What is it about?
Stochastic models is difficult to implement the models in FPGA-based real-time simulation (RTS) because their high order leads to a large calculation. Firstly, an orthogonal polynomials construction method is used based on Schmidt orthogonalization to describe stochastic variables with atypical probability distributions and provide conditions for simplifying the system model. Secondly, the method of probability space transformation is adopted to divide the system model into multiple sub-models to suppress the exponential growth of the model order while maintaining the statistical properties.
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Why is it important?
In this work, we present an optimized real-time stochastic modeling method for power electronic converters based on generalized polynomial chaos (gPC). This method has three contributions: 1) It proposes an orthogonal polynomials construction method that accurately describes stochastic variables with atypical probability distributions that are introduced in the real-time simulation modeling process; 2) It proposes a method of probability space transformation that can divide the system model into multiple sub-models while maintaining the statistical properties of the original model; 3) It can be implemented in FPGA-based real-time simulation platform in steps of 1us. Compared with the traditional stochastic model, it can maintain the same accuracy and save about 37% of resources.
Perspectives
Due to the FPGA's limited hardware resources, FPGA-based real-time simulation has difficulty in realizing functions other than basic models, such as state detection and digital twins. I hope this article will inspire further exploration of real-time simulation technology.
Shinan Wang
Read the Original
This page is a summary of: Optimized Real-Time Stochastic Model of Power Electronic Converters based on FPGA, ACM Transactions on Modeling and Computer Simulation, July 2024, ACM (Association for Computing Machinery),
DOI: 10.1145/3678174.
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