A Tutorial Review of the Solar Power Curve: Regressions, Model Chains, and Their Hybridization and Probabilistic Extensions

Owing to the persisting hype in pushing toward global carbon neutrality,the study scope of atmospheric science is rapidly expanding.Among numerous trending topics,energy meteorology has been attracting the most attention hitherto.One essential skill of solar energy meteorologists is solar power curve modeling,which seeks to map irradiance and auxiliary weather variables to solar power,by statistical and/or physical means.In this regard,this tutorial review aims to deliver a complete overview of those fundamental scientific and engineering principles pertaining to the solar power curve.Solar power curves can be modeled in two primary ways,one of regression and the other of model chain.Both classes of modeling approaches,alongside their hybridization and probabilistic extensions,which allow accuracy improvement and uncertainty quantification,are scrutinized and contrasted thoroughly in this review.

Prediction models of the ionization coefficient and ionization cross-section based on multi-layer molecular parameters

Prediction models were proposed to estimate the reduced Townsend ionization coefficient and ionization cross-section.A shape function of the reduced Townsend ionization coefficient curves was derived from the ionization collision probability model.The function had three parameters:the first ionization potential energy,A_(α),and B_(α).A_(α)and B_(α)were related to the molecule symmetry and size.The polarization of molecules could characterize the molecule symmetry.The multi-layer molecular cross-section(MMCS)was proposed to describe the contributions of electrons and molecule radius on different molecule surfaces to collisions.A prediction model of the ionization cross-section was also proposed based on Aα.The molecule parameters were calculated by the Becke3–Lee–Yang–Parr(B3LYP)method and the 6–311G**basis set.We used available data of 30 and 23 gases,respectively,to build the prediction models of reduced Townsend ionization coefficients and ionization cross-sections.The relationships between the molecular parameters Aαand Bαand the ionization cross-section were built up via nonlinear fittings.The determination coefficients R^(2)of Aα,Bα,and the ionization cross-section were 0.877,0.887,and 0.838,respectively.The results showed that the accuracy of models was positively correlated with the molecule symmetry and reduced electric field.This was mainly related to the accuracy of the MMCS model in predicting Aα.The MMCS model needed to be improved to describe the collision direction selectivity caused by the molecule asymmetry.Under a high reduced electric field,that error of Aαhad less influence on the prediction results.However,the prediction results for single atoms with high symmetry were poor.This may be due to the absolute error of the model close to single atoms’reduced Townsend ionization coefficients.The models could provide the basis for gas insulation prediction and discharge calculations,especially for symmetric molecules under a high electric field.

Impact of the Foucault Current and Its Vibration Noise Reduction on Bracket Features for AMDT

This manuscript explores the influences of the Foucault current on bracket vibration. Noise measurements are obtained in conditions that the coils are enclosed by the bracket, and exposed to the air for confirming the impact of Foucault current on bracket vibration. The outcomes illustrate that the bracket increases the primary noise to 21 d B. Throughout investigational modal computation, ordinary frequencies of the coils stay afar from the exciting frequency of 100 Hz but the ordinary frequency 72.924 Hz of the bracket stands near to 100 Hz, which similarly explicates the upsurge of the noise level. To do the computations on bracket vibrations instigated by the Foucault current, a finite-element method(FEM) has been utilized for evaluating the eddy current density, electromagnetic forces(EMF), coupled with bracket response in frequency domain. The calculations displayed a proper approval by mean of theoretic investigations, and the simulations analyzed effectively the bracket vibration produced by Eddy current, and the electromagnetic force(EMF). Referring to the simulation, the primary vibration characteristics, and noise level, the method by applying weights(stress) on the top bracket of transformer is proposed to lessen the vibration amplitude, and noise level. To indorse the utility, and application of the suggested technique, the experiment results are obtained, analyzed, and compared with those of simulations, primary vibration characteristics, and noise level.

