Short-term prediction of photovoltaic power generation based on LMD-EE-ESN with error correction

Considering the instability of the output power of photovoltaic(PV)generation system,to improve the power regulation ability of PV power during grid-connected operation,based on the quantitative analysis of meteorological conditions,a short-term prediction method of PV power based on LMD-EE-ESN with iterative error correction was proposed.Firstly,through the fuzzy clustering processing of meteorological conditions,taking the power curves of PV power generation in sunny,rainy or snowy,cloudy,and changeable weather as the reference,the local mean decomposition(LMD)was carried out respectively,and their energy entropy(EE)was taken as the meteorological characteristics.Then,the historical generation power series was decomposed by LMD algorithm,and the hierarchical prediction of the power curve was realized by echo state network(ESN)prediction algorithm combined with meteorological characteristics.Finally,the iterative error theory was applied to the correction of power prediction results.The analysis of the historical data in the PV power generation system shows that this method avoids the influence of meteorological conditions in the short-term prediction of PV output power,and improves the accuracy of power prediction on the condition of hierarchical prediction and iterative error correction.

FAULT DIAGNOSIS APPROACH FOR ROLLER BEARINGS BASED ON EMPIRICAL MODE DECOMPOSITION METHOD AND HILBERT TRANSFORM

Based upon empirical mode decomposition (EMD) method and Hilbert spectrum, a method for fault diagnosis of roller bearing is proposed. The orthogonal wavelet bases are used to translate vibration signals of a roller bearing into time-scale representation, then, an envelope signal can be obtained by envelope spectrum analysis of wavelet coefficients of high scales. By applying EMD method and Hilbert transform to the envelope signal, we can get the local Hilbert marginal spectrum from which the faults in a roller bearing can be diagnosed and fault patterns can be identified. Practical vibration signals measured from roller bearings with out-race faults or inner-race faults are analyzed by the proposed method. The results show that the proposed method is superior to the traditional envelope spectrum method in extracting the fault characteristics of roller bearings.

Adaptive Bearing Fault Diagnosis based on Wavelet Packet Decomposition and LMD Permutation Entropy

Bearing fault signal is nonlinear and non-stationary, therefore proposed a fault feature extraction method based on wavelet packet decomposition （WPD） and local mean decomposition （LMD） permutation entropy, which is based on the support vector machine （SVM） as the feature vector pattern recognition device Firstly, the wavelet packet analysis method is used to denoise the original vibration signal, and the frequency band division and signal reconstruction are carried out according to the characteristic frequency. Then the decomposition of the reconstructed signal is decomposed into a number of product functions （PE） by the local mean decomposition （LMD） , and the permutation entropy of the PF component which contains the main fault information is calculated to realize the feature quantization of the PF component. Finally, the entropy feature vector input multi-classification SVM, which is used to determine the type of fault and fault degree of bearing The experimental results show that the recognition rate of rolling bearing fault diagnosis is 95%. Comparing with other methods, the present this method can effectively extract the features of bearing fault and has a higher recognition accuracy

Short-Term Prediction of Photovoltaic Power Generation Based on LMD Permutation Entropy and Singular Spectrum Analysis

The power output state of photovoltaic power generation is affected by the earth’s rotation and solar radiation intensity.On the one hand,its output sequence has daily periodicity;on the other hand,it has discrete randomness.With the development of new energy economy,the proportion of photovoltaic energy increased accordingly.In order to solve the problem of improving the energy conversion efficiency in the grid-connected optical network and ensure the stability of photovoltaic power generation,this paper proposes the short-termprediction of photovoltaic power generation based on the improvedmulti-scale permutation entropy,localmean decomposition and singular spectrum analysis algorithm.Firstly,taking the power output per unit day as the research object,the multi-scale permutation entropy is used to calculate the eigenvectors under different weather conditions,and the cluster analysis is used to reconstruct the historical power generation under typical weather rainy and snowy,sunny,abrupt,cloudy.Then,local mean decomposition(LMD)is used to decompose the output sequence,so as to extract more detail components of the reconstructed output sequence.Finally,combined with the weather forecast of the Meteorological Bureau for the next day,the singular spectrumanalysis algorithm is used to predict the photovoltaic classification of the recombination decomposition sequence under typical weather.Through the verification and analysis of examples,the hierarchical prediction experiments of reconstructed and non-reconstructed output sequences are compared.The results show that the algorithm proposed in this paper is effective in realizing the short-term prediction of photovoltaic generator,and has the advantages of simple structure and high prediction accuracy.

