Search Results (8)

This paper proposes the expressions for the motor airgap torque harmonics induced by a cascaded H-bridge inverter operating with failed cells. These variable frequency drive systems (VFDs), are widely used in oil and gas applications, where a torsional vibration evaluation is a critical challenge for field engineers. This paper proposes mathematical expressions that are crucial for an accurate torsional analysis during the design stage of VFDs, as required by international standards such as API 617, API 672, etc. By accurately reconstructing the electromagnetic torque from the stator voltages and currents in the (αβ0) reference frame, the obtained expressions enable the precise prediction of the exact locations of torque harmonics induced by the inverter under various real-world operating conditions, without the need for installed torque sensors. The neutral-shifted and peak-reduction fault-tolerant control techniques are commonly adopted under faulty operation of these VFDs. However, their effects on the pulsating torques harmonics in machine air-gap remain uncovered. This paper fulfils this gap by conducting a detailed evaluation of spectral characteristics of these fault-tolerant methods. The theoretical analyses are supported by MATLAB/Simulink 2024 based offline simulation and Typhoon based virtual real-time simulation results performed on a (4.16 kV and 7 MW) vector-controlled induction motor fed by a 7-level cascaded H-bridge inverter. According to the theoretical analyses- and simulation results, the Neutral-shifted and Peak-reduction approaches rebalance the motor input line-to-line voltages in the event of an inverter’s failed cells but, in contrast to the normal mode the carrier, all the triplen harmonics are no longer suppressed in the differential voltage and current spectra due to inequal magnitudes in the phase voltages. These additional current harmonics induce extra airgap torque components that can excite the lowly damped eigenmodes of the mechanical shaft found in the oil and gas applications and shut down the power conversion system due torsional vibrations. Full article

Studies on the geometry variation-related compressor uncertainty quantification (UQ) have often used dimension reduction methods, such as the principal component analysis (PCA), for the modeling of deviations. However, in the PCA method, the main eigenmodes were determined based only on the statistical behavior of geometry variations. While this process can cause some missing modes with a small eigenvalue, it is much more sensitive to blade aerodynamic performances, and thereby reducing the reliability of the UQ analysis. Hence, a novel geometry variation modeling method, named sensitivity-correlated principal component analysis (SCPCA), has been proposed. In addition, by means of the blade sensitivity analysis, the weighting factors for each eigenmode were determined and then used to modify the process of the PCA. As a result, by considering the covariance of geometry variations and the performance sensitivity, the main eigenmodes could be determined and used to reconstruct the blade samples in the UQ analysis. With 98 profile samples measured at the midspan of a high-pressure compressor rotor blade, both the PCA and SCPCA methods were employed for the UQ analysis. The results showed that, compared to the PCA method, the SCPCA method provided a more accurate reconstruction of sensitive deviations, leading to an 11.8% improvement in evaluating the scatter of the positive incidence range, while also maintaining the accuracy of the uncertainty assessment for other performances. Full article

The aim of this paper was to provide a more scientific and effective analysis of the fluctuation pattern of the Chinese potato market by extracting the characteristics of the price fluctuation cycle to effectively grasp the characteristics of price changes in the potato market, thus promoting the stable and healthy development of the Chinese potato industry, and to expand the application scenarios of the EEMD model to provide a reference for the study of price fluctuation patterns in other agricultural markets. This study used an ensemble empirical modal decomposition (EEMD) model to examine time-series data on Chinese wholesale potato market prices from January 2005 to December 2021. The results showed that (1) Chinese wholesale potato market prices are characterized by some rigidity, with sharp changes in growth rates; (2) Chinese wholesale potato market prices are dominated by short- and medium-term fluctuations, and the decomposed components can better reflect the characteristics of the original series fluctuations; (3) Chinese wholesale potato market monthly prices have long- and short-term fluctuations with a 6- and 19-month cycle, and are dominated by short-term high-frequency fluctuations; (4) monthly price fluctuations in the Chinese wholesale potato market are more intense in high-frequency than low-frequency fluctuations, and there is a strong correlation between high- and low-frequency fluctuations in precipitation, temperature and potato prices. Finally, suggestions were made for creating and improving a national potato price information platform and strengthening the information early warning mechanism; improving the potato production interest linkage mechanism and enhancing potato farmers’ ability to cope with market and natural risks; and improving the potato reserve system and potato storage facilities. Full article

Power systems have an increasing demand for operational condition monitoring and safety control aspects. Low-frequency oscillation mode identification is one of the keys to maintain the safe and stable operation of power systems. To address the problems of low accuracy and poor anti-interference of the current low-frequency oscillation mode identification method for power systems, a low-frequency oscillation mode feature identification method combining the adaptive variational modal decomposition and sparse time-domain method is proposed. Firstly, the grey wolf optimization algorithm (GWO) is used to find the optimal number of eigenmodes and penalty factor parameters of the variational modal decomposition (VMD). And the improved method (GWVMD) is used to decompose the measured signal with low-frequency oscillations and then reconstruct the signal to achieve a noise reduction. Next, the processed signal is used as a new input for the identification of the oscillation modes and their parameters using the sparse time-domain method (STD). Finally, the effectiveness of the method is verified by the actual low-frequency oscillation signal identification in the Hengshan power plant and numerical signal simulation experiments. The results show that the proposed method outperforms the conventional methods such as Prony, ITD, and HHT in terms of modal discrimination. Meanwhile, the overall reduction in the frequency error is 34, 44, and 21%, and the overall reduction in the damping error is 37, 41, and 18%, compared with the recently proposed methods such as the EFEMD-HT, RDT-ERA, and TLS-ESPRIT. The effectiveness of the methods in suppressing the modal confusion and noise immunity is demonstrated. Full article