Novel camera calibration method based on invariance of collinear points and pole-polar constraint

To address the eccentric error of circular marks in camera calibration,a circle location method based on the invariance of collinear points and pole–polar constraint is proposed in this paper.Firstly,the centers of the ellipses are extracted,and the real concentric circle center projection equation is established by exploiting the cross ratio invariance of the collinear points.Subsequently,since the infinite lines passing through the centers of the marks are parallel,the other center projection coordinates are expressed as the solution problem of linear equations.The problem of projection deviation caused by using the center of the ellipse as the real circle center projection is addressed,and the results are utilized as the true image points to achieve the high precision camera calibration.As demonstrated by the simulations and practical experiments,the proposed method performs a better location and calibration performance by achieving the actual center projection of circular marks.The relevant results confirm the precision and robustness of the proposed approach.

Extraction and analysis of tree canopy height information in high-voltage transmission-line corridors by using integrated optical remote sensing and LiDAR

Traditional inspection methods cannot quickly and accurately monitor tree barriers and safeguard the transmission lines.To solve these problems,in this study,we proposed a rapid canopy height information extraction method using optical remote sensing and LiDAR,and used UAV optical imagery with LiDAR to monitor the height of trees in a university and a high-voltage transmission line corridor in the Ningxia region.The results showed that the relative error of tree height extraction using UAV optical images was less than 5%,and the lowest relative error was 0.11%.The determination coefficient R^(2) between the optical image tree height extraction results and the measured tree height was 0.97,thus indicating a high correlation for both.In the field of tree barrier monitoring,the determination coefficient R^(2) of tree height extracted using airborne LiDAR point cloud,and canopy height model(CHM)and of the measured tree height were 0.947 and 0.931,respectively.The maximum and minimum relative error in tree height extraction performed using point cloud was 2.91%and 0.2%,respectively,with an extraction accuracy of over 95%.The experimental results demonstrated that it is feasible to use UAV optical remote sensing and LiDAR in monitoring tree barriers and tree height information extraction quickly and accurately,which is of great significance for the risk assessment and early warning of tree barriers in transmission-line corridors.

Fault diagnosis of rolling bearings with noise signal based on modified kernel principal component analysis and DC‐ResNet

In view of the influence of aliasing noise on the effectiveness and accuracy of bearing fault diagnosis,a bearing fault diagnosis algorithm based on the spatial decoupling method of modified kernel principal component analysis(MKPCA)and the residual network with deformable convolution(DC‐ResNet)is innovatively proposed.Firstly,the Gaussian noise with different signal‐to‐noise ratios(SNRs)is added to the data to simulate the different degrees of noise in the actual data acquisition process.The MKPCA is used to project the fault signal with different SNRs in the kernel space to reduce the data dimension and eliminate some noise effects.Finally,the DC‐ResNet model is used to further filter the noise effects and fully extract the fault features through the training of the preprocessed data.The proposed algorithm is tested on the Case Western Reserve University(CWRU)and Xi'an Jiaotong University and Changxing Sumyoung Technology Co.,Ltd.(XJTU‐SY)bearing data sets with different SNR noise.The fault diagnosis accuracy can reach 100%within 30 min,which has better performance than most of the existing methods.The experimental results show that the algorithm has an excellent effect on accuracy and computation complexity under different noise levels.

Constructing a stable interface on Ni-rich LiNi_(0.8)Co_(0.1)Mn_(0.1)O_(2) cathode via lactic acid-assisted engineering strategy

