Transient Voltage Stability Assessment Method for the UHVDC Power Grid with High Proportion of New Energy Based on Artificial Intelligence

Wei Hu, Rongfu Sun, Yun-tao Sun, Ran Ding, Yiming Yao, Kexi Qian
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Abstract

UHVDC has developed rapidly in recent years, due to its large transmission power, large disturbances such as blocking and commutation failure will have a huge impact on the reactive power balance of the sending power grid, resulting in large-scale new energy off-grid. This paper establishes an improved BP neural network model based on an adaptive genetic algorithm for transient overvoltage evaluation, and takes a UHVDC sending power grid in northern China as an example to explore the nonlinear relationship between transient overvoltage and system operation state and verify the effectiveness of the method.
基于人工智能的超高直流高新能源电网暂态电压稳定性评估方法
特高压直流输电近年来发展迅速,由于其传输功率大,阻塞、换流故障等大扰动将对送电电网无功平衡产生巨大影响,造成新能源大规模离网。本文建立了一种基于自适应遗传算法的改进BP神经网络暂态过电压评估模型,并以中国北方特高压直流送电电网为例,探讨暂态过电压与系统运行状态的非线性关系,验证了该方法的有效性。
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