Small-signal modelling and stability analysis of grid-following and grid-forming inverters dominated power system

In this paper,the explicit state-space model for a multi-inverter system including grid-following inverter-based generators(IBGs)and grid-forming IBGs is developed by the two-level component connection method(CCM),which modularized inverter control blocks at the primary level and IBGs at the secondary level.Based on the comprehensive state-space model representing full order of system dynamics,eigenvalues of the overall system are thoroughly analyzed,identifying potential adverse impacts of not only grid-following inverters,but also grid forming inverters on the system small-signal stability,with the underlying principle of oscillations also understood.Numerical and simulation results validate effectiveness of the proposed methodology on IEEE benchmarking 39-bus system.

Spectral Analysis and Validation of Parietal Signals for Different Arm Movements

Brain signal analysis plays a significant role in attaining data related to motor activities.The parietal region of the brain plays a vital role in muscular movements.This approach aims to demonstrate a unique technique to identify an ideal region of the human brain that generates signals responsible for muscular movements;perform statistical analysis to provide an absolute characterization of the signal and validate the obtained results using a prototype arm.This can enhance the practical implementation of these frequency extractions for future neuro-prosthetic applications and the characterization of neurological diseases like Parkinson’s disease(PD).To play out this handling method,electroencepha-logram(EEG)signals are gained while the subject is performing different wrist and elbow movements.Then,the frontal brain signals and just the parietal signals are separated from the obtained EEG signal by utilizing a band pass filter.Then,feature extraction is carried out using Fast Fourier Transform(FFT).Subse-quently,the extraction process is done by Daubechies(db4)and Haar wavelet(db1)in MATLAB and classified using the Levenberg-Marquardt Algorithm.The results of the frequency changes that occurred during various wrist move-ments in the parietal region are compared with the frequency changes that occurred in frontal EEG signals.This proposed algorithm also uses the deep learn-ing pattern analysis network to evaluate the matching sequence for each action that takes place.Maximum accuracy of 97.2%and maximum error range of 0.6684%are achieved during the analysis.Results of this research confirm that the Levenberg-Marquardt algorithm,along with the newly developed deep learn-ing hybrid PatternNet,provides a more accurate range of frequency changes than any other classifier used in previous works of literature.Based on the analysis,the peak-to-peak value is used to define the threshold for the prototype arm,which performs all the intended degrees of freedom(DOF),verifying the results.These results would aid the specialists in their decision-making by facilitating the ana-lysis and interpretation of brain signals in the field of neuroscience,specifically in tremor analysis in PD.

Analysis and control of a class of uncertain linear periodic discrete-time systems

Feedback control problems for linear periodic systems （LPSs） with interval- type parameter uncertainties are studied in the discrete-time domain. First, the stability analysis and stabilization problems are addressed. Conditions based on the linear matrices inequality （LMI） for the asymptotical stability and state feedback stabilization, respec-tively, are given. Problems of L2-gain analysis and control synthesis are studied. For the L2-gain analysis problem, we obtain an LMI-based condition such that the autonomous uncertain LPS is asymptotically stable and has an L2-gain smaller than a positive scalar γ. For the control synthesis problem, we derive an LMI-based condition to build a state feedback controller ensuring that the closed-loop system is asymptotically stable and has an L2-gain smaller than the positive scalar γ. All the conditions are necessary and sufficient.

Role of Interfacial Viscosity and pH in L-Phenylalanine,L-Tryptophan Molecular Rotors

Protein folding involves the aminoacid sequence to come forth and form an energy minimized structure.Recently molecular crowding leading to increase in viscosity is said to be one of the major concerns affecting protein folding.Many external fluorescent probes are used to detect such increases in viscosity.Since most of the protein sequences contain L-Phe and L-Trp,in this study we have used these aminoacids as probes to detect changes in viscosity.This study will help to advance the knowledge on molecular crowding effects in protein folding.

