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Micro-Turbine Generation Control System Optimization Using Evolutionary algorithm

机译:基于进化算法的微机发电控制系统优化

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Distribution systems management is becoming an increasingly complicated issue due to the introduction of new technologies, new energy trading strategies, and new deregulated environment. In the new deregulated energy market and considering the incentives coming from the technical and economical fields, it is reasonable to consider Distributed Generation (DG) as a viable option to solve the lacking electric power supply problem. This paper presents a mathematical distribution system planning model considering three planning options to system expansion and to meet the load growth requirements with a reasonable price as well as the system power quality problems. DG is introduced as an attractive planning option in competition with voltage regulator devices and Interruptible load. This paper presents a dynamic modelling and simulation of a high speed single shaft micro-turbine generation (MTG) system for grid connected operation and shows genetic algorithm (GA) role in improvement of control system operation. The model is developed with the consideration of the main parts including: compressor-turbine, permanent magnet (PM) generator, three phase bridge rectifier and inverter. The simulation results show the capability of Genetic Algorithm for controlling MTG system. The model is developed in Mat lab / Simulink.
机译:由于引入了新技术,新能源交易策略和新的放松管制的环境,配电系统管理正变得越来越复杂。在新的放松管制的能源市场中,考虑到技术和经济领域的激励措施,将分布式发电(DG)视为解决电力供应不足问题的可行选择是合理的。本文提出了一种数学配电系统规划模型,该模型考虑了用于系统扩展和以合理的价格满足负载增长要求以及系统电源质量问题的三种规划方案。 DG是与电压调节器设备和可中断负载竞争的一种有吸引力的计划选择。本文介绍了用于电网连接运行的高速单轴微涡轮发电(MTG)系统的动态建模和仿真,并展示了遗传算法(GA)在改善控制系统运行中的作用。该模型的开发考虑了以下主要部分:压缩机涡轮,永磁(PM)发电机,三相桥式整流器和逆变器。仿真结果表明了遗传算法对MTG系统的控制能力。该模型是在Mat lab / Simulink中开发的。

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