Data-Driven Partitioning of Power Networks Via Koopman Mode Analysis

• Published in: IEEE transactions on power systems Vol. 31; no. 4; pp. 2799 - 2808
• Main Authors: Raak, Fredrik, Susuki, Yoshihiko, Hikihara, Takashi
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Coherency identification; Dynamical systems; Eigenvalues and eigenfunctions; Generators; Graph theory; Heuristic algorithms; Mathematical models; Networks; Partitioning; Partitioning algorithms; power network partitioning; Power system dynamics; power system monitoring; Spectra; spectral graph theory; Stems; Test systems
• ISSN: 0885-8950; 1558-0679
• DOI: 10.1109/TPWRS.2015.2464779

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 islanding 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.