Analysis of Electricity Consumption Pattern Clustering and Electricity Consumption Behavior

Studying user electricity consumption behavior is crucial for understanding their power usage patterns.However,the traditional clustering methods fail to identify emerging types of electricity consumption behavior.To address this issue,this paper introduces a statistical analysis of clusters and evaluates the set of indicators for power usage patterns.The fuzzy C-means clustering algorithm is then used to analyze 6 months of electricity consumption data in 2017 from energy storage equipment,agricultural drainage irrigation,port shore power,and electric vehicles.Finally,the proposed method is validated through experiments,where the Davies-Bouldin index and profile coefficient are calculated and compared.Experiments showed that the optimal number of clusters is 4.This study demonstrates the potential of using a fuzzy C-means clustering algorithmin identifying emerging types of electricity consumption behavior,which can help power system operators and policymakers to make informed decisions and improve energy efficiency.

Non-linear performance analysis and voltage control of MFC based on feedforward fuzzy logic PID strategy

Microbial fuel cell(MFC)is a kind of promising clean power supply energy equipment,but serious nonlinearities and disturbances exist when the MFC runs,and it is an important topic to guarantee that the output voltage reaches the setting value quickly and smoothly.Regulating the feeding flow is an effective way to achieve this goal,and especially,the satisfactory results can be achieved by regulating anode feeding flow.In this work,a feedforward fuzzy logic PID algorithm is proposed.The fuzzy logic system is introduced to deal with the non-linear dynamics of MFC,and corresponding PID parameters are calculated according to defuzzification.The magnitude value of the current density is used to simulate the value of the external load.The simulation results indicate that the MFC output voltage can track the setting value quickly and smoothly with the proposed feedforward fuzzy logic PID algorithm.The proposed algorithm is more efficient and robust with respect to anti-disturbance performance and tracking accuracy than other three control methods.

Face anti-spoofing algorithm combined with CNN and brightness equalization

Face anti-spoofing is a relatively important part of the face recognition system,which has great significance for financial payment and access control systems.Aiming at the problems of unstable face alignment,complex lighting,and complex structure of face anti-spoofing detection network,a novel method is presented using a combination of convolutional neural network and brightness equalization.Firstly,multi-task convolutional neural network(MTCNN)based on the cascade of three convolutional neural networks(CNNs),P-net,R-net,and O-net are used to achieve accurate positioning of the face,and the detected face bounding box is cropped by a specified multiple,then brightness equalization is adopted to perform brightness compensation on different brightness areas of the face image.Finally,data features are extracted and classification is given by utilizing a 12-layer convolution neural network.Experiments of the proposed algorithm were carried out on CASIA-FASD.The results show that the classification accuracy is relatively high,and the half total error rate(HTER)reaches 1.02%.

Application of fuzzy analytic hierarchy process and neural network in power transformer risk assessment

In operation,risk arising from power transformer faults is of much uncertainty and complicacy.To timely and objectively control the risks,a transformer risk assessment method based on fuzzy analytic hierarchy process(FAHP) and artificial neural network(ANN) from the perspective of accuracy and quickness is proposed.An analytic hierarchy process model for the transformer risk assessment is built by analysis of the risk factors affecting the transformer risk level and the weight relation of each risk factor in transformer risk calculation is analyzed by application of fuzzy consistency judgment matrix;with utilization of adaptive ability and nonlinear mapping ability of the ANN,the risk factors with large weights are used as input of neutral network,and thus intelligent quantitative assessment of transformer risk is realized.The simulation result shows that the proposed method increases the speed and accuracy of the risk assessment and can provide feasible decision basis for the transformer risk management and maintenance decisions.

