Metaheurísticas de otimização aplicadas à sintonia dos ganhos de controlador PI multivariável

Orientador : Prof. Dr. Leandro dos Santos CoelhoDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa: Curitiba, 04/05/2016Inclui referências : f. 79-87Área de concentração: Sistemas eletrônicosResumo: Esta dissertação tem por objetivo avaliar abordagens de sintonia de controladores PI (Proporcional e Integral) multivariável e acoplado, utilizando metaheurísticas de otimização aplicada a soma ponderada dos sinais de erro do sistema. Os controladores PI e PID (Proporcional, Integral e Derivativo) são os controladores mais utilizados na indústria, pois possui um algoritmo simples e eficiente. Nesta dissertação, o algoritmo evolutivo denominado evolução diferencial (DE), é comparado a outros algoritmos derivados do DE clássico e também a outros algoritmos evolutivos baseados em população. Estes algoritmos são aplicados na otimização de controle PI em dois estudos de caso: um processo de uma caldeira de turbina (Boiler-Turbine) e um processo de controle de nível (Quadruple Tank). O processo de otimização lida com a soma ponderada dos sinais de erro dos sistemas tratando-os como um problema de otimização mono-objetivo. Nos dois estudos de caso o algoritmo que obteve o melhor desempenho entre todos os algoritmos foi o EPSDE (Ensemble of Mutation and Crossover Strategies and Parameters in DE), e o que apresentou o desempenho menos promissor entre todos os algoritmos testados foi o CMAES (do inglês, Covariance Matrix Adaptation Evolution Strategy). Entre os algoritmos baseados em população o que apresentou o pior desempenho nos dois estudos de caso foi o MVO (do inglês, Multi-Verse Optimization) e o que apresentou o melhor desempenho foi PSO (do inglês, Particle Swarm Optimization). Para o primeiro estudo de caso, o DE clássico teve um bom desempenho, o que não ocorreu no segundo estudo de caso. Os algoritmos variantes de DE apresentaram um bom desempenho para os dois estudos de caso quando comparados a outros algoritmos baseados em população aplicados nesta dissertação, concluindo assim, a eficácia dos algoritmos DE para os casos testados. Palavras-chave: Controle PI Multivariável, Metaheurísticas de Otimização, Algoritmo de Evolução Diferencial.Abstract: This thesis focuses on validate the approaches used for PI control (proportional and integral) multivariable and coupled using metaheuristics optimization applied the weighted sum of the system error signals. The PI and PID (proportional, integral and derivative) controllers are the controllers most commonly used in the industry because it has a simple and efficient algorithm. In this thesis the evolutionary algorithm named differential evolution (DE) is compared to other derived algorithms and also other evolutionary algorithms based on population. These algorithms are applied to the optimization of PI control in two case studies: a process of a boiler turbine (Boiler-Turbine) and level control process (Quadruple Tank). The optimization process deals with the weighted sum of the systems errors signals by treating them as a singleobjective optimization problem. In the two case studies the algorithm which obtained the best performance among all algorithms was the EPSDE (Ensemble of Mutation and Crossover Strategies and Parameters in DE) and presented the performance less promising among all algorithms tested was the CMAES (Covariance Matrix Adaptation Evolution Strategy). Among the algorithms based on population presented the worst performance in two case studies was the MVO (Multi-Verse Optimization) and presented the best performance was PSO (Particle Swarm Optimization). For the first case study, the classic DE had a good performance, which did not occur in the second case study. The algorithms DE variants performed well for the two case studies compared to other based population algorithms applied in this thesis, concluding thus the effectiveness of DE algorithms for the cases tested. Key-words: PI Control Multivariable, Optimization Metaheuristics, Differential Evolutionary Algorithm

Monitoring and Control Framework for Advanced Power Plant Systems Using Artificial Intelligence Techniques

This dissertation presents the design, development, and simulation testing of a monitoring and control framework for dynamic systems using artificial intelligence techniques. A comprehensive monitoring and control system capable of detecting, identifying, evaluating, and accommodating various subsystem failures and upset conditions is presented. The system is developed by synergistically merging concepts inspired from the biological immune system with evolutionary optimization algorithms and adaptive control techniques.;The proposed methodology provides the tools for addressing the complexity and multi-dimensionality of the modern power plants in a comprehensive and integrated manner that classical approaches cannot achieve. Current approaches typically address abnormal condition (AC) detection of isolated subsystems of low complexity, affected by specific AC involving few features with limited identification capability. They do not attempt AC evaluation and mostly rely on control system robustness for accommodation. Addressing the problem of power plant monitoring and control under AC at this level of completeness has not yet been attempted.;Within the proposed framework, a novel algorithm, namely the partition of the universe, was developed for building the artificial immune system self. As compared to the clustering approach, the proposed approach is less computationally intensive and facilitates the use of full-dimensional self for system AC detection, identification, and evaluation. The approach is implemented in conjunction with a modified and improved dendritic cell algorithm. It allows for identifying the failed subsystems without previous training and is extended to address the AC evaluation using a novel approach.;The adaptive control laws are designed to augment the performance and robustness of baseline control laws under normal and abnormal operating conditions. Artificial neural network-based and artificial immune system-based approaches are developed and investigated for an advanced power plant through numerical simulation.;This dissertation also presents the development of an interactive computational environment for the optimization of power plant control system using evolutionary techniques with immunity-inspired enhancements. Several algorithms mimicking mechanisms of the immune system of superior organisms, such as cloning, affinity-based selection, seeding, and vaccination are used. These algorithms are expected to enhance the computational effectiveness, improve convergence, and be more efficient in handling multiple local extrema, through an adequate balance between exploration and exploitation.;The monitoring and control framework formulated in this dissertation applies to a wide range of technical problems. The proposed methodology is demonstrated with promising results using a high validity DynsimRTM model of the acid gas removal unit that is part of the integrated gasification combined cycle power plant available at West Virginia University AVESTAR Center. The obtained results show that the proposed system is an efficient and valuable technique to be applied to a real world application. The implementation of this methodology can potentially have significant impacts on the operational safety of many complex systems

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

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Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

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