Wire Rope Weak Defect Signal Processing Methods Based on Improved SVD and Phase Space Reconstruction

Due to the complexity of the interference operation environment of wire rope, the detection signals are usually weak and coupled in time-frequency domain, which makes the defect difficult to recognize, while the signal characterizations in phase space are also needed to be studied. Combining the nonlinear dynamic feature identification theories, phase space characteristics and chaotic features of wire rope defect detection signals are mainly investigated in this paper. First, principles of phase space reconstruction method for wire rope detection signals are presented by the chaotic dynamic indexes calculation of embedded dimension and delay time. Second, the change trends of the correlation dimension, approximate entropy and Lyapunov index of different phase space reconstructed wire rope defect detection signals are studied through the nonlinear simulation and analysis. Finally, a phase space reconstruction algorithm based on improved SVD is proposed, and the new algorithm is also compared with traditional signal processing methods. These results obtained by 6 groups of experiments were also evaluated and compared by the parameters of signal-to-noise ratio (SNR) and phase space trajectory chart, which manifests that the improved algorithm not only can increase the weak detection signal SNR to about 2.3dB of wire rope effectively, but also demonstrate the feasibility of the proposed methods in application.

State relevance for model order reduction applied to a microgrid

This paper presents a systematic model order reduction (MOR) algorithm based on state relevance applied to an islanded microgrid with electronic power generation. MOR of such islanded microgrids may not benefit, a priori, from the well-established time-scale separation usually applied to conventional power systems, and a systematic MOR is still an open issue. The proposed algorithm uses a balanced realization of the linear system, where state variables may not have physical meaning, to obtain the states' energies. It then calculates the relevance of the original system states from those energy values. The newly proposed ``state-relevance coefficient'' should help to choose which states to consider in a reduced model in each study case. Detailed nonlinear simulation results show that the proposed algorithm is able to find the relevant states to include in the reduced model systematically, even in operation points near the stability limit, where ad-hoc MOR techniques are likely to fail. The performance of the algorithm is illustrated in a system with grid-forming converters in various scenarios but can be easily applied to other systems.

State relevance for model order reduction applied to a microgrid

This paper presents a systematic model order reduction (MOR) algorithm based on state relevance applied to an islanded microgrid with electronic power generation. MOR of such islanded microgrids may not benefit, a priori, from the well-established time-scale separation usually applied to conventional power systems, and a systematic MOR is still an open issue. The proposed algorithm uses a balanced realization of the linear system, where state variables may not have physical meaning, to obtain the states' energies. It then calculates the relevance of the original system states from those energy values. The newly proposed ``state-relevance coefficient'' should help to choose which states to consider in a reduced model in each study case. Detailed nonlinear simulation results show that the proposed algorithm is able to find the relevant states to include in the reduced model systematically, even in operation points near the stability limit, where ad-hoc MOR techniques are likely to fail. The performance of the algorithm is illustrated in a system with grid-forming converters in various scenarios but can be easily applied to other systems.

Hybrid simulations of beta-induced Alfvén eigenmode with reversed safety factor profile

Based on the experimental parameters in HL-2A tokamak, hybrid simulations have been carried out to investigate the linear stability and nonlinear dynamics of BAE. It is found that the (m/n=3/2) beta-incuced Alfvén eigenmode (BAE) is excited by co-passing energetic ions with qmin=1.5 in linear simulation, and the mode frequency is consistent with experimental meuasurement. The simulation results show that the energetic ions βh, the injection velocity v0 and orbit width parameter ρh of energetic ions are important parameters determining the drive of BAE. Furthermore, the effect of qmin (with fixed shape of q profile) is studied, and it is found that: when qmin ≤ 1.50, the excited modes are BAEs, which are located near q=1.50 rational surfaces; when qmin > 1.50, the excited modes are simillar to the reversed-shear Alfvén eigenmodes (RSAEs), which are mainly localized around q=qmin surfaces. Nonlinear simulation results show that the nonlinear dynamics of BAE is sensitive to the EP drive. For strongly driven case, firstly, redistribution and transport of engetic ions are trigged by (m/n=3/2) BAE, which raised the radial gradient of energetic ions distribution function near q=2 rational surface, and then an EPM (m/n=4/2) is driven in nonlinear phase. Finally, these two instabilities triggered significant redistribution of energetic ions, which results in the twice-repeated and mostly-downward frequency chirping of (m/n=3/2) BAE. For weakly driven case, there are no (m/n=4/2) EPM being driven and twice-repeated chirping in nonlinear phase, since the radial gradient near q=2 rational surface is small and almost unchanged.

