Valceres Vieira Rocha e Silva

Performance optimization of gas turbine engine

Performance optimization of a gas turbine engine can be expressed in terms of minimizing fuel consumption while maintaining nominal thrust output, maximizing thrust for the same fuel consumption and minimizing turbine blade temperature. Additional control layers are used to improve engine performance. This paper presents an evolutionary approach called the StudGA as the optimization framework to design for optimal performance in terms of the three criteria above. This approach converges fast and can potentially save on computing cost. Model-based experimental results are used to illustrate this approach.

Multiobjective optimization using variable complexity modelling for control system design

A multi-stage design approach that uses a multiobjective genetic algorithm as the framework for optimization and multiobjective preference articulation, and an H_infty loop-shaping technique are used to design controllers for a gas turbine engine. A non-linear model is used to assess performance of the controller. Because the computational load of applying multiobjective genetic algorithm to this control strategy is very high, a neural network and response surface models are used in order to speed up the design process within the framework of a multiobjective genetic algorithm. The final designs are checked using the original non-linear model.

Coordinated allocation and control of voltage regulators based on reactive tabu search

This paper present a coordinated allocation and control of Step voltage regulators (SVRs) and Static Var Compensators (SVCs) for voltage deviations in case distributed generations are installed in distribution networks. In the proposed method, the reactive tabu search (RTS) with multiple structures and functions has been applied to the coordination. Firstly, the locations of SVRs are selected optimally and secondly the tap positions of SVRs are optimized by the RTS. Finally, the locations of SVC are decided to brash up the voltage profile in the distribution network. The proposed method enables us to take account of the installation cost of both SVR and SVC as an economic criterion, the upper and lower limit of voltage at each node and also the upper limit of line currents as constraints. By applying the proposed method to a practical distribution test system, it is verified that this method is efficient to allocate SVRs and SVCs at the minimum cost and regulate the system voltages within an appropriate value after introducing distributed generations into the distribution system.

Application of genetic programming for fine tuning PID controller parameters designed through ziegler-nichols technique

PID optimal parameters selection have been extensively studied, in order to improve some strict performance requirements for complex systems. Ziegler-Nichols methods give estimated values for these parameters based on the systems transient response. Therefore, a fine tuning of these parameters is required to improve the systems behavior. In this work, genetic programming is used to optimize the three parameters Kp , Ti and Td , after been tuned by Ziegler-Nichols method, to control a high-order process, a large time delay plant and a highly non-minimum phase process. The results were compared to some other tuning methods, and showed to be promising.

Control system design for a gas turbine engine using evolutionary computing for multidisciplinary optimization

Multidisciplinary optimization (MDO) is concerned with complex systems exhibiting challenges in terms of organization and scale. Thus, it is well suited to be applied to complex multivariable control design. Collaborative optimization is one approach for dealing with complex multidisciplinary optimization problems. Three MDO architectures, including collaborative optimization, are applied to control system design for a gas turbine engine, in order to improve the design search process by exploring possible solutions with parallel, but independent search strands. The optimization is carried out through a multiobjective genetic algorithm framework.

Nonlinear control system design using variable complexity modelling and multiobjective optimization

To design controllers for complex non-linear systems usually involves the use of expensive computational models. A non-linear thermodynamic model of a gas turbine engine is used to evaluate a selection of designs for a multivariable PI controller configuration. An approach using variable complexity modelling (VCM) is introduced to allow more designs to be evaluated and also to speed up the design process. Response surface methodology (RSM) is a statistical technique in which smooth functions are used to model an objective function. RSM employs statistical methods to create functions, typically polynomials, to model the response or outcome of a numerical experiment in terms of several independent variables. Regression analysis is applied to fit polynomial models to this data for various control responses. These control responses models are evaluated by a multiobjective genetic algorithm to design the controller parameters. The final designs are checked using the original non-linear model.

SIMULATION OF CHUA'S CIRCUIT BY MEANS OF INTERVAL ANALYSIS

The Chuas circuit is a paradigm for nonlinear scientific studies. It is usually simulated by means of numerical methods under IEEE 754-2008 standard. Although the error propagation problem is well known, little attention has been given to the relationship between this error and inequalities presented in Chuas circuit model. Taking the average of round mode towards

SELECTING THE CONTROL CONFIGURATION FOR A NONLINEAR SYSTEM BY ON-LINE CONTROLLERS’ PARAMETERS OPTIMIZATION

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Design synergy through variable complexity architectures

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The design of multiple linear regression models using a genetic algorithm to diagnose initial short-circuit faults in 3-phase induction motors

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