Multiply-Connected Neuro PID Control of Ultra-Compact Binary Power Plant

Abstract

An ultra-compact binary power plant converts thermal energy into electric power using low temperature difference thermal energy between heat source and cooling source. In control of the binary power plant, the negative effects such as characteristic changes caused by environmental condition and corrosion of related equipment, and coupling between control loops are the main difficulties in designing a controller and fine-tuning its parameters. In order to realize the stable power generation it is necessary to consider a control system to keep control performance when characteristic change of the binary power plant, and to compensate coupling in Multi-Input and Multi-Output (MIMO) systems. A Multiply-Connected (MC) Neuro PID control system using a Neural Network architecture connected directly by neurons of each control loop is proposed to overcome above difficulties, and its strategy for design of the control system is introduced. The proposed MC Neuro PID control system is compared to traditional control systems to show the effectiveness of the MC Neuro PID control through simulations in this paper.