Browsing Institute for Innovation in Sustainable Engineering by Subjects
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A novel genetic programming approach to the design of engine control systems for the voltage stabilization of hybrid electric vehicle generator outputsThis paper describes a Genetic Programming based automatic design methodology applied to the maintenance of a stable generated electrical output from a series-hybrid vehicle generator set. The generator set comprises a three-phase AC generator whose output is subsequently rectified to DC. The engine/generator combination receives its control input via an electronically actuated throttle, whose control integration is made more complex due to the significant system time delay. This time delay problem is usually addressed by model predictive design methods, which add computational complexity and rely as a necessity on accurate system and delay models. In order to eliminate this reliance, and achieve stable operation with disturbance rejection, a controller is designed via a Genetic Programming framework implemented directly in Matlab and, particularly, Simulink. The principal objective is to obtain a relatively simple controller for the time-delay system which does not rely on computationally expensive structures, yet retains inherent disturbance rejection properties. A methodology is presented to automatically design control systems directly upon the block libraries available in Simulink to automatically evolve robust control structures.
Robust fault estimation for wind turbine energy via hybrid systems.The rapid development of modern wind turbine technology has led to increasing demand for improving system reliability and practical concern for robust fault monitoring scheme. This paper presents the investigation of a 5 MW Dynamic Wind Turbine Energy System that was designed to sustain condition monitoring and fault diagnosis with the goal of improving the reliability operations of universal practical control systems. A hybrid stochastic technique is proposed based on an augmented observer combined with eigenstructure assignment for the parameterisation and the genetic algorithm (GA) optimisation to address the attenuation of uncertainty mostly generated by disturbances. Scenarios-based are employed to explore sensor and actuator faults that have direct and indirect impacts on modern wind turbine system, based on monitoring components that are prone to malfunction. The analysis is aimed to determine the effect of concerned simulated faults from uncertainty in respect to environmental disturbances mostly challenged in real-world operations. The efficiency of the proposed approach will improve the reliability performance of wind turbine system states and diagnose uncertain faults simultaneously. The simulation outcomes illustrate the robustness of the dynamic turbine systems with a diagnostic performance to advance the practical solutions for improving reliable systems.