在双碳目标下,构建以新能源为主体的新型电力系统是实现电力工业转型升级的主要方向和关键途径,新型电力系统背景下快速准确的暂态功角稳定评估研究具有重要意义.为此,基于物理信息嵌入序列到序列(PI-seq2seq)神经网络与级联卷积神经网络模型提出一种含构网型新能源的新型电力系统暂态功角稳定评估方法.首先,采用PI-seq2seq网络结构预测未来功角轨迹,通过构造含物理损失项的损失函数引导模型训练过程,避免时域仿真耗时过长影响快速暂态评估.其次,级联卷积神经网络以预测的功角轨迹作为输入评估暂态稳定情况及其置信度,并配置评估置信度阈值判断机制以实现非固定评估长度的暂态稳定判断,克服了固定功角曲线长度对评估结果的影响.最后,在Kundur系统中进行验证,仿真结果表明:所提方法在功角曲线预测与稳定评估方法均获得令人满意的结果.

Against the backdrop of “dual carbon” goals, the construction of a new power system with new energy as the main component is the main direction and key way for the transformation and upgrading of the power industry. Research into fast and accurate evaluation of transient power angle stability in the context of new power systems is of great significance. To address this, a new transient power angle stability evaluation method is proposed for power systems with grid-forming new energy based on the physics-informed sequence-to-sequence (PI-seq2seq) neural networks and cascaded convolutional neural networks models. First, the PI-seq2seq network structure is used to predict the future power angle trajectory, and a loss function with physical loss terms is constructed to guide the model training process, which avoids the long-duration time-domain simulation to ensure fast transient evaluation. Then, predicted power angle trajectory is taken as input by the cascade convolutional neural networks to evaluate the transient stability and its confidence level. A threshold judgment mechanism for the evaluation confidence level is configured to realize the transient stability judgment of the non-fixed evaluation length, which overcomes the impact of the fixed power angle curve length on the evaluation results. Finally, the method proposed is verified in the Kundur system, and the simulation results show that it has obtained satisfactory results in both the power angle curve prediction and the stability evaluation.