arXiv subjects

Christian Wiedemann

Publications and source records attributed to Christian Wiedemann.

4 recordsLinked to original sources

arXiv2026

Anomalous Superfluid Response in Altermagnetic Superconductors

We report on the emergence of the anomalous (paramagnetic) superfluid response in altermagnetic superconductors at arbitrary impurity concentrations. Due to anisotropic gapless superconductivity, altermagnetic superconductors with an out-of-plane Zeeman field display an anisotropic paramagnetic Meissner effect. The effect is strongest for parallel altermagnetic and Zeeman exchange field vectors and in the clean sample. The presence of nonmagnetic impurities leads to isotropisation and, consequently, weakens the effect; however, the paramagnetic response sustains intermediate amounts of impurities in the system. As demonstrated in recent experiments, microwave superfluid stiffness measurements can serve as a sensitive probe of gapless superconductivity.

cond-mat.supr-con↗

arXiv2024

Extension of the Langevin power curve analysis by separation per operational state

In the last few years, the dynamical characterization of the power output of a wind turbine by means of a Langevin equation has been well established. For this approach, temporally highly resolved measurements of wind speed and power output are used to obtain the drift and diffusion coefficients of the energy conversion process. These coefficients fully determine a Langevin stochastic differential equation with Gaussian white noise. The drift term specifies the deterministic behavior of the system whereas the diffusion term describes the stochastic behavior of the system. A precise estimation of these coefficients is essential to understand the dynamics of the power conversion process of the wind turbine. We show that the dynamics of the power output of a wind turbine have a hidden dependency on turbine's different operational states. Here, we use an approach based on clustering Pearson correlation matrices for different observables on a moving time window to identify different operational states. We have identified five operational states in total, for example the state of rated power. Those different operational states distinguish non-stationary behavior in the mutual dependencies and represent different turbine control settings. As a next step, we condition our Langevin analysis on these different states to reveal distinctly different behaviors of the power conversion process for each operational state. Moreover, in our new representation hysteresis effects which have typically appeared in the Langevin dynamics of wind turbines seem to be resolved. We assign these typically observed hysteresis effects clearly to the change of the wind energy system between our estimated different operational states. In this contribution, we discuss further consequences for the meaning of hysteric switching and detection of malbehaviors in wind turbines.

physics.data-an↗

arXiv2024

Local statistical moments to capture Kramers-Moyal coefficients

This study introduces an innovative local statistical moment approach for estimating Kramers-Moyal coefficients, effectively bridging the gap between nonparametric and parametric methodologies. These coefficients play a crucial role in characterizing stochastic processes. Our proposed approach provides a versatile framework for localized coefficient estimation, combining the flexibility of nonparametric methods with the interpretability of global parametric approaches. We showcase the efficacy of our approach through use cases involving both stationary and non-stationary time series analysis. Additionally, we demonstrate its applicability to real-world complex systems, specifically in the energy conversion process analysis of a wind turbine.

stat.ME↗

arXiv2024

Dynamics of wind turbine operational states

Modern wind turbines gather a wealth of data with Supervisory Control And Data Acquisition (SCADA) systems. We study the short-term mutual dependencies of a variety of observables by evaluating Pearson correlation matrices on a moving time window. Using clustering on these matrices, we identify multiple stable operational states, which characterize the non-stationarity of mutual dependencies at a single turbine. They represent different turbine operational settings. Moreover, we combine the clustering analysis with a construction of a stochastic process to study the switching dynamics of those states in more detail. Calculating the distances between correlation matrices we obtain a time series that describes the behavior of the complex system in a collective way. Assuming this time series to be governed by a Langevin equation, we estimate the deterministic (drift) and stochastic (diffusion) components of the dynamics to understand the underlying non-stationarity. After adapting our method to specific features of our data, we are able to study the dynamics of operational states and their transitions as well as to resolve hysteresis effects.

physics.flu-dyn↗