Feature Extraction of Bearing Vibration Signals Using Second Generation Wavelet and Spline-Based Local Mean Decomposition

In order to extract the fault feature frequency of weak bearing signals,we put forward a local mean decomposition(LMD)method combining with the second generation wavelet transform.After performing the second generation wavelet denoising,the spline-based LMD is used to decompose the high-frequency detail signals of the second generation wavelet signals into a number of production functions(PFs).Power spectrum analysis is applied to the PFs to detect bearing fault information and identify the fault patterns.Application in inner and outer race fault diagnosis of rolling bearing shows that the method can extract the vibration features of rolling bearing fault.This method is suitable for extracting the fault characteristics of the weak fault signals in strong noise.

Weak characteristic information extraction from early fault of wind turbine generator gearboxKeywords wind turbine generator gearbox, B-singular value decomposition, local mean decomposition, weak characteristic information extraction, early fault warning

Given the weak early degradation characteristic information during early fault evolution in gearbox of wind turbine generator, traditional singular value decomposition （SVD）-based denoising may result in loss of useful information. A weak characteristic information extraction based on μ-SVD and local mean decomposition （LMD） is developed to address this problem. The basic principle of the method is as follows： Determine the denoising order based on cumulative contribution rate, perform signal reconstruction, extract and subject the noisy part of signal to LMD and μ-SVD denoising, and obtain denoised signal through superposition. Experimental results show that this method can significantly weaken signal noise, effectively extract the weak characteristic information of early fault, and facilitate the early fault warning and dynamic predictive maintenance.

Corrosion Inhibition of Aluminium in Gas and Acid Media by Some Chalcone-Based N-(3-Aminopropyl)Imidazoles: TD-DFT-Based FMO, Conceptual DFT, QTAIM and EDA Studies

The efficacy and mode of action of five chalcone-based imidazole derivatives as corrosion inhibitors of aluminium metal in gas-phase and acidic medium have been investigated herein via quantum chemical calculations. Dispersion-corrected DFT (DFT-D3) and time-dependent DFT (TD-DFT) calculations were performed at PBE0/def2-TZVP//PBEh-3c and CAM-B3LYP/def2- TZVP levels of theory, respectively. Conceptual DFT, the quantum theory of atoms-in-molecules (QTAIM) and local energy decomposition (LED) analyses have been performed. The LED analysis was performed at the coupled-cluster singles and doubles with perturbative triples (CCSD(T))/def2-SVP level of theory. Frontier molecular orbital energy gaps calculated using the TD-DFT method are found to lie in the range 3.574 - 4.444 eV, indicative of good adsorption and corrosion inhibition efficacies of the investigated molecules. The interactions between aluminium and the inhibitor molecules studied are found to be energetically favorable, owing to negative computed interaction energy values. Furthermore, QTAIM analysis revealed metal-carbon, metal-oxygen and metal-nitrogen interactions in the inhibitor-aluminium complexes, which are predominantly electrostatic in character, according to LED analysis results. Calculated proton affinities (PAs) have revealed the anticorrosion potentials of the investigated inhibitors in acidic medium, with a noticeable dependency on temperature within the range 273.15 - 343.15 K.

