Proper Orthogonal Decomposition for Order Reduction of Permanent Magnet Machine Model

Model order reduction is an approach for reducing size, complexity, and computation cost of mathematical models in numerical simulations. This paper describes the application of proper orthogonal decomposition method, as one of the most efficient model order reduction techniques, in generating lower dimensional model of a permanent magnet machine. In proper orthogonal decomposition, data collected from high-dimensional numerical simulations (called snapshots) are projected onto a set of orthonormal basis functions. Thereafter, these basis functions are combined with the original model equations to build a reduced order model. The comparison of computational results of the original model with the reduced model indicates that the reduced model is able to accurately reproduce both local and global operation quantities of the machine under investigation.