Research on Grid-Connected Control Strategy of Distributed Generator Based on Improved Linear Active Disturbance Rejection Control

The virtual synchronous generator(VSG)technology has been proposed to address the problem of system frequency and active power oscillation caused by grid-connected new energy power sources.However,the traditional voltage-current double-closed-loop control used in VSG has the disadvantages of poor disturbance immunity and insufficient dynamic response.In light of the issues above,a virtual synchronous generator voltage outer-loop control strategy based on improved linear autonomous disturbance rejection control(ILADRC)is put forth for consideration.Firstly,an improved first-order linear self-immunity control structure is established for the characteristics of the voltage outer loop;then,the effects of two key control parameters-observer bandwidthω_(0)and controller bandwidthω_(c)on the control system are analyzed,and the key parameters of ILADRC are optimally tuned online using improved gray wolf optimizer-radial basis function(IGWO-RBF)neural network.A simulationmodel is developed using MATLAB to simulate,analyze,and compare the method introduced in this paper.Simulations are performed with the traditional control strategy for comparison,and the results demonstrate that the proposed control method offers superior anti-interference performance.It effectively addresses power and frequency oscillation issues and enhances the stability of the VSG during grid-connected operation.

A Double-Interactively Recurrent Fuzzy Cerebellar Model Articulation Controller Model Combined with an Improved Particle Swarm Optimization Method for Fall Detection

In many Eastern and Western countries,falling birth rates have led to the gradual aging of society.Older adults are often left alone at home or live in a long-term care center,which results in them being susceptible to unsafe events(such as falls)that can have disastrous consequences.However,automatically detecting falls fromvideo data is challenging,and automatic fall detection methods usually require large volumes of training data,which can be difficult to acquire.To address this problem,video kinematic data can be used as training data,thereby avoiding the requirement of creating a large fall data set.This study integrated an improved particle swarm optimization method into a double interactively recurrent fuzzy cerebellar model articulation controller model to develop a costeffective and accurate fall detection system.First,it obtained an optical flow(OF)trajectory diagram from image sequences by using the OF method,and it solved problems related to focal length and object offset by employing the discrete Fourier transform(DFT)algorithm.Second,this study developed the D-IRFCMAC model,which combines spatial and temporal(recurrent)information.Third,it designed an IPSO(Improved Particle Swarm Optimization)algorithm that effectively strengthens the exploratory capabilities of the proposed D-IRFCMAC(Double-Interactively Recurrent Fuzzy Cerebellar Model Articulation Controller)model in the global search space.The proposed approach outperforms existing state-of-the-art methods in terms of action recognition accuracy on the UR-Fall,UP-Fall,and PRECIS HAR data sets.The UCF11 dataset had an average accuracy of 93.13%,whereas the UCF101 dataset had an average accuracy of 92.19%.The UR-Fall dataset had an accuracy of 100%,the UP-Fall dataset had an accuracy of 99.25%,and the PRECIS HAR dataset had an accuracy of 99.07%.

Improved Rotor Flux Observer for Sensorless Control of PMSM with Adaptive Harmonic Elimination and Phase Compensation

In this paper,a sensorless control strategy of a permanent magnet synchronous machine(PMSM)based on an improved rotor flux observer(IFO)is proposed.Due to the unknown integral initial value and the high harmonics caused by current sampling and inverter nonlinearities,the flux linkage estimated by traditional rotor flux observer may be inaccurate.In order to address these issues,a self-adaptive band-pass filter(SABPF)is designed to eliminate the DC component and high-frequency harmonics of the estimated equivalent rotor flux linkage.Furthermore,in order to avoid that the design of PI parameter is influenced by the amplitude of equivalent rotor flux linkage,an improved phase-locked loop(IPLL)is employed to obtain the rotor speed and to normalize the estimated equivalent rotor flux linkage.In addition,angle shift caused by an SABPF is compensated to improve the accuracy of the estimated flux linkage angle.Besides,the parameter robustness of this method is analyzed in detail.Finally,simulation and experimental results demonstrate the effectiveness and parameter robustness of the proposed method.