Application of a joint algorithm based on L-T to pulse pressure detection signal of fiber Fabry-Perot nano pressure sensor

An improved denoising method and its application in pulse beat signal denoising are studied.The proposed denoising algorithm takes the advantages of local mean decomposition(LMD)and time-frequency peak filtering(TFPF),called L-T algorithm.As a classical time-frequency filtering method,TFPF can effectively suppress random noise with signal amplitude retained when selecting a longer window length,while the signal amplitude will be seriously attenuated when selecting a shorter window length.In order to maintain effective signal amplitude and suppress random noise,LMD and TFPF are improved.Firstly,the original signal is decomposed into progression-free survival(PFS)by LMD,and then the standard error of mean(SEM)of each product function is calculated to classify many PFSs into useful component,mixed component and noise component.Secondly,by using the shorter window TFPF for useful component and the longer window TFPF for mixed component,noise component is removed and the final signal is obtained after reconstruction.Finally,the proposed algorithm is used for noise reduction of an Fabry-Perot(F-P)pressure sensor.Experimental results show that compared with traditional wavelet,L-T algorithm has better denoising effect on sampled data.

Theoretical Study on Xe…N non-Covalent Interactions:Three Hybridization N with XeO_(3) and XeOF_(2)

The interactions of complexes of XeOF_(2) and XeO_(3) with a series of different hybridization N-containing donors are studied by means of DFT and MP_(2) calculations.The aerogen bonding interaction energies range from 6.5 kcal/mol to19.9 kcal/mol between XeO_(3) or XeOF_(2) and typical N-containing donors.The sequence of interaction for N-containing hy-bridization is sp^(3)>sp^(2)>sp,and XeO_(3)is higher than XeOF_(2).For some donors of sp^(2)and sp^(3) hybridization,the steric effect plays a minor role in the interaction with the evidence of reduced density gradient plots.The dominant stable part is the electrostatic interaction.In complex of XeO_(3),the weight of polarization is larger than dispersion,while the situation is opposite for XeOF_(2)complexes.Except for the sum of the maximum value of molecular electrostatic potential on Xe atom and minimum value of molecular electrostatic potential on N atom,the other five interaction parameters including the potential energy density at bond critical point,the equilibrium distances,interaction energies with the basis set superposition error correction,localized molecular orbital energy decomposition analysis interaction energies,and the electron charge density,show great linear correlation coefficients with each other.

Mode decomposition and source localization performance for the short vertical array in shallow water

In this paper, by using the fast iterative method of mode decomposition[12], source range-depth localization performance of MMP for three kinds of vertical array (short, sparse and short-sparse arrays) in shallow water with a downward refraction sound-speed profile in the surnmertime is discussed; the accuracy of mode decomposition is measured by its rootmean-square error, RMS. The numerical results illustrate that the accuracy of source range and depth estimation are raised and the sidelobes are effectively suppressed. The short-sparse vertical array not only has shorter length and fewer hydrophones, but also can be applied to the different sea areas with various depth, so it is a practical type of vertical arrny in the engineering project of the passive source localization.

Novel passive localization algorithm based on double side matrix-restricted total least squares

In order to solve the bearings-only passive localization problem in the presence of erroneous observer position, a novel algorithm based on double side matrix-restricted total least squares （DSMRTLS） is proposed. First, the aforementioned passive localization problem is transferred to the DSMRTLS problem by deriving a multiplicative structure for both the observation matrix and the observation vector. Second, the corresponding optimization problem of the DSMRTLS problem without constraint is derived, which can be approximated as the generalized Rayleigh quotient minimization problem. Then, the localization solution which is globally optimal and asymptotically unbiased can be got by generalized eigenvalue decomposition. Simulation results verify the rationality of the approximation and the good performance of the proposed algorithm compared with several typical algorithms.