Ni-rich layered oxides are potential cathode materials for next-generation high energy density Li-ion batteries due to their high capacity and low cost.However,the inherently unstable surface properties,including high levels of residual Li compounds,dissolution of transition metal cations,and parasitic side reactions,have not been effectively addressed,leading to significant degradation in their electrochemical performance.In this study,we propose a simple and effective lactic acid-assisted interface engineering strategy to regulate the surface chemistry and properties of Ni-rich LiNi_(0.8)Co_(0.1)Mr_(0.1)O_(2) cathode.This novel surface treatment method successfully eliminates surface residual Li compounds,inhibits structural collapse,and mitigates cathode-electrolyte interface film growth.As a result,the lactic acidtreated LiNi_(0.8)Co_(0.1)Mn_(0.1)O_(2) achieved a remarkable capacity retention of 91.7% after 100 cycles at 0.5 C(25℃) and outstanding rate capability of 149.5 mA h g^(-1) at 10 C,significantly outperforming the pristine material.Furthermore,a pouch-type full cell incorporating the modified LiNi_(0.8)Co_(0.1)Mn_(0.1)O_(2) cathode demonstrates impressive long-term cycle life,retaining 81.5% of its capacity after 500 cycles at 1 C.More importantly,the thermal stability of the modified cathode is also dramatically improved.This study offers a valuable surface modification strategy for enhancing the overall performance of Ni-rich cathode materials.

Optimal Cooperative Secondary Control for Islanded DC Microgrids via a Fully Actuated Approach

DC-DC converter-based multi-bus DC microgrids(MGs) in series have received much attention, where the conflict between voltage recovery and current balancing has been a hot topic. The lack of models that accurately portray the electrical characteristics of actual MGs while is controller design-friendly has kept the issue active. To this end, this paper establishes a large-signal model containing the comprehensive dynamical behavior of the DC MGs based on the theory of high-order fully actuated systems, and proposes distributed optimal control based on this. The proposed secondary control method can achieve the two goals of voltage recovery and current sharing for multi-bus DC MGs. Additionally, the simple structure of the proposed approach is similar to one based on droop control, which allows this control technique to be easily implemented in a variety of modern microgrids with different configurations. In contrast to existing studies, the process of controller design in this paper is closely tied to the actual dynamics of the MGs. It is a prominent feature that enables engineers to customize the performance metrics of the system. In addition, the analysis of the stability of the closed-loop DC microgrid system, as well as the optimality and consensus of current sharing are given. Finally, a scaled-down solar and battery-based microgrid prototype with maximum power point tracking controller is developed in the laboratory to experimentally test the efficacy of the proposed control method.

Machine learning for parameters diagnosis of spark discharge by electro-acoustic signal

Discharge plasma parameter measurement is a key focus in low-temperature plasma research.Traditional diagnostics often require costly equipment,whereas electro-acoustic signals provide a rich,non-invasive,and less complex source of discharge information.This study harnesses machine learning to decode these signals.It establishes links between electro-acoustic signals and gas discharge parameters,such as power and distance,thus streamlining the prediction process.By building a spark discharge platform to collect electro-acoustic signals and implementing a series of acoustic signal processing techniques,the Mel-Frequency Cepstral Coefficients(MFCCs)of the acoustic signals are extracted to construct the predictors.Three machine learning models(Linear Regression,k-Nearest Neighbors,and Random Forest)are introduced and applied to the predictors to achieve real-time rapid diagnostic measurement of typical spark discharge power and discharge distance.All models display impressive performance in prediction precision and fitting abilities.Among them,the k-Nearest Neighbors model shows the best performance on discharge power prediction with the lowest mean square error(MSE=0.00571)and the highest R-squared value(R^(2)=0.93877).The experimental results show that the relationship between the electro-acoustic signal and the gas discharge power and distance can be effectively constructed based on the machine learning algorithm,which provides a new idea and basis for the online monitoring and real-time diagnosis of plasma parameters.

Review of Field Weakening Control Strategies of Permanent Magnet Synchronous Motors

Due to high power density,high efficiency,and accurate control performance,permanent magnet synchronous motors(PMSMs)have been widely adopted in equipment manufacturing and energy transformation fields.To expand the speed range under finite DC-bus voltage,extensive research on field weakening(FW)control strategies has been conducted.This paper summarizes the major FW control strategies of PMSMs,which are categorized into calculation-based methods,voltage closed-loop control methods,and model predictive control related methods.The existing strategies are analyzed and compared according to performance,robustness,and execution difficulty,which can facilitate the implementation of FW control.

