现代电力系统在遭受扰动后,失稳模式呈现多样化,迫切需要准确识别不同的失稳模式,以采取相应的控制措施避免造成较大损失,因此本文提出一种基于改进Swin Transformer的电力系统暂态稳定评估方法.首先,通过时域仿真采集电力系统受扰后的电压幅值及相角特征构建起特征矩阵;然后,基于Swin Transformer,本文提出一种空间跨尺度卷积注意力模块,用来替代原来的多头自注意力模块,该模块通过一系列不同卷积核大小的卷积层,能够充分提取到不同维度的有效特征,进而实现更为准确的预测结果.最后,通过在修改后的New England 10机39节点系统及IEEE 50机145节点系统中进行仿真实验,预测准确率分别达到99.05%和99.00%,多摆失稳误判率为0.35%和0.27%,这表明所提方法不仅能够对不同的失稳模式进行准确的预测,同时在噪声及PMU特征缺失情况下仍表现出优越的鲁棒性.
Power system transient stability prediction method based on SCSC-Swin Transformer
In modern power systems,instability modes have become increasingly diversified following disturbances,necessitating the accurate identification of various instability modes to implement appropriate control measures and prevent significant losses.Therefore,a transient stability assessment method for power systems based on an improved Swin Transformer is proposed in this paper.Firstly,time-domain simulations are conducted to collect voltage magnitude and phase angle characteristics following disturbances,which are used to construct a feature matrix.Then,building upon the Swin Transformer,a spatial cross-scale convolutional attention module is introduced to replace the original multi-head self-attention module.This new module utilizes a series of convolutional layers with different kernel sizes to effectively extract features across multiple dimensions,leading to more accurate prediction results.Finally,simulation experiments on the modified New England 10-machine 39-bus system and IEEE 50-machine 145-bus system show prediction accuracies of 99.05%and 99.00%,respectively,with multi-swing instability misjudgment rates of 0.35%and 0.27%.These results demonstrate that the proposed method not only accurately predicts different instability modes but also exhibits superior robustness in the presence of noise and missing PMU features.
万方数据
维普
