An integrative approach to transient power system analysis with standard and ANN-based dynamic models

The paper addresses practical aspects of integration of classical and artificial neural network (ANN)-based dynamic equivalents for transient analysis of large-scale interconnected power systems. Following standard model reduction procedures in power systems, we divide the system in two parts - one retained in detail, and the remainder that is to be replaced with a simpler ("equivalent") model. Our equivalent is based on two ANNs, and is derived from measurements at boundary points. In this paper we focus on a complete two-way interaction between the retained portion of the system that is modeled with explicit differential-algebraic equations (DAEs) and the ANN-based equivalent. The key issue here is the consistency between dynamic variables produced by the two different mechanisms at boundary points, and a two-way communication between the corresponding software modules. The overall model is denoted as hybrid, and the paper proposes a fast two-way iterative interaction algorithm. We demonstrate the feasibility of implementing our approach in standard industrial software by integrating our ANN-based equivalent with the Matlab power system toolbox. We illustrate capabilities of our approach to transient analysis on a benchmark multi-machine example, derived from the New England/New York power system.