PMU-based dynamic state and parameter estimation for dynamic security assessment in power systems -- Ultimate boundedness in the presence of measurement noise
Dynamic state and parameter estimation methods for dynamic security assessment in power systems are becoming increasingly important for system operators. Usually, the data used for this type of applications stems from phasor measurement units (PMUs) and is corrupted by noise. In general, the impact of the latter may significantly deteriorate the estimation performance. This motivates the present work, in which it is proven that the state and parameter estimation method proposed by part of the authors in [1] and extended in [2] features the property that the estimation errors are ultimately bounded in the presence of PMU measurement data corrupted by bounded noise. The analysis is conducted for the third-order flux-decay model of a synchronous generator and holds independently of the employed automatic voltage regulator and power system stabilizer (if present). The analysis is illustrated by simulations.
Code (0)
등록된 구현이 없습니다.
Tasks
parameter estimationSimilar Papers 제목 키워드 기반
Physics-Informed Neural Networks for Non-linear System Identification for Power System Dynamics
Varying power-infeed from converter-based generation units introduces great uncertainty on system parameters such as inertia and damping. As a consequence, system operators face increasing challenges in performing dynami…
parameter estimationSecure State Estimation and Control for Cyber Security of AC Microgrids
A timely, accurate, and secure dynamic state estimation is needed for reliable monitoring and efficient control of microgrids. The synchrophasor technology enables us to obtain synchronized measurements in real-time and …
State EstimationDeep Learning of Dynamical System Parameters from Return Maps as Images
We present a novel approach to system identification (SI) using deep learning techniques. Focusing on parametric system identification (PSI), we use a supervised learning approach for estimating the parameters of discret…
Data Augmentationparameter estimationCoordinate ascent neural Kalman-MLE for state estimation
This paper presents a coordinate ascent algorithm to learn dynamic and measurement models in dynamic state estimation using maximum likelihood estimation in a supervised manner. In particular, the dynamic and measurement…
Graph Layer Security: Encrypting Information via Common Networked Physics
The proliferation of low-cost Internet of Things (IoT) devices has led to a race between wireless security and channel attacks. Traditional cryptography requires high-computational power and is not suitable for low-power…