The Use of LMS and RLS Adaptive Algorithms for an Adaptive Control Method of Active Power Filter

This paper deals with the adaptive control mechanism management meant for shunt active power filters (SAPF). Systems driven this way are designed to improve the quality of electric power (power quality) in industrial networks. The authors have focused on the implementation of two basic representatives of adaptive algorithms, first, the algorithm with a stochastic LMS (least mean square) gradient adaptation and then an algorithm with recursive RLS (recursive least square) optimal adaptation. The system examined by the authors can be used for non-linear loads for appliances with rapid fluctuations of the reactive and active power consumption. The proposed system adaptively reduces distortion, falls (dip) and changes in a supply voltage (flicker). Real signals for measurement were obtained at a sophisticated, three-phase experimental workplace. The results of executed experiments indicate that, with use of the certain adaptive algorithms, the examined AHC system shows very good dynamics, resulting in a much faster transition during the AHC connection-disconnection or during a change in harmonic load on the network. The actual experiments are evaluated from several points of view, mainly according to a time convergence (convergence time) and mistakes in a stable state error (steady state error) of the investigated adaptive algorithms and finally as a total harmonic distortion (THD). The article presents a comparison of the most frequently used adaptive algorithms.

Simulation of grain boundary effect on characteristics of ZnO thin film transistor by considering the location and orientation of grain boundary

The grain boundaries （GBs） have a strong effect on the electric properties of ZnO thin film transistors （TFTs）. A novel grain boundary model was developed to analyse the effect. The model was characterized with different angles between the orientation of the grain boundary and the channel direction. The potential barriers formed by the grain boundaries increase with the increase of the grain boundary angle, so the degradation of the transistor characteristics increases. When a grain boundary is close to the drain edge, the potential barrier height reduces, so the electric properties were improved.

PSO-based Parameter Tuning for a TwoDegree-of-Freedom IMC Scheme and Its Application to Paper Basis Weight Control

Basis weight is an important indicator for evaluating paper quality and a major factor directly affecting the economic benefits of enterprises.Focusing on the large time-delay,time-varying,and nonlinear characteristics of a basis weight control system,a two-degree-of-freedom(TDF)internal model control(IMC)method based on a particle swarm optimization(PSO)algorithm was proposed.The method took the integral of time multiplied by the absolute error(ITAE)as the objective function,and the PSO algorithm was used to optimize the time constant of the tuning IMC filter.The simulation results for the control system under the proposed TDF-IMC method based on the PSO algorithm demonstrate good set-point tracking performance,strong anti-interference capabilities,and good robustness properties.The application results revealed that the basis weight fluctuation range of the paper was±2 g/m2,which significantly improved both the control quality and the product quality.

Study on phase retardation characteristic of LCVR using dispersion analysis and SVM

To calibrate the phase retardance of a Liquid crystal variable retarder(LCVR),its birefringence dispersion characteristic was analyzed,and the Support vector machines(SVM) algorithm was adopted to establish the prediction model.The obtained SVM decision function was used as a part of LCVR phase retardance,which was generated by the driving voltage.The experimental verification was carried out with a 568 nm laser.The results show that the deviation of the experimental value and the theoretical value is about 0.0061λ.SVM method could be used as an effective method for LCVR phase retardance characteristic calibration.

Magnetic Field and Operating Performance Analysisof Conical-rotor Permanent Magnet Synchronous Motor

In order to accurately analyze the magnetic field of conical-rotor permanent magnet synchronous motor(CR-PMSM),the effectiveness of two methods was studied on handling of problems concerned with non-uniform distribution of magnetic field along axial direction in CR-PMSM,which were sectional calculation(SC)method and three-dimensional finite element(3-D FE)method.On this basis,the influence of the axial displacement and dq-axis currents on the operating characteristics of axial magnetic force and torque is analyzed by using the 3-D FE model.Analysis results show that the axial magnetic force and torque decrease with the increase of axial displacement of the rotor,and the amplitude regularity of the axial magnetic force is affected by the d-axis current.A prototype machine is fabricated and tested,in order to validate the design theory.

