Yunfan Huang¹, Qingquan Lv², Zhenzhen Zhang², Haiying Dong^1,*

Energy Engineering, Vol.120, No.6, pp. 1401-1420, 2023, DOI:10.32604/ee.2023.027365

Abstract The virtual synchronous generator (VSG) can simulate synchronous machine’s operation mechanism in the control link of an energy storage converter, so that an electrochemical energy storage power station has the ability to actively support the power grid, from passive regulation to active support. Since energy storage is an important physical basis for realizing the inertia and damping characteristics in VSG control, energy storage constraints of the physical characteristics on the system control parameters are analyzed to provide a basis for the system parameter tuning. In a classic VSG control, its virtual inertia and damping coefficient remain unchanged. When the grid… More >

Jiaxiang Luo^1,2, Yu Li², Weien Zhou², Zhiqiang Gong², Zeyu Zhang¹, Wen Yao^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.3, pp. 823-848, 2021, DOI:10.32604/cmes.2021.016737

Abstract Deep learning for topology optimization has been extensively studied to reduce the cost of calculation in recent years. However, the loss function of the above method is mainly based on pixel-wise errors from the image perspective, which cannot embed the physical knowledge of topology optimization. Therefore, this paper presents an improved deep learning model to alleviate the above difficulty effectively. The feature pyramid network (FPN), a kind of deep learning model, is trained to learn the inherent physical law of topology optimization itself, of which the loss function is composed of pixel-wise errors and physical constraints. Since the calculation of… More >