Surface properties of Al-doped ZnO thin film before and after CF_4/Ar plasma etching

Al-doped ZnO(AZO) is considered as an alternative to transparent conductive oxide materials.Patterning and achieving a stable surface are important challenges in the development and optimization of dry etching processes, which must be overcome for the application of AZO in various devices. Therefore, in this study, the etch rate and surface properties of an AZO thin film after plasma etching using the adaptive coupled plasma system were investigated. The fastest etch rate was achieved with a CF_(4)/Ar ratio of 50:50 sccm. Regardless of the ratio of CF_(4) to Ar,the transmittance of the film in the visible region exceeded 80%. X-ray photoelectron spectroscopy analysis of the AZO thin film confirmed that metal-F bonding persists on the surface after plasma etching. It was also shown that F eliminates O vacancies. Consequently, the work function and bandgap energy increased as the ratio of CF-4 increased. This study not only provides information on the effect of plasma on AZO thin film, but identifies the cause of changes in the device characteristics during device fabrication.

Effect of Dust and Shadow on Performance of Solar Photovoltaic Modules: Experimental Analysis

This study presents an experimental performance of a solar photovoltaic module under clean,dust,and shadow conditions.It is found that there is a significant decrease in electrical power produced(40%in the case of dust panels and 80%in the case of shadow panels)and a decrease in efficiency of around 6%in the case with dust and 9%in the case with the shadow,as compared to the clean panel.From the results,it is clear that there is a substantial effect of a partial shadow than dust on the performance of the solar panel.This is due to the more obstruction of the sunlight by the shadowed area compared to the dust.The dust being finer particles for the given local experimental condition did not influence the panel than the shadow.The main outcome of this study is that the shadowing effect may cause more harm to the PV module than dust for the given experimental conditions.However,Further long-term studies on the effect of dust and shadow are needed to understand the effect on performance degradation and module life.

Study on the interaction mechanism of double-blade corona discharge with a large discharge gap

Multi-source corona discharge is a commonly used method to generate more charged particles,but the interaction mechanism between multiple discharge sources,which largely determines the overall discharge effect,has still not been studied much.In this work,a large-space hybrid model based on a hydrodynamic model and ion-transport model is adopted to study the interaction mechanism between discharge sources.Specifically,the effects of the number of electrodes,voltage level,and electrode spacing on the discharge characteristics are studied by taking a double-blade electrode as an example.The calculation results show that,when multiple discharge electrodes operate simultaneously,the superimposed electric field includes multiple components from the electrodes,making the ion distribution and current different from that under a single-blade electrode.The larger the distance between discharge electrodes,the weaker the interaction.When the electrode spacing d is larger than 4 cm,the interaction can be ignored.The results can guide the design of large discharge gap array electrodes to achieve efficient discharge.

Multi-point discharge model: study on corona discharge of double-ended needle in large space

Corona discharge, as a common means to obtain non-equilibrium plasma, can generally obtain high-concentration plasma by increasing discharge points to meet production needs. However,the existing numerical simulation models used to study multi-point corona discharge are all calculations of small-scale space models, which cannot obtain the distribution characteristics of plasma in large space. Based on our previous research, this paper proposes a hybrid model for studying the distribution of multi-point discharge plasma in large-scale spaces, which divides the computational domain and computes separately with the hydrodynamic model and the ion mobility model. The simulation results are verified by a needle–ball electrode device. Firstly, the electric field distribution and plasma distribution of the needle electrodes with single tip and double tips are compared and discussed. Secondly, the plasma distribution of the needle electrode with the double tip at different voltages is investigated. Both computational and experimental results indicate that the charged particle concentration and current of the needle electrode with double tips are both twice as high as those of the needle electrode with a single tip. This model can extend the computational area of the multi-point corona discharge finite element model to the sub-meter(25 cm) or meter level, which provides an effective means to study the plasma distribution generated by multiple discharge points in large-scale space.

