Eigenvalue and frequency-domain analysis of the intermountain power project and the wscc network
The Western Systems Coordinating Council (WSCC) has several interarea modes of oscillation in the range of 0.3 to 0.8 Hz. The results of an investigation of how various network parameters and operating conditions affect the stability of those modes are presented. Eigenvalue and frequency-domain techniques are used. The study results show that numerous system parameters can have a significant impact on overall network stability. These parameters can include control settings, such as those in HVDC systems or power system stabilizers, or operational parameters, such as scheduled tie-line flows. The results show that careful analysis and tuning of all significant controls in a large network are necessary to ensure overall system stability and satisfactory performance. >
A Sparsity-Based Technique for Identifying Slow-Coherent Areas in Large Power Systems
A sparsity-based technique is developed for the identification of coherent areas in large power systems. The technique, based on the slow-coherency approach, is novel in that it introduces small machines at the load buses to retain the system sparseness. Then the computation of the slow eigenbasis for the identification of slow-coherent groups of machines is performed by the Lanczos algorithm which is an efficient eigenfunction computation method for large, sparse, symmetric but unstructured matrices. The technique also groups the load buses into coherent areas, information that is useful for network reduction. Two large scale models of portions of the U.S. power system are used as illustrations. The computation time required is of the order of magnitude of that required for a few load flow solutions.
Aggregate modeling of dynamic networks with sparse connections
This paper develops a time scale approach to the aggregation and decomposition of dynamic networks with dense and sparse connections. Two density parameters are used to characterize time scale and weak coupling properties. Bounds in terms of these parameters determine when there are two time scales in sparse networks. Simplified models of the slow and fast subsystems are proposed and physical interpretations are provided.