Intelligent Speed Regulation Control in Switched Reluctance Motor of Electric Vehicle Based on Neural Network Parameter Identification

This paper studies the intelligent speed regulation control of switched reluctance motor of electric vehicle based on neural network parameter identification. Starting with the analysis of the performance of switched reluctance motor, the nonlinear flux linkage characteristic inversion model and torque characteristic model of switched reluctance motor are established based on BP neural network. This paper studies and improves the fast self configuration algorithm of BP neural network. Finally, the nonlinear simulation model of switched reluctance motor is established under Matlab/Simulink. The model can be used for further control research. In this paper, the integrated control method of instantaneous torque control based on torque observation and three-step commutation control is studied, and the simulation analysis is carried out. The results show that this method can effectively reduce the torque ripple of switched reluctance motor and improve the performance of its drive system.

Unscented Kalman filter for airship model uncertainties and wind disturbance estimation

An airship is lighter than an air vehicle with enormous potential in applications such as communication, aerial inspection, border surveillance, and precision agriculture. An airship model is made up of dynamic, aerodynamic, aerostatic, and propulsive forces. However, the computation of aerodynamic forces remained a challenge. In addition to aerodynamic model deficiencies, airship mass matrix suffers from parameter variations. Moreover, due to the lighter-than-air nature, it is also susceptible to wind disturbances. These modeling issues are the key challenges in developing an efficient autonomous flight controller for an airship. This article proposes a unified estimation method for airship states, model uncertainties, and wind disturbance estimation using Unscented Kalman Filter (UKF). The proposed method is based on a lumped model uncertainty vector that unifies model uncertainties and wind disturbances in a single vector. The airship model is extended by incorporating six auxiliary state variables into the lumped model uncertainty vector. The performance of the proposed methodology is evaluated using a nonlinear simulation model of a custom-developed UETT airship and is validated by conducting a kind of error analysis. For comparative studies, EKF estimator is also developed. The results show the performance superiority of the proposed estimator over EKF; however, the proposed estimator is a bit expensive on computational grounds. However, as per the requirements of the current application, the proposed estimator can be a preferred choice.

Unsupervised Wireless Network Model-Assisted Abnormal Warning Information in Government Management

The data generated through telecommunication networks has grown exponentially in the last few years, and the resulting traffic data is unlikely to be processed and analyzed by manual style, especially detecting unintended traffic consumption from normal patterns remains an important issue. This area is critical because anomalies may lead to a reduction in network efficiency. The origin of these anomalies may be a technical problem in a cell or a fraudulent intrusion in the network. Usually, they need to be identified and fixed as soon as possible. Therefore, in order to identify these anomalies, data-driven systems using machine learning algorithms are developed with the aim from the raw data to identify and alert the occurrence of anomalies. Unsupervised learning methods can spontaneously describe the data structure and derive network patterns, which is effective for identifying unintended anomalous behavior and detecting new types of anomalies in a timely manner. In this paper, we use different unsupervised models to analyze traffic data in wireless networks, focusing on models that analyze traffic data combined with timeline information. The factor analysis method is used to derive the results of factor analysis, obtain the three major public factors and comprehensive factor scores, and combine the results with the BP neural network model to conduct a nonlinear simulation study on local governmental debt risk. A potential semantic analysis model based on Gaussian probability is presented and compared with other methods, and experimental results show that this model can provide a robust, over-the-top anomaly detection in a fully automated, data-driven solution.