AI 中文总结
本文提出一种基于实际电压响应的无监督学习框架，采用层次凝聚聚类选择代表性运行点，可在高RES渗透率电力系统中大幅减少年度运行点数量，且电压行为重构精度高，性能优于现有方法。
AI 中文摘要
在高可再生能源（RES）渗透率的电力系统中，电压安全评估需要分析大量运行工况以捕捉变异性和不确定性，但模拟全年的运行点计算成本高昂，这促使人们选择代表性运行点（ROPs）。现有大多数方法对需求和发电曲线进行聚类，但这些曲线的相似性并不能保证电压行为的相似性，因为无功功率极限、电压控制动作以及非线性网络相互作用会以无法从功率曲线模式推断的方式塑造电压响应。本文提出一种无监督学习框架，该框架基于系统的实际电压响应选择ROPs：每个运行点由交流潮流解得到的全系统电压风险指数表示，主成分分析用于降维，采用Ward链接的层次凝聚聚类识别代表性电压状态。随后，一套综合评估标准用于衡量所选ROPs在正常及故障工况下重现全年电压安全特性的效果。在高RES渗透率下的IEEE电压测试系统上，该框架将年度运行点集减少了99.66%，同时在稳态下以98.3%的重构精度重现全年电压行为，故障后响应下的重构精度为93.4%，优于现有的注入空间聚类和启发式采样方法。
英文摘要
Voltage security assessment in power systems with high renewable energy source (RES) penetration requires analyzing many operating conditions to capture variability and uncertainty, but simulating a full year of operating points is computationally costly - motivating the selection of representative operating points (ROPs). Most existing methods cluster demand and generation profiles, but similarity in these profiles does not guarantee similarity in voltage behavior, since reactive power limits, voltage-control actions, and nonlinear network interactions shape voltage response in ways that cannot be inferred from power profile patterns. This paper proposes an unsupervised learning framework that selects ROPs based on the system's actual voltage response: each operating point is represented by system-wide voltage-risk indices from AC power-flow solutions, Principal Component Analysis reduces dimensionality, and Hierarchical Agglomerative Clustering with Ward linkage identifies representative voltage regimes. A comprehensive set of evaluation criteria then measures how well the selected ROPs reproduce the full year's voltage-security characteristics under normal and contingency conditions. On the IEEE Voltage Test System under very high RES penetration, the framework reduces the annual operating point set by 99.66 percent while reproducing full-year voltage behavior with 98.3 percent reconstruction accuracy in steady state and 93.4 percent in post-contingency response, outperforming existing injection-space clustering and heuristic sampling.
CommentsIn preparation for submission to IEEE