The scattering of electromagnetic waves by a resonator is determined by the excitation of the eigenmodes of the system. In the case of open resonators made of absorbing materials, the system is non-Hermitian, and the eigenmodes are quasinormal modes. Among the whole set of quasinormal modes, static modes (modes with a zero eigenfrequency) occupy a specific place. We study the role of static modes in quasinormal modes expansions calculated with a numerical solver implemented with the finite-element method. We show that, in the case of a dielectric permittivity described by a Lorentz model, static modes markedly contribute to the electromagnetic field reconstruction but are incorrectly calculated with a solver designed to compute modes with non-zero eigenfrequencies. We propose to solve this issue by adding to the solver a separate, specific computation of the static modes. Full article

The complex and diverse structural forms make it impossible to define universal shape coefficients for large-span roof structures, which usually need to be obtained by wind tunnel tests. However, the number of test measurement points is limited, which leads to obvious limitations in the study of wind loads on large-span roof structures. Taking a large-span folded-plate roof as an example, based on the wind tunnel pressure test results of the rigid model, the proper orthogonal decomposition (POD) method is used to reconstruct the wind pressure field of the roof using the first several eigenmodes. The wind pressure of several typical characteristic points is predicted based on four different interpolations methods, and the accuracy and feasibility of POD method in reconstruction and prediction of wind pressure field of large-span roof are analyzed and studied from multiple perspectives. The results show that the order of the selected structural eigenmodes has an impact on the reconstruction accuracy of the wind pressure field. The more orders are selected, the closer the wind pressure field reconstruction is to the true value. The reconstruction effect of the wind pressure field based on the POD method is related to the spatial position of the predicted point, and the reconstruction effect of the wind field based on the fluctuating wind pressure is obviously better than the that based on the mean wind pressure. When the POD method is used to predict the wind pressure of an unknown point, different interpolation methods can achieve ideal results. Among them, the bilinear interpolation method has the highest prediction accuracy, and the adjacent point interpolation method and Griddata V4 interpolation method only have certain errors in the low frequency region. Full article

Isometric feature mapping (ISOMAP) is a nonlinear dimensionality reduction method used for extracting features from spatiotemporal data. The traditional principal component analysis (PCA), a linear dimensionality reduction method, measures the distance between two data points based on the Euclidean distance (line segment), which cannot reflect the actual distance between the data points in a nonlinear space. By contrast, the ISOMAP measures the distance between two data points based on the geodesic distance, which more closely reflects the actual distance by the view of tracing along the local linearity in the original nonlinear structure. Thus, ISOMAP-reconstructed data points can reflect the features of real structures and can be classified more accurately than traditional PCA-reconstructed data points. Moreover, these ISOMAP-reconstructed data points can be used for cluster analysis by emphasizing the differences among the points more than those by the traditional PCA. In this study, sea surface temperature (SST) data points reconstructed using the traditional PCA and ISOMAP were compared. The classification based on these reconstructed SST points was tested using the Niño 3.4 index, which labels El Niño, La Niña, or normal events. The mean differences from the ISOMAP data points were larger than those from the traditional PCA data points. The ISOMAP not only helped differentiate the points in two different events but also provided better difference measurement of the points belonging to the same class (e.g., 82/83 and 97/98 El Niño events). On examining the evolution of the leading three temporal eigen components of the SST PCA, or especially the SST ISOMAP, we found that the trajectories were similar to the Lorenz 63 model on a phase space figure. This implies that NWP perturbations can be traced using the ISOMAP to measure growing unstable behaviors. Spatial eigenmodes (empirical orthogonal function) between the traditional PCA and ISOMAP were also determined and compared herein. Full article

In this paper, we implement a Whispering Gallery mode microbubble resonator (MBR) as an optical transducer to detect the photoacoustic (PA) signal generated by plasmonic nanoparticles. We simulate a flow cytometry experiment by letting the nanoparticles run through the MBR during measurements and we estimate PA intensity by a Fourier analysis of the read-out signal. This method exploits the peaks associated with the MBR mechanical eigenmodes, allowing the PA response of the nanoparticles to be decoupled from the noise associated with the particle flow whilst also increasing the signal-to-noise ratio. The photostability curve of a known contrast agent is correctly reconstructed, validating the proposed analysis and proving quantitative PA detection. The experiment was run to demonstrate the feasible implementation of the MBR system in a flow cytometry application (e.g., the detection of venous thrombi or circulating tumor cells), particularly regarding wearable appliances. Indeed, these devices could also benefit from other MBR features, such as the extreme compactness, the direct implementation in a microfluidic circuit, and the absence of impedance-matching material. Full article