Capacitance estimation for dc-link capacitors in a back-to-back converter based on Artificial Neural Network algorithm

The reliability of dc-link capacitors in power electronic converters is one of the critical aspects to be considered in modern power converter design. The observation of their ageing process and the estimation of their health status have been an attractive subject for the industrial field and hence for the academic research field as well. The existing condition monitoring methods suffer from shortcomings such as low estimation accuracy, extra hardware, and also increased cost. Therefore, the developed methods of condition monitoring that are based on software solutions and algorithms could be the way out of the aforementioned challenges and shortcomings. In this paper, a pure software condition monitoring method based on Artificial Neural Network (ANN) algorithm is proposed. The implemented ANN estimates the capacitance of the dc-link capacitor in a back-to-back converter. The error analysis of the estimated results is also studied. The developed ANN algorithm has been implemented in a Digital Signal Processor (DSP) in order to have a proof of concept of the proposed method.