Data-Driven Partitioning of Power Networks Via Koopman Mode Analysis

作者：Raak Fredrik^*; Susuki Yoshihiko^*; Hikihara Takashi

来源：IEEE Transactions on Power Systems, 2016, 31(4): 2799-2808.

DOI：10.1109/TPWRS.2015.2464779

• Coherency identification
• power network partitioning
• power system monitoring
• spectral graph theory

This paper applies a new technique for modal decomposition based solely on measurements to test systems and demonstrates the technique's capability for partitioning a power network, which determines the points of separation in an is landing strategy. The mathematical technique is called the Koopman mode analysis (KMA) and stems from a spectral analysis of the so-called Koopman operator. Here, KMA is numerically approximated by applying an Arnoldi-like algorithm recently first applied to power system dynamics. In this paper we propose a practical data-driven algorithm incorporating KMA for network partitioning. Comparisons are made with two techniques previously applied for the network partitioning: spectral graph theory which is based on the eigenstructure of the graph Laplacian, and slow-coherency which identifies coherent groups of generators for a specified number of low-frequency modes. The partitioning results share common features with results obtained with graph theory and slow-coherency-based techniques. The suggested partitioning method is evaluated with two test systems, and similarities between Koopman modes and Laplacian eigenvectors are showed numerically and elaborated theoretically.

• 出版日期2016-7