Relationships between the Southwest Monsoon Surge and the Heavy Rainfall Associated with Landfalling Super Typhoon Rammasun

Based on the China Meteorological Administration (CMA) best-track data, the ERA5 reanalysis, and the Global Precipitation Measurement (GPM) precipitation data, this paper analyzes the reasons for the heavy rainfall event of Super Typhoon Rammasun in 2014, and the results are as follows: (1) Rammasun was blocked by the western Pacific subtropical high (WPSH), the continental high, and the mid-latitude westerly trough. Such a stable circulation pattern maintained the vortex circulation of Rammasun. (2) During the period of landfall, the southwest summer monsoon surge was reinforced due to the dramatic increase of the zonal wind and the cross-equatorial flow near 108° E. The results of the dynamic monsoon surge index (DMSI) and boundary water vapor budget (BWVB) show that the monsoon surge kept providing abundant water vapor for Rammasun, which led to the enhanced rainfall. (3) The East Asian monsoon manifested an obvious low-frequency oscillation with a main period of 20–40 days in the summer of 2014, which propagated northward significantly. When the low-frequency oscillation reached the extremely active phase, the monsoon surge hit the maximum and influenced the circulation of Rammasun. Meanwhile, the convergence and water vapor flux associated with the low-frequency oscillation significantly contributed to the heavy rainfall.

Inertia Control Strategy of DFIG-Based Wind Turbines Considering Low-Frequency Oscillation Suppression

It has become a basic requirement for wind turbines (WTs) to provide frequency regulation and inertia support. The influence of WTs on the low-frequency oscillation (LFO) of the system will change after adopting inertia control methods. This paper intends to investigate and compare in detail the IC effects on LFO characteristics in two systems with different structures. First, the mechanism of inertia control of doubly fed induction generator (DFIG)-based WTs is analyzed. Then, the small-signal analysis method and modal analysis method are used to study the influence of the inertia control on the LFO characteristics based on the two-machine infinite-bus system and the four-machine two-area system, respectively. The difference in impact rules of IC on LFO is compared in detail. Finally, considering that the inertia control might worsen the LFO in some systems, an improved inertia control strategy of DFIG-based WTs is proposed to suppress the LFO. The simulation results demonstrate that, in systems with different structures, the impact rules of the inertia control parameters on LFO are different. With the improved inertia control strategy, DFIG-based WTs can suppress the LFO of the system and provide inertia support for the system.

Research on Governor Parameter Optimization to Suppress Ultra-Low Frequency Oscillation of Power System Caused by Hydropower Unit

In the actual power system with hydropower, long-time and ultra-low frequency oscillation events occur many times. It is found that the unreasonable setting of governor parameters is an important reason for the oscillation. Firstly, the single machine on load system model is used to analyse the relationship between the PID parameters of the governor and the system stability, then the relationship between oscillation mode and PID parameters of governor is analyzed by eigenvalue analysis method, and the negative damping provided by speed regulation system is analyzed by damping torque method, and then the particle swarm optimization algorithm is used to optimize the PID parameters. Through the analysis of the step response of the single machine system before and after the optimization and the damping torque coefficient provided by the speed regulation system, it shows the effectiveness of the optimization algorithm. Finally, in the simulation platform MATLAB/SIMULINK, a single machine load system model which is closer to the actual power grid is built. The governor parameters of the generator are simulated and verified, and the PID parameters are adjusted by using the parameters obtained by the optimization algorithm. The results show that the optimized parameters have a good suppression for the ultra-low frequency oscillation.

A Novel EMG-Based Hand Gesture Recognition Framework Based on Multivariate Variational Mode Decomposition

Surface electromyography (sEMG) is a kind of biological signal that records muscle activity noninvasively, which is of great significance in advanced human-computer interaction, prosthetic control, clinical therapy, and biomechanics. However, the number of hand gestures that can be recognized is limited and the recognition accuracy needs to be further improved. These factors lead to the fact that sEMG products are not widely used in practice. The main contributions of this paper are as follows. Firstly, considering the increasing number of gestures to be recognized and the complexity of gestures, an extensible two-stage machine learning lightweight framework was innovatively proposed for multi-gesture task recognition. Secondly, the multivariate variational mode decomposition (MVMD) is applied to extract the spatial–temporal features from the multiple channels to the EMG signals, and the separable convolutional neural network is used for modelling. In this work, the experimental results for 52 hand gestures recognition task show that the average accuracy on each stage is about 90%. The potential movement information is mainly contained in the low-frequency oscillator of the sEMG signal, and the model performs better with the low-frequency oscillation from the MVMD algorithm on the second stage classification than that of other decomposition methods.

Metabolic Low-Frequency Oscillation and Abbreviated Protocol for Estimating REE by Indirect Calorimetry in Healthy Adults

Objectives: The aim of this study is to propose a new wave protocol to identify low-frequency oscillations for evaluating resting energy expenditure (REE) and compare its performance with the 5-minute interval abbreviated protocol and standard protocol. Research methods & procedures: Consecutive 20-minute indirect calorimetry (IC) was used to collect metabolic data from 23 women and 37 men (between 23 and 43 years old). Sliding window filter algorithms were used to eliminate noise. Three protocols were used to evaluate REE: averaging the data between two consecutive waves (wave protocol), averaging the second 5-minute intervals (interval protocol), and averaging the last 15-minute REE (standard protocol). Results: Based on 60 healthy participants' metabolic data, compared with the interval protocol, the wave protocol showed better consistency with the standard protocol. The mean bias (limits of agreement) using the wave protocol was 0.3458% (-7.817% to 8.509%), and that using the interval protocol was -1.720% (-16.06% to 12.62%). The time required to evaluate REE with the wave protocol and interval protocol was measured. The measurement time for the interval protocol was 10 minutes, while the average measurement time for the wave protocol was 9.75 minutes. Conclusions: We recommend the wave protocol for estimating REE in healthy people. This abbreviated protocol can identify low-frequency oscillations and consider individual differences to more accurately reflect the baseline REE compared to the interval protocol. Compared with the standard protocol, the measurement time of the wave protocol was reduced by nearly half (from 20 minutes (standard protocol) to 9.75 minutes).