Analysis of Magnetic Field and Torque for All-Harmonic-Torque Motor with Surface-Mounted Permanent Magnets

With the development of the advanced industrial products and technologies,the requirements for permanent magnet synchronous motors(PMSM)with high torque density are increasing.This paper proposed a new type of PMSM,named All-Harmonic-Torque PMSM(AHT-PMSM),which could use all harmonics of the magneto motive force(MMF)generated by permanent magnets and armature currents to enhance the electromagnetic torque.The stator windings structure,drive converter configurations and control model of the AHT-PMSM are illustrated firstly.And then the harmonic characteristics of the MMF generated by permanent magnets and armature currents are analyzed.It is found that the MMFs of permanent magnets and armature currents include the same order harmonics and each order harmonic is orthonormal for each other.Finally the calculation process of the average electromagnetic torque for AHT-PMSM based on harmonic MMFs is deduced.It is demonstrated that the theoretical average electromagnetic torque of AHT-PMSM is improved by 23%comparing to that of the normal sinusoidal PMSM when they are with the same structure sizes and armature MMFs.A 24-slot/4-pole AHT-PMSM and sinusoidal PMSM prototypes with the same sizes are modeled and manufactured,the comparison conclusion is validated by the finite element analyses(FEA)and experimental results.

Tunable Fano resonances and plasmonic hybridization of gold triangle–rod dimer nanostructure

A gold dimer structure consisting of a notched triangle nanoslice and a rectangle nanorod is proposed to produce distinct Fano resonance. Owing to the coupling between the dipole plasmon mode of the nanorod and the dipole or quadrupole plasmon mode of the nanoslice, the extinction spectrum with a deep Fano dip is formed and can be well fitted by the Fano interference model for different geometry parameters. In addition, Fano resonance of the gold dimer nanostructure also intensely depends on the polarization direction of incident light. Moreover, Fano resonance of the triangle–rod trimer is also analyzed by adding another nanorod into the former dimer and exhibits the splitting of plasmonic resonant peak in high order coupling modes. The plasmonic hybridizations in these nanostructures have been analyzed for revealing the physical origin of the Fano resonance.

Robust fuzzy control of Takagi-Sugeno fuzzy neural networks with discontinuous activation functions and time delays

The problem of global robust asymptotical stability for a class of Takagi-Sugeno fuzzy neural networks（TSFNN） with discontinuous activation functions and time delays is investigated by using Lyapunov stability theory.Based on linear matrix inequalities（LMIs）,we originally propose robust fuzzy control to guarantee the global robust asymptotical stability of TSFNNs.Compared with the existing literature,this paper removes the assumptions on the neuron activations such as Lipschitz conditions,bounded,monotonic increasing property or the right-limit value is bigger than the left one at the discontinuous point.Thus,the results are more general and wider.Finally,two numerical examples are given to show the effectiveness of the proposed stability results.

Undetermination of the relation between network synchronizability and betweenness centrality

Betweenness centrality is taken as a sensible indicator of the synchronizability of complex networks. To test whether betweenness centrality is a proper measure of the synchronizability in specific realizations of random networks, this paper adds edges to the networks and then evaluates the changes of betweenness centrality and network synchronizability. It finds that the two quantities vary independently.

Enhanced and tunable Imbert–Fedorov shift based on epsilon-near-zero response of Weyl semimetal

We theoretically investigate the reflected spatial Imbert–Fedorov(IF)shift of transverse-electric(TE)-polarized beam illuminating on a bulk Weyl semimetal(WSM).The spatial IF shift is enhanced significantly at two different frequencies close to the epsilon-near-zero(ENZ)frequency,where large values of reflection coefficients|r_(pp)|/|r_(ss)|are obtained due to the ENZ response induced different rapid increasing trends of|r_(pp)|and|r_(ss)|.Particularly,the tunable ENZ effect with tilt degree of Weyl cones and Fermi energy enables the enhanced spatial IF shift at different frequencies.The enhanced spatial IF shift also shows the adjustability of WSM thickness,incident angle and Weyl node separation.Our findings provide easy and available methods to enlarge and adjust the reflected IF shift of TE-polarized light with a WSM.

