Neural network-based control of an intelligent solar Stirling pump. (1st January 2016) - Tavakolpour-Saleh, A.R.; Jokar, H.
Abstract: In this paper, an ANN (artificial neural network) control system is applied to a novel solar-powered active LTD (low temperature differential) Stirling pump. First, a mathematical description of the proposed Stirling pump is presented. Then, optimum operating frequencies of the converter corresponding to different operating conditions ( i.e. different sink and source temperatures and water heads) are investigated using the proposed mathematical framework. It is found that the proposed complex mathematical scheme has a very slow convergence and thus, is not appropriate for real-time implementation of the model-based controller. Consequently, a NN (neural network) model with a lower complexity is proposed to learn the simulation data obtained from the mathematical model. The designed neural network controller is thus applied to a digital processor to effectively tune the converter frequency so that a maximum output power is acquired. Finally, the performance of the proposed mechatronic system is evaluated experimentally. The experimental results clearly demonstrate the feasibility of pumping water at low temperature difference under variable operating conditions using the proposed intelligent Stirling converter. Highlights: A novel intelligent solar-powered active LTD Stirling pump was introduced. A neural network controller was used to tune the converter speed. The intelligent converter was able to adapt itself to different operating conditions. It was possible to excite the water column with its resonance mode. Experimental results showed the effectiveness of the proposed converter.