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

机译：基于强化学习的PFC整流器分段仿射大信号优化建模

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摘要

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.

机译：功率因数校正整流器具有非线性特性。基于非线性模型，分析和设计非常困难。分段仿射建模是用于此目的的一种方法。分段仿射建模的主要问题是模型的复杂性。本文介绍了一种基于强化学习的优化算法，以降低复杂度。通过引入有针对性的算法获得大信号模型。有目的（新）算法在升压功率因数校正整流器上实现。通过仿真比较了最佳分段仿射，常规分段仿射，线性和非线性模型之间的比较。

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《2011 2nd Power Electronics, Drive Systems and Technologies Conference》|2011年|p.512-517|共6页
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作者
Molla Ahmadian Kaseb Hamed; Mohammad Bagher Naghibi Sistani;
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中图分类输配电工程、电力网及电力系统;
关键词
Large Signal Modeling; Piecewise Affine Approximation; Reinforcement Learning; power factor correction rectifiers;

机译：大信号建模;分段仿射逼近;强化学习;功率因数校正整流器;

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

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