一种基于发电机对相对能量的电力系统临界机群快速辨识方法

A Fast Approach to Identify Power System Critical Machines Cluster Based on Relative Energy of Pair-wise Generators

准确辨识临界失稳机组是暂态稳定分析的关键。该文在推导基于发电机机组对的相对暂态动、势能函数基础上,根据机组对在故障切除时刻和故障切除后两个时步内的相对能量聚集特性,采用两阶段聚类分群算法,对故障切除后极短时间内系统的临界机群进行了辨识。由于相对能量函数的计算不依赖于全系统的网络拓扑参数或故障位置信息,仅需实时采集系统中少量的发电机机端电气状态量,减小了因候选集合漏选重要机组带来的辨识误差,且计算相对简单,缩短了辨识时间。新英格兰10机39节点系统、IEEE 145节点系统算例结果验证了该方法的正确性和有效性。

The accurate identification of the critical machines cluster is the critical feature for transient stability analysis. Two novel relative transient kinetic and potential energy function are deduced by traditional transient energy function method which is based on pair-wise generators. According to the aggregation of these relative energy at fault clearing moment and two time steps later, a two-stage clustering algorithm is utilized to identify the critical machines cluster within a short fault cleared time. The derivation of the method shows that the proposed relative energy function relies on a part of machines＇ terminal electric parameters rather than a complete system network topology parameters or fault location information. So the proposed approach can reduce the omission error of important machines in a candidate set. And the identification time is reduced by the simple calculation progress. The correctness and effectiveness of the model are verified by simulation results of New England 10 generators 39 buses system and IEEE 145 buses system.