In-situ EXAFS study on the thermal decomposition of TiH_2

Thermal decomposition behaviors of TiH_2 powder under a flowing helium atmosphere and in a low vacuum condition have been studied using an in situ EXAFS technique.By an EXAFS analysis containing the multiple scattering paths including H atoms,the changes of the hydrogen stoichiometric ratio and the phase transformation sequence are obtained.The results demonstrate that the initial decomposition temperature is dependent on experimental conditions,which occurs,respectively,at about 300 and 400℃ in a low vacuum condition and under a flowing helium atmosphere.During the decomposition process of TiH_2 in a low vacuum condition,the sample experiences a phase change process：δ（TiH_2）→δ（TiH_x）→δ（TiH_1）＋β（TiH_x）→δ（TiH_x）＋β（TiH_x）＋α（Ti）→β（TiH_x）＋α（Ti）→α（Ti）＋β（Ti）.This study offers a way to detect the structural information of hydrogen.A detailed discussion about the decomposition process of TiH_2 is given in this paper.

基于多尺度局部极值分解与ResNet152的红外与可见光图像融合

为了进一步提升红外与可见光图像融合方法的性能,本文提出了一种基于多尺度局部极值分解与深度学习网络ResNet152的红外与可见光图像融合方法。首先,利用多尺度局部极值分解(multiscale local extrema decomposition,MLED)方法将源图像分解为近似图像和细节图像,分离出源图像中重叠的重要特征信息。然后采用残差网络ResNet152深度提取源图像的多维显著特征,以l_(1)-范数作为活性测度生成显著特征图,对近似图像进行加权平均融合,以保持能量和残留细节信息不丢失。在细节图像中,利用“系数绝对值取大”规则获得初始决策图,源图像作为引导图像,初始决策图作为输入图像进行引导滤波处理,得到优化决策图,计算加权局部能量得到能量显著图,对细节图像进行加权平均融合,使融合图像具有丰富的纹理细节和良好的视觉边缘感知。最后,对近似融合图像和细节融合图像进行重构,得到融合图像。实验结果表明,与现有的典型融合方法相比,本文所提出的融合方法在客观评价和视觉感受方面都取得了最好的效果。

Nanostructures formation in ferroelectrics in the process of phase transformation

Received 26 June 2014;Revised 13 October 2014;Accepted 20 October 2014;Published 12 November 2014 Inhomogeneous states caused by the coexistence of the ferroelectric(FE)and antiferroelectric(AFE)phases in lead–zirconate–titanate based solid solutions have been investigated.It has been found that the domains of the FE and AFE phases with sizes of the order of 20 nm to 30 nm coexist in the bulk of the samples due to a small difference in the free energies of these phases.The coherent character of the interphase boundaries(IPBs)leads to the concentration of the elastic stresses along these boundaries.These elastic stresses cause the local decomposition of the solid solution and formation of segregates near the IPBS due to the condition that equivalent positions of the crystal lattice are occupied by the ions with different sizes.The sizes of the segregates formed in this way are of the order 8 nm to 15 nm.Some physical effects caused by the presence of these segregate nanostructures are analyzed and discussed.

Theoretical Study on the Hydrogen Bonding Interactions in Complexes of 5-Hydroxytryptamine with Water

The energies, geometries and harmonic vibrational frequencies of 1 ： 1 5-hydroxytryptamine-water （5-HT-H20） complexes are studied at the MP2/6-311 ＋ ＋ G（d,p） level. Natural bond orbital （NBO）, quantum theory of atoms in molecules （QTAIM） analyses and the localized molecular orbital energy decomposition analysis （LMO-EDA） were performed to explore the nature of the hydrogen-bonding interactions in these complexes. Various types of hydro- gen bonds （H-bonds） are formed in these 5-HT-H20 complexes. The intermolecular C4H55HT＇＂Ow H-bond in HTW3 is strengthened due to the cooperativity, whereas no such cooperativity is found in the other 5-HT-H20 complexes. H-bond in which nitrogen atom of amino in 5-HT acted as proton donors was stronger than other H-bonds. Our researches show that the hydrogen bonding interaction plays a vital role on the relative stabilities of 5-HT-H20 complexes.