Identifying a competency improvement strategy for infection prevention and control professionals:A rapid systematic review and cluster analysis

Remarkable progress has been made in infection prevention and control(IPC)in many countries,but some gaps emerged in the context of the coronavirus disease 2019(COVID-19)pandemic.Core capabilities such as standard clinical precautions and tracing the source of infection were the focus of IPC in medical institutions during the pandemic.Therefore,the core competences of IPC professionals during the pandemic,and how these contributed to successful prevention and control of the epidemic,should be studied.To investigate,using a systematic review and cluster analysis,fundamental improvements in the competences of infection control and prevention professionals that may be emphasized in light of the COVID-19 pandemic.We searched the PubMed,Embase,Cochrane Library,Web of Science,CNKI,WanFang Data,and CBM databases for original articles exploring core competencies of IPC professionals during the COVID-19 pandemic(from January 1,2020 to February 7,2023).Weiciyun software was used for data extraction and the Donohue formula was followed to distinguish high-frequency technical terms.Cluster analysis was performed using the within-group linkage method and squared Euclidean distance as the metric to determine the priority competencies for development.We identified 46 studies with 29 high-frequency technical terms.The most common term was“infection prevention and control training”(184 times,17.3%),followed by“hand hygiene”(172 times,16.2%).“Infection prevention and control in clinical practice”was the most-reported core competency(367 times,34.5%),followed by“microbiology and surveillance”(292 times,27.5%).Cluster analysis showed two key areas of competence:Category 1(program management and leadership,patient safety and occupational health,education and microbiology and surveillance)and Category 2(IPC in clinical practice).During the COVID-19 pandemic,IPC program management and leadership,microbiology and surveillance,education,patient safety,and occupational health were the most important focus of development and should be given due consideration by IPC professionals.

Improved Bat Algorithm Based Energy Efficient Congestion Control Scheme for Wireless Sensor Networks

Energy conservation and congestion control are widely researched topics in Wireless Sensor Networks in recent years. The main objective is to develop a model to find the optimized path on the basis of distance between source and destination and the residual energy of the node. This paper shows an implementation of nature inspired improved Bat Algorithm to control congestion in Wireless Sensor Networks at transport layer. The Algorithm has been applied on the fitness function to obtain an optimum solution. Simulation results have shown improvement in parameters like network lifetime and throughput as compared with CODA (Congestion Detection and Avoidance), PSO (Particle Swarm Optimization) algorithm and ACO (Ant Colony Optimization).

Study of improved gray predictive PID control algorithm

According to these characteristics of the movement of the special platform servo,a new improved grey predictive PID control algorithm was proposed based on the grey predictive PID,and then the algorithm was simulated by MATLAB.As a result that it can improve the response speed and stability of the system,and meet the demand of the system.

Improved Bacterial Foraging Optimization Algorithm Based on Fuzzy Control Rule Base

Manual construction of a rule base for a fuzzy system is the hard and time-consuming task that requires expert knowledge.In this paper we proposed a method based on improved bacterial foraging optimization（IBFO）,which simulates the foraging behavior of “E.coli” bacterium,to tune the Gaussian membership functions parameters of an improved Takagi-Sugeno-Kang fuzzy system（C-ITSKFS） rule base.To remove the defect of the low rate of convergence and prematurity,three modifications were produced to the standard bacterial foraging optimization（BFO）.As for the low accuracy of finding out all optimal solutions with multi-method functions,the IBFO was performed.In order to demonstrate the performance of the proposed IBFO,multiple comparisons were made among the BFO,particle swarm optimization（PSO）,and IBFO by MATLAB simulation.The simulation results show that the IBFO has a superior performance.

A Control Strategy for Smoothing Active Power Fluctuation of Wind Farm with Flywheel Energy Storage System Based on Improved Wind Power Prediction Algorithm

The fluctuation of active power output of wind farm has many negative impacts on large-scale wind power integration into power grid. In this paper, flywheel energy storage system (FESS) was connected to AC side of the doubly-fed induction generator (DFIG) wind farm to realize smooth control of wind power output. Based on improved wind power prediction algorithm and wind speed-power curve modeling, a new smooth control strategy with the FESS was proposed. The requirement of power system dispatch for wind power prediction and flywheel rotor speed limit were taken into consideration during the process. While smoothing the wind power fluctuation, FESS can track short-term planned output of wind farm. It was demonstrated by quantitative analysis of simulation results that the proposed control strategy can smooth the active power fluctuation of wind farm effectively and thereby improve power quality of the power grid.

Application of Improved Fuzzy Controller in Networked Control System

Aiming at the problem of network-induced delay and data dropout in networked control system, an improved fuzzy controller is proposed in this paper. Considering the great influence of a controller on the performance of control system, an improved controller with a second order fuzzy controller and network-induced delay compensator being added to the basic fuzzy controller is proposed to realize self-regulation on-line. For this type of controller, neither plant model nor measurement of network delay is required. So it is capable of automatically adjusting quantified factor, pro- portional factor, and integral factor according to the control system error and its derivative. The design makes full use of the advantages of quickness in operation and reduction of steady state error because of its integral function. The con- troller has a good control effect on time-delay and can keep a better performance by self-regulation on-line in the net- work with data dropout and interference. It is good in quickness, adaptability, and robustness, which is favorable for controlling the long time-delay system.

