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.
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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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