discussed in this paper through the comparison of multiobjective optimization methods applied to heterogeneous devices in electrical engineering. Avoiding criteria function derivatives, direct optimization algorithms are used. In particular, deterministic geometric methods such as the Hooke & Jeeves heuristic approach are compared with stochastic evolutionary algorithms (Pareto genetic algorithms). Different issues relative to convergence rapidity and robustness on mixed (continuous/discrete), constrained and multiobjective problems are discussed. A typical electrical engineering heterogeneous and multidisciplinary system is considered as a case study: the motor drive of an electric vehicle. Some results emphasize the capacity of each approach to facilitate system analysis and particularly to display couplings between optimization parameters, constraints, objectives and the driving mission

Conception simultanée de systèmes électriques hétérogènes par algorithmes évolutionnaires multicritères. Applications à l'optimisation de chaînes de traction pour véhicules électriques

Dans cet article, des algorithmes évolutionnaires sont appliqués à l’optimisation multicritère d’une chaîne de traction pour véhicules électriques. Dans une première partie, la notion de conception simultanée de systèmes hétérogènes en génie électrique est développée et analysée. Les problèmes d’optimisation induits par cette approche sont assez complexes et les algorithmes évolutionnaires multicritères semblent bien adaptés pour fournir des solutions intéressantes au concepteur. Par la suite, un état de l’art de ces techniques est développé et un exemple d’application est donné. Il s’agit de concevoir des chaînes de traction pour véhicules électriques en fonction de missions spécifiques (cycle urbain ou routier). Les résultats obtenus soulignent l’intérêt de l’approche évolutionnaire multicritère pour la conception simultanée tant au niveau de l’optimisation que de l’analyse systémique de dispositifs complexes

Multiobjective optimisation by self-adapting Pareto genetic algorithms for electrical system design

In this paper, Pareto Genetic Algorithms are applied to solve multiobjective optimisation problems. In particular, a recent version of the nondominated sorting genetic algorithm (NSGA-II) is presented. A self-adaptive recombination scheme is used for crossover operators to improve the algorithm efficiency. Tests on mathematical functions of various difficulties are carried out to show the robustness of self adaptation. Finally, the self-adaptive NSGA-II is applied to the optimal design of an electrical system based on a inverter - permanent magnet motor - reducer - load association. It allows to reduce the global losses and weight in the system and help the designer to understand couplings and interactions between design variables in relation to technological constraints and objectives

Détermination et diagnostic des modulations des courants statoriques d'une machine asynchrone en présence d'oscillations de couple

National audienceCet article présente une modélisation de la machine asynchrone permettant le diagnostic des modulations des courants statoriques dans le cas d'oscillations du couple de charge. Le modèle proposé est établi en régime permanent pour un système en boucle ouverte. Les composantes oscillatoires du couple de charge entraînent l'apparition de modulations d'amplitude et de modulations de phase sur les courants d'alimentation de la machine. Les caractéristiques de ces modulations dépendent des paramètres électriques et mécaniques, du point de fonctionnement de l'entraînement, de la fréquence des oscillations de couple. Un modèle analytique de l'entraînement, basé sur le modèle d'état linéaire dans le domaine fréquentiel est proposée afin de prédire les composantes spectrales des courants statoriques. Le modèle proposé est validé par des mesures expérimentales

Stator Current based Indicators for Bearing Fault Detection in Synchronous Machine by Statistical Frequency Selection

The aim of this paper is to present some indicators developed for efficient detection of bearing defaults in high speed synchronous machines using a stator current analysis. These actuators are used in an air conditioning fan for aeronautic applications. The signatures of the bearing defects appear through an increase in amplitude of specific current harmonics multiples of the rotation frequency. From an experimental comparison between a healthy fan and another with damaged bearings, an automatic frequency selection is performed to identify the frequency ranges for which the energy is the most sensitive to the considered faults. From these frequencies, several strategies are investigated to propose a suitable indicator for the bearing fault detection. A post-processing algorithm is then developed and tested for different measurements, different types of faults and different operating points, to ensure the robustness of the proposed method

Surrogate-based diagnosis of mechanical faults in induction motor from stator current measurements

This paper focuses on induction motor monitoring based on stator current measurements. The diagnosis aims at identifying the mechanical faults related to either airgap eccentricity or load torque oscillation. The airgap eccentricity (respectively load torque oscillation) essentially results in an amplitude (respectively phase) modulation of the stator current. Classical spectral analysis allows for the detection but not for the discrimination of these modulations. Time-frequency representations, such as the spectrogram or the Wigner distribution, provide appropriate signatures for fault discrimination. This paper proposes to perform the decision task from the time-frequency representation using the surrogate data technique. In a deterministic context, the phase and amplitude modulations can be considered as non-stationarities since they correspond to time-variations of the signal spectral content. The detection of a modulation is expressed as a binary hypothesis test. The null hypothesis corresponds to a signal without modulation. Stationarized/unmodulated replicas of the observed (possibly modulated) signal are obtained by phase randomization of its Fourier transform. These so-called surrogates provide a reference for the null hypothesis. The observed signal is then compared to these surrogates using appropriate distances in the time-frequency domain. A one-class classifier may be used considering the surrogates as a learning set. This classifier detects outliers corresponding to the modulation and thus to the failures. Moreover, this technique provides the information concerning the predominant type of modulation. This diagnosis method will be tested on simulated and experimental signals

Optimal design of electrical engineering systems using Pareto Genetic Algorithms

This paper presents a Pareto Genetic Algorithm for multiobjective optimisation in electrical engineering design. Through a simple electromechanical system, based on an inverter-permanent magnet motor-reducer-load association described by analytical models, we discuss the efficiency and advantages of such methods. We have chosen a rather simplified case study, to emphasise advantages of this approach for optimisation issues as well as a better understanding of system couplings