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.

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%.

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.

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.

New Islanding Detection Method with Better Performance in Presence of Non-resistive Load

Islanding detection is a mandatory component in grid-connected photovoltaic （PV） inverters. It is also a key issue in the photovoltaic agriculture. In this work, an overview on the islanding effect in grid-connected PV power systems was provided. Various islanding detection methods were introduced and their strength and weakness were dicussed. An improved islanding detection method was proposed based on active frequency drift （AFD）. The new method tolerated capacitive and inductive loads, because its perturbation signal was not offset by the non-resistive load. The new method through simulation in MATLAB/Simulink was evaluated and the advantages of the new method were demonstrated.

Model Construction and Visualization Simulation of Soybean Root System

This paper mainly focused on the growth law, model construction and visualization of a soybean root. A pot experiment was conducted in the laboratory to collect root system data and measure soybean root＇s length, diameter and number by excavating in different periods. On the basis of an in-depth analysis of root structure geometry, we analyzed the collected experimental data with logistic equations, and got the growth of soybean root equation, according to its morphological structure characteristics of self-similarity, and discussed the virtual modeling method on the soybean root based on L-system and in Visual c＋＋ Using OpenGL technology to achieve a soybean root system topology model and visualization simulation.

Design of Observer-based Adaptive Controller for Nonlinear Systems with Unmodeled Dynamics and Actuator Dead-zone

This paper presents an up-to-date study on the observer-based control problem for nonlinear systems in the presence of unmodeled dynamics and actuator dead-zone.By introducing a dynamic signal to dominate the unmodeled dynamics and using an adaptive nonlinear damping to counter the effects of the nonlinearities and dead-zone input,the proposed observer and controller can ensure that the closed-loop system is asymptotically stable in the sense of uniform ultimate boundedness.Only one adaptive parameter is needed no matter how many unknown parameters there are.The system investigated is more general and there is no need to solve Linear matrix inequality （LMI）.Moreover,with our method,some assumptions imposed on nonlinear terms and dead-zone input are relaxed.Finally,simulations illustrate the effectiveness of the proposed adaptive control scheme.

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.

Research and Application of Distributed Data Mining Method for Improving Rural Power Grid Enterprises in Production and Operation Status Evaluation

With the reform of rural network enterprise system,the speed of transfer property rights in rural power enterprises is accelerated.The evaluation of the operation and development status of rural power enterprises is directly related to the future development and investment direction of rural power enterprises.At present,the evaluation of the production and operation of rural network enterprises and the development status of power network only relies on the experience of the evaluation personnel,sets the reference index,and forms the evaluation results through artificial scoring.Due to the strong subjective consciousness of the evaluation results,the practical guiding significance is weak.Therefore,distributed data mining method in rural power enterprises status evaluation was proposed which had been applied in many fields,such as food science,economy or chemical industry.The distributed mathematical model was established by using principal component analysis(PCA)and regression analysis.By screening various technical indicators and determining their relevance,the reference value of evaluation results was improved.Combined with statistical program for social sciences(SPSS)data analysis software,the operation status of rural network enterprises was evaluated,and the rationality,effectiveness and economy of the evaluation was verified through comparison with current evaluation results and calculation examples of actual grid operation data.

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.