Development of Energy Management System for Micro Grid Operation

The introduction of several small and large-scale industries,malls,shopping complexes,and domestic applications has significantly increased energy consumption.The aim of the work is to simulate a technically viable and economically optimum hybrid power system for residential buildings.The proposed micro-grid model includes four power generators:solar power,wind power,Electricity Board(EB)source,and a Diesel Generator(DG)set,with solar and wind power performing as major sources and the EB supply and DG set serving as backup sources.The core issue in direct current to alternate current conversion is harmonics distortion,a five-stage multilevel inverter is employed with the assistance of an intelligent control system is simulated and the optimum system configuration is estimated to reduce harmonics and improve the power quality.The monthly demand for residential buildings is 13-15 Megawatts.So,almost 433 Kilo-Watts(KW)of electricity is required every day,and if it is used for 8 h per day,50-60 KW of electricity is needed per hour.The overall micro-grid model’s operation and performance are established using MATLAB/SIMULINK software,and simulation results are provided.The simulation results show that the developed system is both cost-effective and environment friendly resulting in yearly cost reductions.

Investigation of Tablet Defects by AI Based Terahertz Technology

Recently a lot of medical tablets with special packets in the global market are available. For the safety and purity of the tablet, we need to scan it by developed scanner technology, which should be not more expensive and easily available in the market. The THz technology is one of them. In the proposed work, we have tasted tablet images with the help of the THz super-resolution scanner, which is already available in our lab. The AI machine learning data concept has been investigated. Good resolution of images has been obtained. Furthermore, the challenging research problems are discussed. Finally, it summarizes the recent updates in terahertz technology for drug inspection and medical applications with potential research challenges.

Bionics and Structural Biology:A Novel Approach for Bio-energy Production

Cellular metabolism is a very complex process. The biochemical pathways are fundamental structures of biology. These pathways possess a number of regeneration steps which facilitate energy shuttling on a massive scale. This facilitates the biochemical pathways to sustain the energy currency of the cells. This concept has been mimicked using electronic circuit components and it has been used to increase the efficiency of bio-energy generation. Six of the carbohydrate biochemical pathways have been chosen in which glycolysis is the principle pathway. All the six pathways are interrelated and coordinated in a complex manner. Mimic circuits have been designed for all the six biochemical pathways. The components of the metabolic pathways such as enzymes, cofactors etc., are substituted by appropriate electronic circuit components. Enzymes are related to the gain of transistors by the bond dissociation energies of enzyme-substrate molecules under consideration. Cofactors and coenzymes are represented by switches and capacitors respectively. Resistors are used for proper orientation of the circuits. The energy obtained from the current methods employed for the decomposition of organic matter is used to trigger the mimic circuits. A similar energy shuttle is observed in the mimic circuits and the percentage rise for each cycle of circuit functioning is found to be 78.90. The theoretical calculations have been made using a sample of domestic waste weighing 1.182 kg. The calculations arrived at finally speak of the efficiency of the novel methodology employed.

Mean Square Containment Control of Multi-agent Systems with Transmission Noises

为有传播噪音的多代理人系统的抑制控制问题被考虑。由于噪音的存在，腐烂的获得功能被介绍稀释噪音。动态地交换拓扑学并且随机交换拓扑学被考虑。通讯图和控制获得功能上的足够的条件被导出保证吝啬的方形的抑制。一些数字例子被提供验证结果。

Component Content Soft-Sensor Based on Hybrid Models in Countercurrent Rare Earth Extraction Process

In consideration of the online measurement of the component content in rare earth countercurrent extraction separation process, the soft sensor method based on hybrid modeling was proposed to measure the rare earth component content. The hybrid models were composed of the extraction equilibrium calculation model and the Radial Basis Function (RBF) Neural Network (NN) error compensation model; the parameters of compensation model were optimized by the hierarchical genetic algorithms (HGA). In addition, application experiment research of this proposed method was carried out in the rare earth separation production process of a corporation. The result shows that this method is effective and can realize online measurement for the component content of rare earth in the countercurrent extraction.

