基于人工神经网络的分布式发电机组连续、非线性、最优速度控制

C. Hill, P. Zanchetta, N. Okaeme, S. Bozhko
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引用次数: 0

摘要

采用内燃机原动机的分布式发电机组在各种应用中仍被广泛用于供电。这些应用范围从备用电源系统到在电网连接技术上不切实际或经济上不经济的地方提供电力。由于柴油成本的不断增加以及与柴油使用相关的环境问题,优化这些交流发电机和减少燃料消耗至关重要。本文介绍了如何利用人工神经网络来获得一个将可变负荷需求与最优速度需求联系起来的连续函数。使用MATLAB中的人工神经网络工具箱来创建、训练和测试人工神经网络。本文还介绍了分布式发电机组仿真系统的实验结果。总的来说,这表明了使用人工神经网络在最优非线性速度控制下运行变速系统是可能的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Continuous, non-linear, optimal speed control of a Distributed Generation Power Pack using Artificial Neural Networks
Distributed Generation Power Packs with a combustion engine prime mover are still widely used to supply electric power in a variety of applications. These applications range from backup power supply systems to providing power in places where grid connection is either technically impractical or financially uneconomic. Due to the ever increasing cost of diesel fuel and the environmental issues associated with its use, the optimisation of these AC generators and the reduction of fuel consumption is vital. This paper presents how Artificial Neural Networks can be utilised in order to obtain a continuous function which relates variable load demand to optimal speed demand. The Artificial Neural Network toolbox within MATLAB is used to create, train and test the Artificial Neural Networks. This paper also shows the results of an experimental system used in order to emulate the Distributed Generation Power Pack. Overall it is shown that is possible to operate a variable speed system under optimal, non-linear, speed control using Artificial Neural Networks.
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