# Online Measurement-Based Estimation of Dynamic System State Matrix in Ambient Conditions

## TL;DR

This paper introduces a measurement-based, real-time method to estimate the dynamic system state matrix in power systems, independent of network and generator models, effective even under noise and missing data.

## Contribution

It presents a novel recursive algorithm leveraging the Ornstein-Uhlenbeck process for accurate, model-independent estimation of the system state matrix in ambient conditions.

## Key findings

- Accurately estimates the system state matrix during topology changes.
- Robust against measurement noise and missing data.
- Effective with higher-order generator models and control devices.

## Abstract

In this paper, a purely measurement-based method is proposed to estimate the dynamic system state matrix by applying the regression theorem of the multivariate Ornstein-Uhlenbeck process. The proposed method employs a recursive algorithm to minimize the required computational effort, making it applicable to the real-time environment. One main advantage of the proposed method is model independence, i.e., it is independent of the network model and the dynamic model of generators. Among various applications of the estimated matrix, detecting and locating unexpected network topology change is illustrated in details. Simulation studies have shown that the proposed measurement-based method can provide an accurate and efficient estimation of the dynamic system state matrix under the occurrence of unexpected topology change. Besides, various implementation conditions are tested to show that the proposed method can provide accurate approximation despite measurement noise, missing PMUs, and the implementation of higher-order generator models with control devices.