Robust H_∞ observer-based control for synchronization of a class of complex dynamical networks

This paper is concerned with the robust Hoo synchronization problem for a class of complex dynamical networks by applying the observer-based control. The proposed feedback control scheme is developed to ensure the asymptotic stability of the augmented system, to reconstruct the non-measurable state variables of each node and to improve the H∞ performance related to the synchronization error and observation error despite the external disturbance. Based on the Lyapunov stability theory, a synchronization criterion is obtained under which the controlled network can be robustly stabilized onto a desired state with a guaranteed H∞ performance. The controller and the observer gains can be given by the feasible solutions of a set of linear matrix inequalities （LMIs）. The effectiveness of the proposed control scheme is demonstrated by a numerical example through simulation.

Linearized Controller Design for the Output Probability Density Functions of Non-Gaussian Stochastic Systems

This paper presents a linearized approach for the controller design of the shape of output probability density functions for general stochastic systems. A square root approximation to an output probability density function is realized by a set of B-spline functions. This generally produces a nonlinear state space model for the weights of the B-spline approximation. A linearized model is therefore obtained and embedded into a performance function that measures the tracking error of the output probability density function with respect to a given distribution. By using this performance function as a Lyapunov function for the closed loop system, a feedback control input has been obtained which guarantees closed loop stability and realizes perfect tracking. The algorithm described in this paper has been tested on a simulated example and desired results have been achieved.

The Structural Stability of Alpha-Helix Determined by the Preference of Amino Acids

To accomplish their functions, proteins have to achieve different conformations accompanied by conformational transitions. However, the relationship between the preference of amino acids and the stability of the secondary structure is still unclear. Here we perform molecular simulations on a series of helical structures. Our data show that the dissociation energy of the helical structure is related to the preference of amino acids, and the electrostatic repulsion of the residue i and i ＋ 3/4 with the same sign of charge destabilizes the alpha helix.

Diagnosis of Dental Caries Using Terahertz Technology

Recently,the diagnoses of dental caries and other dental issues are in a queue as only X-ray-based techniques are available in most hospitals around the world.Terahertz(THz)parametric imaging(TPI)is the latest technology that can be applied for medical applications,especially dental caries.This technology is harmless and thus suitable for biological samples owing to the low energy of THz emission.In this paper,a developed TPI system is used to investigate the two-dimensional(2 D)and three-dimensional(3D)images of different samples from human teeth.After analyzing the measured images of human teeth,the results suggest that the THz parametric technology is capable of investigating the inner side structure of the teeth.This technique can be useful in detecting the defects in all types of human and animal teeth.The measurement and analytical calculations have been performed by using the TPI system and MATLAB,respectively,and both are in good agreement.The characteristics of THz waves and their interactions with the tooth samples are summarized.And the available THz-based technologies,such as TPI,and their potential applications of diagnoses are also presented.

Swarm-LSTM: Condition Monitoring of Gearbox Fault Diagnosis Based on Hybrid LSTM Deep Neural Network Optimized by Swarm Intelligence Algorithms

Nowadays,renewable energy has been emerging as the major source of energy and is driven by its aggressive expansion and falling costs.Most of the renewable energy sources involve turbines and their operation and maintenance are vital and a difficult task.Condition monitoring and fault diagnosis have seen remarkable and revolutionary up-gradation in approaches,practices and technology during the last decade.Turbines mostly do use a rotating type of machinery and analysis of those signals has been challenging to localize the defect.This paper proposes a new hybrid model wherein multiple swarm intelligence models have been evaluated to optimize the conventional Long Short-Term Memory(LSTM)model in classifying the faults from the vibration signals data acquired from the gearbox.This helps to analyze the performance and behavioral patterns of the system more effectively and efficiently which helps to suggest for replacement of the unit with higher precision.The results have demonstrated that the proposed hybrid modeling approach is effective in classifying the faults of the gearbox from the time series data and achieve higher diagnostic accuracy in comparison to the conventional LSTM methods.