Self-assembled S-scheme In_(2.77)S_(4)/K^(+)-doped g-C_(3)N_(4)photocatalyst with selective O_(2) reduction pathway for efficient H_(2)O_(2) production using water and air

The development of an efficient artificial H_(2)O_(2) photosynthesis system is a challenging work using H_(2)O and O_(2) as starting materials.Herein,3D In_(2.77)S_(4) nanoflower precursor was in-situ deposited on K^(+)-doped g-C_(3)N_(4)(KCN)nanosheets using a solvothermal method,then In_(2.77)S_(4)/KCN(IS/KCN)het-erojunction with an intimate interface was obtained after a calcination process.The investigation shows that the photocatalytic H_(2)O_(2) production rate of 50IS/KCN can reach up to 1.36 mmol g^(-1)h^(-1)without any sacrificial reagents under visible light irradiation,which is 9.2 times and 4.1 times higher than that of KCN and In_(2.77)S_(4)/respectively.The enhanced activity of the above composite can be mainly attributed to the S-scheme charge transfer route between KCN and In_(2.77)S_(4) according to density functional theory calculations,electron paramagnetic resonance and free radical capture tests,leading to an expanded light response range and rapid charge separation at their interface,as well as preserving the active electrons and holes for H_(2)O_(2) production.Besides,the unique 3D nanostructure and surface hydrophobicity of IS/KCN facilitate the diffusion and transportation of O_(2) around the active centers,the energy barriers of O_(2) protonation and H_(2)O_(2) desorption steps are ef-fectively reduced over the composite.In addition,this system also exhibits excellent light harvesting ability and stability.This work provides a potential strategy to explore a sustainable H_(2)O_(2) photo-synthesis pathway through the design of heterojunctions with intimate interfaces and desired reac-tion thermodynamics and kinetics.

Adaptive VSG control of flywheel energy storage array for frequency support in microgrids

The application of virtual synchronous generator(VSG)control in flywheel energy storage systems(FESS)is an effective solution for addressing the challenges related to reduced inertia and inadequate power supply in microgrids.Considering the significant variations among individual units within a flywheel array and the poor frequency regulation performance under conventional control approaches,this paper proposes an adaptive VSG control strategy for a flywheel energy storage array(FESA).First,by leveraging the FESA model,a variable acceleration factor is integrated into the speed-balance control strategy to effectively achieve better state of charge(SOC)equalization across units.Furthermore,energy control with a dead zone is introduced to prevent SOC of the FESA from exceeding the limit.The dead zone parameter is designed based on the SOC warning intervals of the flywheel array to mitigate its impact on regular operation.In addition,VSG technology is applied for the grid-connected control of the FESA,and the damping characteristic of the VSG is decoupled from the primary frequency regulation through power differential feedback.This ensures optimal dynamic performance while reducing the need for frequent involvement in frequency regulation.Subsequently,a parameter design method is developed through a small-signal stability analysis.Consequently,considering the SOC of the FESA,an adaptive control strategy for the inertia damping and the P/ωdroop coefficient of the VSG control is proposed to optimize the grid support services of the FESA.Finally,the effectiveness of the proposed control methods is demonstrated through electromagnetic transient simulations using MATLAB/Simulink.

Effect of attack angle on the electromagnetic wave transmission characteristics in the hypersonic plasma sheath of a re-entry vehicle

The attack angle may greatly affect the hypersonic plasma sheaths around the re-entry vehicle,thereby affecting the transmission characteristics of electromagnetic(EM)waves in the sheaths.In this paper,we propose an integrated three-dimensional(3D)model with various attack angles and realistic flying conditions of radio attenuation measurement C-II(RAM C-II)re-entry tasks for analyzing the effect of the attack angle on the transmission characteristics of EM waves in the sheaths.It is shown that the electron density and collision frequency of the sheath on the windward side can be increased by an order of magnitude with the increase of the attack angle.Meanwhile,the thickness of the sheath on the leeward side is increased where the electron density and collision frequency are reduced.The EM waves are mainly reflected on the windward plasma sheath due to the cutoff effect,and the radio-frequency(RF)blackout is mitigated if the antenna is positioned on the leeward side.Thus,by planning the trajectory properly and installing the antenna accordingly during the re-entry,it is possible to provide an approach for mitigation of the RF blackout problem to an extent.

