Optimized Real-Time Stochastic Model of Power Electronic Converters based on FPGA
针对电力电子变换器随机参数建模在FPGA实时仿真中计算量大的问题，提出基于广义多项式混沌的优化方法，通过正交多项式构造和概率空间变换降低模型阶数，在1微秒步长硬件在环实验中资源消耗减少约37%且精度不变。
Stochastic models can effectively describe the operating characteristics of power electronic converters with stochastic parameters. However, it is difficult to implement the models in field programmable gate array– (FPGA) based real-time simulation, because their high order leads to a large calculation. This article proposes an optimized real-time stochastic modeling method for power electronic converters based on generalized polynomial chaos. First, 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. Second, 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. This method has performed over the traditional stochastic modeling method. The proposed model is built on FPGA-based hardware-in-the-loop experiment platform with 1us simulation step. The optimized model uses approximately 37% fewer resources than the traditional stochastic model while maintaining the same level of accuracy.