Multilevel Characteristic Basis Function Method with ACA for Accelerated Solution of Electrically Large Scattering Problems

The multilevel characteristic basis function method(MLCBFM)with the adaptive cross approximation(ACA)algorithm for accelerated solution of electrically large scattering problems is studied in this paper.In the conventional MLCBFM based on Foldy-Lax multiple scattering equations,the improvement is only made in the generation of characteristic basis functions(CBFs).However,it does not provide a change in impedance matrix filling and reducing matrix calculation procedure,which is time-consuming.In reality,all the impedance and reduced matrix of each level of the MLCBFM have low-rank property and can be calculated efficiently.Therefore,ACA is used for the efficient generation of two-level CBFs and the fast calculation of reduced matrix in this study.Numerical results are given to demonstrate the accuracy and efficiency of the method.

Order parameters and synchronization of FitzHugh-Nagumo small-world networks

This paper numerically investigates the order parameter and synchronisation in the small world connected FitzHugh-Nagumo excitable systems. The simulations show that the order parameter continuously decreases with increasing D, the quality of the synchronisation worsens for large noise intensity. As the coupling intensity goes up, the quality of the synchronisation worsens, and it finds that the larger rewiring probability becomes the larger order parameter. It obtains the complete phase diagram for a wide range of values of noise intensity D and control parameter g.

Impact of low-dose radiation on nitrided lateral 4H-SiC MOSFETs and the related mechanisms

Lateral type n-channel 4H-SiC metal–oxide–semiconductor field effect transistors(MOSFETs),fabricated using a current industrial process,are irradiated with gamma rays at different irradiation doses in this paper to carry out a profound study on the generation mechanism of radiation-induced interface traps and oxide trapped charges.Electrical parameters(e.g.,threshold voltage,subthreshold swing and channel mobility)of the device before and after irradiation are investigated,and the influence of the channel orientation([1100]and[1120])on the radiation effect is discussed for the first time.A positive threshold voltage shift is observed at very low irradiation doses(<100 krad(Si));the threshold voltage then shifts negatively as the dose increases.It is found that the dependence of interface trap generation on the radiation dose is not the same for doses below and above 100 krad.For irradiation doses<100 krad,the radiation-induced interface traps with relatively high generation speeds dominate the competition with radiation-induced oxide trapped charges,contributing to the positive threshold voltage shift correspondingly.All these results provide additional insight into the radiation-induced charge trapping mechanism in the SiO_(2)/SiC interface.

Extinction and Stationary Distribution of a Stochastic SIR Epidemic Model with Jumps

A stochastic susceptible-infective-recovered(SIR)epidemic model with jumps was considered.The contributions of this paper are as follows.(1) The stochastic differential equation(SDE)associated with the model has a unique global positive solution;(2) the results reveal that the solution of this epidemic model will be stochastically ultimately bounded,and the non-linear SDE admits a unique stationary distribution under certain parametric conditions;(3) the coefficients play an important role in the extinction of the diseases.

Multi-objective optimization of process parameters for ultra-narrow gap welding based on Universal Kriging and NSGA Ⅱ

The successful confinement of the arc by the flux band depends on the welding process parameters for achieving single-pass,multi-layer, and ultra-narrow gap welding. The sidewall fusion depth, the width of the heat-affected zone, and the line energy are utilized as comprehensive indications of the quality of the welded joint. In order to achieve well fusion and reduce the heat input to the base metal.Three welding process characteristics were chosen as the primary determinants, including welding voltage, welding speed, and wire feeding speed. The metamodel of the welding quality index was built by the orthogonal experiments. The metamodel and NSGA-Ⅱ(Non-dominated sorting genetic algorithm Ⅱ) were combined to develop a multi-objective optimization model of ultra-narrow gap welding process parameters. The results showed that the optimized welding process parameters can increase the sidewall fusion depth, reduce the width of the heataffected zone and the line energy, and to some extent improve the overall quality of the ultra-narrow gap welding process.