Numerical studies for plasmas of a linear plasma device HIT-PSI with geometry modified SOLPS-ITER

The HIT-PSI is a linear plasma device built for physically simulating the high heat flux environment of future reactor divertors to test/develop advanced target plate materials.In this study,the geometry-modified SOLPS-ITER program is employed to examine the effects of the magnetic field strength and neutral pressure in the device on the heat flux experienced by the target plate of the HIT-PSI device.The findings of the numerical simulation indicate a positive correlation between the magnetic field strength and the heat flux density.Conversely,there is a negative correlation observed between the heat flux density and the neutral pressure.When the magnetic field strength at the axis exceeds 1 tesla and the neutral pressure falls below 10 Pa,the HIT-PSI has the capability to attain a heat flux of 10 MW·m-2 at the target plate.The simulation results offer a valuable point of reference for subsequent experiments at HIT-PSI.

Prospect of Hexagonal CsMg(I_(1-x)Br_(x))_(3) Alloys for Deep-Ultraviolet Light Emission

Materials for deep-ultraviolet(DUV)light emission are extremely rare,significantly limiting the development of efficient DUV light-emitting diodes.Here we report CsMg(I_(1−x)Br_(x))_(3) alloys as potential DUV light emitters.Based on rigorous first-principles hybrid functional calculations,we find that CsMgI_(3) has an indirect bandgap,while CsMgBr_(3) has a direct bandgap.Further,we employ a band unfolding technique for alloy supercell calculations to investigate the critical Br concentration in CsMg(I_(1−x)Br_(x))_(3) associated with the crossover from an indirect to a direct bandgap,which is found to be∼0.36.Thus,CsMg(I_(1−x)Br_(x))_(3) alloys with 0.366≤6≤1 cover a wide range of direct bandgap(4.38–5.37 eV;284–231 nm),falling well into the DUV regime.Our study will guide the development of efficient DUV light emitters.

Model-free Speed Control of Single-phase Flux Switching Motor with an Asymmetrical Rotor

This paper proposes and implements a model-free open-loop iterative learning control(ILC)strategy to realize the speed control of the single-phase flux switching motor(FSM)with an asymmetrical rotor.Base on the proposed winding control method,the asymmetrical rotor enables the motor to generate continuous positive torque for positive rotation,and relatively small resistance torque for negative rotation.An initial iteration coefficient and variable iteration coefficient optimized scheme was proposed based on the characteristics of the hardware circuit,thereby forming the model-free strategy.A series of prototype experiments was carried out.Experimental results verify the effectiveness and practicability of the proposed ILC strategy.

A Deep-Learning and Transfer-Learning Hybrid Aerosol Retrieval Algorithm for FY4-AGRI:Development and Verification over Asia

The Advanced Geosynchronous Radiation Imager(AGRI)is a mission-critical instrument for the Fengyun series of satellites.AGRI acquires full-disk images every 15 min and views East Asia every 5 min through 14 spectral bands,enabling the detection of highly variable aerosol optical depth(AOD).Quantitative retrieval of AOD has hitherto been challenging,especially over land.In this study,an AOD retrieval algorithm is proposed that combines deep learning and transfer learning.The algorithm uses core concepts from both the Dark Target(DT)and Deep Blue(DB)algorithms to select features for the machinelearning(ML)algorithm,allowing for AOD retrieval at 550 nm over both dark and bright surfaces.The algorithm consists of two steps:①A baseline deep neural network(DNN)with skip connections is developed using 10 min Advanced Himawari Imager(AHI)AODs as the target variable,and②sunphotometer AODs from 89 ground-based stations are used to fine-tune the DNN parameters.Out-of-station validation shows that the retrieved AOD attains high accuracy,characterized by a coefficient of determination(R2)of 0.70,a mean bias error(MBE)of 0.03,and a percentage of data within the expected error(EE)of 70.7%.A sensitivity study reveals that the top-of-atmosphere reflectance at 650 and 470 nm,as well as the surface reflectance at 650 nm,are the two largest sources of uncertainty impacting the retrieval.In a case study of monitoring an extreme aerosol event,the AGRI AOD is found to be able to capture the detailed temporal evolution of the event.This work demonstrates the superiority of the transfer-learning technique in satellite AOD retrievals and the applicability of the retrieved AGRI AOD in monitoring extreme pollution events.

