Forward and Backpropagation-Based Artificial Neural Network Modeling Method for Power Conversion System

The PCS controls and converts the flow of electrical power. Generally, it uses sensors to measure voltage and current. However, these sensors require careful tuning across their entire operating range. Additionally, they lead to increased system complexity, greater physical volume, and higher maintenance costs. Therefore, to address these challenges, this paper proposes a forward and backpropagation-based ANN modeling method for PCS. The proposed ANN modeling method is trained using forward and backpropagation to learn the nonlinear relationships between system inputs and outputs. Through this training process, the proposed ANN modeling method can accurately predict system variables without sensors or mathematical modeling. Furthermore, by eliminating the need for sensors, the system structure can be simplified, and the overall cost significantly reduced. This paper focuses on mathematically deriving and implementing a forward and backpropagation-based ANN modeling method, and it verifies its prediction performance using a series-parallel resistor circuit. The validity of the proposed ANN modeling method is verified by simulation and experimental results.