A gray-box harmonic resonance frequency identification method of multiple-inverter-fed power system oriented to system designers

This paper presents a gray-box harmonic resonance frequency identification method of multiple-inverter-fed power system, which enables modal analysis oriented to system designers based on only frequency response data provided by diverse vendors or measured by frequency scanning. First, admittance transfer functions of all grid-connected inverters (GCIs) are fitted using Matrix Pencil Method-Vector Fitting (MPM-VF) combined method. Then, node admittance matrix (NAM) is formed according to the topology of whole system. Finally, harmonic resonance frequency along with changes in number of GCIs are identified by NAM-based modal analysis (MA). The proposed gray-box identification method is implemented in a typical multiple-inverter-fed power system. The correctness of harmonic resonance frequency identification results and the effectiveness of the presented method are verified by simulation results obtained in Matlab/Simulink platform and OPAL-RT digital real-time simulation platform. Based on the identification results, a more stable and better power quality multiple-inverter-fed power system can be built by system designers though avoiding the appearance of harmonic sources with corresponding resonance frequency.

Effect of Salbutamol on Lung Ventilation in Children with Cystic Fibrosis: Comprehensive Assessment Using Spirometry, Multiple-Breath Washout, and Functional Lung Magnetic Resonance Imaging

<b><i>Background:</i></b> Inhalation therapy is one of the cornerstones of the daily treatment regimen in patients with cystic fibrosis (CF). Recommendations regarding the addition of bronchodilators, especially salbutamol are conflicting due to the lack of evidence. New diagnostic measures such as multiple-breath washout (<underline>MBW)</underline> and functional magnetic resonance imaging (MRI) have the potential to reveal new insights into bronchodilator effects in patients with CF. <b><i>Objective:</i></b> The objective of the study was to comprehensively assess the functional response to nebulized inhalation with salbutamol in children with CF. <b><i>Methods:</i></b> Thirty children aged 6–18 years with stable CF performed pulmonary function tests, MBW, and matrix pencil-MRI before and after standardized nebulized inhalation of salbutamol. <b><i>Results:</i></b> Lung clearance index decreased (improved) by −0.24 turnover (95% confidence interval [CI]: −0.53 to 0.06; <i>p</i> = 0.111). Percentage of the lung volume with impaired fractional ventilation and relative perfusion decreased (improved) by −0.79% (CI: −1.99 to 0.42; <i>p</i> = 0.194) and −1.31% (CI: −2.28 to −0.35; <i>p</i> = 0.009), respectively. Forced expiratory volume (FEV₁) increased (improved) by 0.41 z-score (CI: 0.24–0.58; <i>p</i> &#x3c; 0.0001). We could not identify specific clinical factors associated with a more pronounced effect of salbutamol. <b><i>Conclusions:</i></b> There is a positive short-term effect of bronchodilator inhalation on FEV₁ in patients with CF, which is independent of ventilation inhomogeneity. Heterogeneous response between patients suggests that for prediction of a therapeutic effect this should be tested by spirometry in every patient individually.

State-Feedback Control in Descriptor Discrete-Time Fractional-Order Linear Systems: A Superstability-Based Approach

In this article, the superstabilizing state-feedback control problem in descriptor discrete-time fractional-order linear (DDFL) systems with a regular matrix pencil is studied. Methods for investigating the stability and superstability of the considered class of dynamical systems are presented. Procedures for the computation of the static state-feedback (SSF) and dynamic state-feedback (DSF) gain matrices such that the closed-loop DDFL (CL-DDFL) system is superstable are presented. A numerical example is used to show the efficacy of the presented approach. Our considerations were based on the Drazin inverse matrix method.

Estimate the spectrum of affine dynamical systems from partial observations of a single trajectory data

In this paper, we study the nonlinear inverse problem of estimating the spectrum of a system matrix, that drives a finite-dimensional affine dynamical system, from partial observations of a single trajectory data. In the noiseless case, we prove an annihilating polynomial of the system matrix, whose roots are a subset of the spectrum, can be uniquely determined from data. We then study which eigenvalues of the system matrix can be recovered and derive various sufficient and necessary conditions to characterize the relationship between the recoverability of each eigenvalue and the observation locations. We propose various reconstruction algorithms 1with theoretical guarantees, generalizing the classical Prony method, ESPRIT, and matrix pencil method. We test the algorithms over a variety of examples with applications to graph signal processing, disease modeling and a real-human motion dataset. The numerical results validate our theoretical results and demonstrate the effectiveness of the proposed algorithms, even when the data did not follow an exact linear dynamical system.