Measurement-based coherency identification and aggregation for power systems

Model reduction techniques are often applied to large-scale complex power systems to increase simulation performance. The bottleneck of existing methods to get a high reduction ratio lies in: (1) Coherency identification is static and conservative. Some coherent generators are not detected when system topology or operating point changes. (2) Solitary generators outside any coherency group are not aggregated regardless of their importance. To overcome the first problem, a measurement-based online coherency identification method was used in this paper. By analyzing post-fault trajectories measured by phasor measurement units (PMUs), coherency generators were identified through principal component analysis. The method can track conherency groups with time-varying system topology and operating points. To address the second problem, sensitivity analysis was employed into model reduction in this paper. The sensitivity of tie-line power flows against injected active power of external system generators was derived. Those generators having minimal impacts on tie-line power flows were replaced with negative impedances. Case studies show that the proposed method can handle well these solitary generators and the reduction ratio can be enhanced. Future work will include generalization of the sensitivity method.