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.

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.

Matrix Pencil Method for Vital Sign Detection from Signals Acquired by Microwave Sensors

Microwave sensors have recently been introduced as high-temporal resolution sensors, which could be used in the contactless monitoring of artery pulsation and breathing. However, accurate and efficient signal processing methods are still required. In this paper, the matrix pencil method (MPM), as an efficient method with good frequency resolution, is applied to back-reflected microwave signals to extract vital signs. It is shown that decomposing of the signal to its damping exponentials fulfilled by MPM gives the opportunity to separate signals, e.g., breathing and heartbeat, with high precision. A publicly online dataset (GUARDIAN), obtained by a continuous wave microwave sensor, is applied to evaluate the performance of MPM. Two methods of bandpass filtering (BPF) and variational mode decomposition (VMD) are also implemented. In addition to the GUARDIAN dataset, these methods are also applied to signals acquired by an ultra-wideband (UWB) sensor. It is concluded that when the vital sign is sufficiently strong and pure, all methods, e.g., MPM, VMD, and BPF, are appropriate for vital sign monitoring. However, in noisy cases, MPM has better performance. Therefore, for non-contact microwave vital sign monitoring, which is usually subject to noisy situations, MPM is a powerful method.

Locating Ships Using Time Reversal and Matrix Pencil Method by Their Underwater Acoustic Signals

This paper presents a technique, based on the matrix pencil method (MPM), for the compression of underwater acoustic signals produced by boat engines. The compressed signal, represented by its complex resonance expansion, is intended to be sent over a low-bit-rate wireless communication channel. We demonstrate that the method can provide data compression greater than 60%, ensuring a correlation greater than 93% between the reconstructed and the original signal, at a sampling frequency of 2.2 kHz. Once the signal was reconstituted, a localization process was carried out with the time reversal method (TR) using information from four different sensors in a simulation environment. This process sought to achieve the identification of the position of the ship using only passive sensors, considering two different sensor arrangements.