Speed Control of Motor Based on Improved Glowworm Swarm Optimization

To better regulate the speed of brushless DC motors,an improved algorithm based on the original Glowworm Swarm Optimization is proposed.The proposed algorithm solves the problems of poor robustness,slow convergence,and low accuracy exhibited by traditional PID controllers.When selecting the glowworm neighborhood set,an optimization scheme based on the growth and competition behavior of weeds is applied to a single glowworm to prevent falling into a local optimal solution.After the glowworm’s position is updated,the league selection operator is introduced to search for the global optimal solution.Combining the local search ability of the invasive weed optimization with the global search ability of the league selection operator enhances the robustness of the algorithm and also accelerates the convergence speed of the algorithm.The mathematical model of the brushless DC motor is established,the PID parameters are tuned and optimized using improved Glowworm Swarm Optimization algorithm,and the speed of the brushless DC motor is adjusted.In a Simulink environment,a double closed-loop speed control model was established to simulate the speed control of a brushless DC motor,and this simulation was compared with a traditional PID control.The simulation results show that the model based on the improved Glowworm Swarm Optimization algorithm has good robustness and a steady-state response speed for motor speed control.

A Novel Tuning Method for Predictive Control of VAV Air Conditioning System Based on Machine Learning and Improved PSO

The variable air volume(VAV)air conditioning system is with strong coupling and large time delay,for which model predictive control(MPC)is normally used to pursue performance improvement.Aiming at the difficulty of the parameter selection of VAV MPC controller which is difficult to make the system have a desired response,a novel tuning method based on machine learning and improved particle swarm optimization(PSO)is proposed.In this method,the relationship between MPC controller parameters and time domain performance indices is established via machine learning.Then the PSO is used to optimize MPC controller parameters to get better performance in terms of time domain indices.In addition,the PSO algorithm is further modified under the principle of population attenuation and event triggering to tune parameters of MPC and reduce the computation time of tuning method.Finally,the effectiveness of the proposed method is validated via a hardware-in-the-loop VAV system.

How Can Macroeconomic Control Be Improved?

Ⅰ.New macroeconomic controls not fully realised China introduced a new set of macroeconomic controls in 2004 in an attempt to control excessive growth.Due to the impact of the Asian Financial Crisis, China’s economy grew at a low growth rate between 1998 and 2001.However it accelerated from 2002 onwards,and saw a GDP increase of 11%in the fourth quarler of 2003,a historical high since 1997

Direct Yaw Moment Control for Distributed Drive Electric Vehicle Handling Performance Improvement

For a distributed drive electric vehicle（DDEV） driven by four in-wheel motors, advanced vehicle dynamic control methods can be realized easily because motors can be controlled independently, quickly and precisely. And direct yaw-moment control（DYC） has been widely studied and applied to vehicle stability control. Good vehicle handling performance： quick yaw rate transient response, small overshoot, high steady yaw rate gain, etc, is required by drivers under normal conditions, which is less concerned, however. Based on the hierarchical control methodology, a novel control system using direct yaw moment control for improving handling performance of a distributed drive electric vehicle especially under normal driving conditions has been proposed. The upper-loop control system consists of two parts： a state feedback controller, which aims to realize the ideal transient response of yaw rate, with a vehicle sideslip angle observer; and a steering wheel angle feedforward controller designed to achieve a desired yaw rate steady gain. Under the restriction of the effect of poles and zeros in the closed-loop transfer function on the system response and the capacity of in-wheel motors, the integrated time and absolute error（ITAE） function is utilized as the cost function in the optimal control to calculate the ideal eigen frequency and damper coefficient of the system and obtain optimal feedback matrix and feedforward matrix. Simulations and experiments with a DDEV under multiple maneuvers are carried out and show the effectiveness of the proposed method： yaw rate rising time is reduced, steady yaw rate gain is increased, vehicle steering characteristic is close to neutral steer and drivers burdens are also reduced. The control system improves vehicle handling performance under normal conditions in both transient and steady response. State feedback control instead of model following control is introduced in the control system so that the sense of control intervention to drivers is relieved.

Suppression of Amplitude Decoherence in Arbitrary n-Level Atom in -Configuration with Bang-Bang Controls

In this paper, we give Bang-Bang （BB） decoupling schemes to suppress the amplitude decoherence in the five-and six-level atom systems in ≡-configuration. We generalize this scheme to the arbitrary level atom system in ≡-configuration. The corresponding decoupling operators are given explicitly.

