Verification and Validation of Physics-Informed Surrogate Component Models for Dynamic Power-System Simulation

2026-03-18

AI Summary

This study addresses the degradation of accuracy observed when physics-informed surrogate models—despite performing well in isolated evaluations—are integrated into dynamic power system simulators, particularly under high-stress operating conditions. Framing this challenge as a verification and validation (V&V) problem, the work proposes a theoretical framework that combines algebraic coupling sensitivity, dynamic error amplification mechanisms, and finite-time simulation horizons to bound prediction errors. The approach integrates model-based verification with data-driven conformal calibration. Using a physics-informed neural network as a surrogate for synchronous machine dynamics, the authors conduct residual analysis and generate conformal predictions within a differential-algebraic equation simulation framework. Their findings reveal that small residuals in governing equations do not necessarily guarantee small trajectory errors, thereby underscoring the critical need—and a new paradigm—for validating surrogate models in coupled dynamical environments.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Intelligent Robots: State Estimation

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Physics-informed machine learning surrogates are increasingly explored to accelerate dynamic simulation of generators, converters, and other power grid components. The key question, however, is not only whether a surrogate matches a stand-alone component model on average, but whether it remains accurate after insertion into a differential-algebraic simulator, where the surrogate outputs enter the algebraic equations coupling the component to the rest of the system. This paper formulates that in-simulator use as a verification and validation (V\&V) problem. A finite-horizon bound is derived that links allowable component-output error to algebraic-coupling sensitivity, dynamic error amplification, and the simulation horizon. Two complementary settings are then studied: model-based verification against a reference component solver, and data-based validation through conformal calibration of the component-output variables exchanged with the simulator. The framework is general, but the case study focuses on physics-informed neural-network surrogates of second-, fourth-, and sixth-order synchronous-machine models. Results show that good stand-alone surrogate accuracy does not by itself guarantee accurate in-simulator behavior, that the largest discrepancies concentrate in stressed operating regions, and that small equation residuals do not necessarily imply small state-trajectory errors.

Problem

• verification and validation
• physics-informed surrogate
• dynamic power-system simulation
• differential-algebraic equations
• component-model accuracy

Innovation

• physics-informed surrogate
• verification and validation
• dynamic power-system simulation
• conformal calibration
• differential-algebraic equations