Optimal piecewise affine large signal modeling of PFC rectifiers based on reinforcement learning

Power factor correction rectifiers have nonlinear characteristics. Analysis and design is very difficult based on nonlinear models. Piecewise affine modeling is an approach for this purpose The main problem of piecewise affine modeling is complexity of models. In this paper, the optimal algorithm based on reinforcement learning is introduced for complexity reduction. Large signal models are obtained by introduced purposed algorithm. The purposed (new) algorithm is implemented on the boost power factor correction rectifier. Comparison of between optimal piecewise affine, conventional Piecewise affine, linear and nonlinear models is done by simulations.