Optimization of a small passive wind turbine generator with multiobjective genetic algorithms

In this paper Multiobjective Genetic Algorithms (MOGAs) are used for the design of a small wind turbine generator (WTG) coupled to a DC bus through a diode bridge. The originality of the considered system resides in the suppression of the Maximum Power Point Tracker (MPPT). The poor efficiency of the corresponding passive structure is considerably improved by optimizing the generator characteristics associated with the wind turbine in relation to the wind cycle. The optimized configurations are capable of matching very closely the behavior of active wind turbine systems which operate at optimal wind powers by using a MPPT control device

Systemic design of multidisciplinary electrical energy devices: a pedagogical approach

In this paper, we present a complete educative project for illustrating the design and the analysis of hybrid electrical systems. It is based on the study of an ElectroHydrostatic Actuator for flight control application, fed by a power supply associating a PEM fuel cell with a ultracapacitor storage. This system is controlled to achieve a typical energy management strategy of this multi source structure. Step by step, student can faces typical issues relative to the design of heterogenous and multidisciplinary devices by achieving eight pedagogical objectives. These eight targets are focused on methodological approach for multi domain modelling (Bond Graphs), causal analysis, but also on simulation of complex heterogeneous systems. A typical hybrid system feeding an ElectroHydrostatic Actuator (EHA) for flight control application has to be designed which drives students towards other pedagogical objectives: system based device sizing (fuel cell and ultracapacitor), energy management, system analysis

Clustering analysis of railway driving missions with niching

A wide number of applications requires classifying or grouping data into a set of categories or clusters. Most popular clustering techniques to achieve this objective are K-means clustering and hierarchical clustering. However, both of these methods necessitate the a priori setting of the cluster number. In this paper, a clustering method based on the use of a niching genetic algorithm is presented, with the aim of finding the best compromise between the inter-cluster distance maximization and the intra-cluster distance minimization. This method is applied to three clustering benchmarks and to the classification of driving missions for railway applications

Model inversion of electrical engineering systems from bicausal bond graphs

In this paper, the application of bicausal bond graphs for model inversion of typical electrical engineering systems is emphasised. Inverse models are particularly useful for the synthesis step of the system design process. To illustrate these issues, a typical railway traction device and an Aeronautic Electro Hydrostatic Actuator are considered as case studies. From the requirements applied to the system outputs, we show how the synthesis of electrical constraints can be carried out from the inverse bicausal Bond Graph

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

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

Signal synthesis by means of evolutionary algorithms

In this article, we investigate a procedure for generating signals with genetic algorithms. Signals are obtained from elementary patterns characterized by different degrees of freedom. These patterns are repeated and combined in order to reach specific signal shapes. The whole signal parametrization has to be determined by solving a difficult inverse problem of high dimensionality and strong multimodality. This can be carried out using evolutionary algorithms with the aim of finding all pattern configurations in the signal. The different signal synthesis schemes are evaluated, tested and applied to the generation of particular railway driving profiles

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

Bond Graph Multi-time Scale Analysis of a Railway Traction System

The unified formalism of bond graphs is applicable to designing, modeling and analyzing complex and heterogeneous physical systems. Moreover, various order-reduction methods can be applied directly on bond graph models in order to simplify them. The aim of this paper is to demonstrate the characteristics and the advantages of the bond graph formalism, considering as an example, model simplification methods of a railway traction system. Several simulations are carried out in order to validate the simplified models. The analysis of couplings on these models highlights the elements that cause perturbations, such as mechanical resonance