A Novel Method for Aging Prediction of Railway Catenary Based on Improved Kalman Filter

The aging prediction of railway catenary is of profound significance for ensuring the regular operation of electrified trains.However,in real-world scenarios,accurate predictions are challenging due to various interferences.This paper addresses this challenge by proposing a novel method for predicting the aging of railway catenary based on an improved Kalman filter(KF).The proposed method focuses on modifying the priori state estimate covariance and measurement error covariance of the KF to enhance accuracy in complex environments.By comparing the optimal displacement value with the theoretically calculated value based on the thermal expansion effect of metals,it becomes possible to ascertain the aging status of the catenary.To improve prediction accuracy,a railway catenary aging prediction model is constructed by integrating the Takagi-Sugeno(T-S)fuzzy neural network(FNN)and KF.In this model,an adaptive training method is introduced,allowing the FNN to use fewer fuzzy rules.The inputs of the model include time,temperature,and historical displacement,while the output is the predicted displacement.Furthermore,the KF is enhanced by modifying its prior state estimate covariance and measurement error covariance.These modifications contribute to more accurate predictions.Lastly,a low-power experimental platform based on FPGA is implemented to verify the effectiveness of the proposed method.The test results demonstrate that the proposed method outperforms the compared method,showcasing its superior performance.

Fault Diagnosis of Linear Guide Rails Based on SSTG Combined with CA-DenseNet

Monitoring the status of linear guide rails is essential because they are important components in linear motion mechanical production.Thus,this paper proposes a new method of conducting the fault diagnosis of linear guide rails.First,synchrosqueezing transform(SST)combined with Gaussian high-pass filter,termed as SSTG,is proposed to process vibration signals of linear guide rails and obtain time-frequency images,thus helping realize fault feature visual enhancement.Next,the coordinate attention(CA)mechanism is introduced to promote the DenseNet model and obtain the CA-DenseNet deep learning framework,thus realizing accurate fault classifica-tion.Comparison experiments with other methods reveal that the proposed method has a high classification accuracy of up to 95.0%.The experimental results further demonstrate the effectiveness and robustness of the proposed method for the fault diagnosis of linear guide rails.

A New Method of Wind Turbine Bearing Fault Diagnosis Based on Multi-Masking Empirical Mode Decomposition and Fuzzy C-Means Clustering

Based on Multi-Masking Empirical Mode Decomposition (MMEMD) and fuzzy c-means (FCM) clustering, a new method of wind turbine bearing fault diagnosis FCM-MMEMD is proposed, which can determine the fault accurately and timely. First, FCM clustering is employed to classify the data into different clusters, which helps to estimate whether there is a fault and how many fault types there are. If fault signals exist, the fault vibration signals are then demodulated and decomposed into different frequency bands by MMEMD in order to be analyzed further. In order to overcome the mode mixing defect of empirical mode decomposition (EMD), a novel method called MMEMD is proposed. It is an improvement to masking empirical mode decomposition (MEMD). By adding multi-masking signals to the signals to be decomposed in different levels, it can restrain low-frequency components from mixing in highfrequency components effectively in the sifting process and then suppress the mode mixing. It has the advantages of easy implementation and strong ability of suppressing modal mixing. The fault type is determined by Hilbert envelope finally. The results of simulation signal decomposition showed the high performance of MMEMD. Experiments of bearing fault diagnosis in wind turbine bearing fault diagnosis proved the validity and high accuracy of the new method.