Improved LMI representations for delay-independent and delay-dependent stability conditions

Some new linear matrix inequality (LMI) representations for delay-independent and delay-dependent stability conditions are obtained by introducing additional matrices and eliminating the product coupling of the system matrices and the Lya-punov matrices. The results improve conservativeness of the given conditions for the analysis and the design of tune-delay systems with polytopic-type uncertainty.

Efficiency Improvement of PEM Fuel Cells via Humidity Control

PEM （Proton Exchange Membrane） fuel cell is a promising renewable energy source to a wide range of applications for its clean products and high power density. However, controlling its humidity is a challenging problem due to the interdependence of several phenomena contributing in membrane＇s water content. This work deals with efficiency improvement of PEM fuel cells via humidity control. An innovative strategy of control based on the model of Ref. [1] is proposed. It consists on regulating gas humidification rates according to the power demand so that to minimize power losses. The proposed control takes into consideration constraints related to humidification in order to avoid dry out or flooding of the membrane. Simulations results show that time-phasing between hydrogen and oxygen humidification rates plays an important role in minimizing power losses. The proposed control shows significant improvement in the fuel cell＇s efficiency up to 20%.

Recent Progress in Plasma Control Studies on the Improvement of Plasma Performance in Heliotron J

Recent progress in plasma control studies on the improvement of plasma performance in Heliotron J is reviewed. The supersonic molecular beam injection （SMBI） fueling is successfully applied to Heliotron J plasma. A supersonic H2-beam is effectively injected to increase fueling efficiency and generate a peaked density profile. Local fueling with a short-pulsed SMBI can increase the core plasma density and avoid the degradation arising from edge cooling. Second harmonic electron cyclotron current drive （ECCD） experiments were conducted by launching a focused Gaussian beam with a parallel refractive index of -0.05 ≤ Nil 〈 0.6. Results show that the electron cyclotron （EC） driven current is determined not only by Nil but also by local magnetic field （B） structure where the EC power is deposited. Detailed analysis of the observed NI and B dependences is in progress with a ray-tracing simulation using the TRAVIS code. Fast ion velocity distribution was investigated using fast protons generated by ion cyclotron resonant frequency （ICRF） minority heating. For the standard configuration in Heliotron J, charge ex- change neutral particle analysis （CX-NPA） measurements show higher effective temperature of fast minority protons in the on-axis resonance case compared to that in the HFS （high field side） off-axis resonance case. However, the increase in bulk ion temperature in the HFS resonance case is larger than that in the on-axis resonance.

Synchronization motions of a two-link mechanism with an improved OPCL method

An improved OPCL method is developed and applied to both small swing and giant rotation synchronization of a two-link mechanism. Transition processes of the two kinds of synchronization are discussed. Comparisons of different motion characteristics of the two-link synchronization and the effects of different control parameters on synchronous processes are investigated with numerical simulations.

Improving Plasma Confinement by Controlling Hard X-Ray

Since runaway electrons and magnetohydrodynamics activity can contribute to serious damage and energy losses in tokamaks, the effect of an external electric field on runaway electrons and hard x-ray spectra is investigated. Parameters such as the plasma current, the hard x-ray photons count and the mean energy of runaway electrons are measured. Positive and negative voltages of 300 V are applied at lOms after the plasma initiation （while the plasma is forming）, at 15ms （while the plasma is stable） and at 20ms （while the plasma is fading away） to attain the most effective time of applying the external electric field. The number of hard x-ray photons has the most changes in the range of O-200 keV when the external electric fields are applied. Also in the duration of 20-30ms of plasma the greatest number of hard x-ray spectra is detected. When the external electric fields are applied, the mean energy of runaway electrons reduces significantly, especially at 15 ms （while the plasma is stab/e）.

Yield Improvement and Advanced Defect Control——Driving Forces for Modeling of Bulk Crystal Growth

Yield improvement and advanced defect control can be identified as the driving forces for modeling of industrial bulk crystal growth. Yield improvement is mainly achieved by upscaling of the whole crystal growth apparatus and increased processing windows with more tolerances for parameter variations. Advanced defect control means on one hand a reduction of the number of deficient crystal defects and on the other hand the formation of beneficial crystal defects with a uniform distribution and well defined concentrations in the whole crystal. This "defect engineering" relates to the whole crystal growth process as well as the following cooling and optional annealing processes, respectively. These topics were illustrated in the paper by examples of modeling and experimental results of bulk growth of silicon (Si), gallium arsenide (GaAs), indium phosphide (InP) and calcium fluoride (CaF2). These examples also involve the state of the art of modeling of the most important melt growth techniques, crystal pulling (Czochralski methods) and vertical gradient freeze (Bridgman-type methods).