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Short-term prediction of photovoltaic power generation based on LMD-EE-ESN with error correction
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作者 YU Xiangqian LI Zheng 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2024年第3期360-368,共9页
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 meteorolog... 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. 展开更多
关键词 photovoltaic(PV)power generation system short-term forecast local mean decomposition(lmd) energy entropy(EE) echo state network(ESN)
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Adaptive Bearing Fault Diagnosis based on Wavelet Packet Decomposition and LMD Permutation Entropy 被引量:1
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作者 WANG Ming-yue MIAO Bing-rong YUAN Cheng-biao 《International Journal of Plant Engineering and Management》 2016年第4期202-216,共15页
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 ... 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 展开更多
关键词 fault diagnosis wavelet packet decomposition WPD local mean decomposition lmd permutation entropy support vector machine (SVM)
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Review of local mean decomposition and its application in fault diagnosis of rotating machinery 被引量:5
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作者 LI Yongbo SI Shubin +1 位作者 LIU Zhiliang LIANG Xihui 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2019年第4期799-814,共16页
Rotating machinery is widely used in the industry.They are vulnerable to many kinds of damages especially for those working under tough and time-varying operation conditions.Early detection of these damages is importa... Rotating machinery is widely used in the industry.They are vulnerable to many kinds of damages especially for those working under tough and time-varying operation conditions.Early detection of these damages is important,otherwise,they may lead to large economic loss even a catastrophe.Many signal processing methods have been developed for fault diagnosis of the rotating machinery.Local mean decomposition(LMD)is an adaptive mode decomposition method that can decompose a complicated signal into a series of mono-components,namely product functions(PFs).In recent years,many researchers have adopted LMD in fault detection and diagnosis of rotating machines.We give a comprehensive review of LMD in fault detection and diagnosis of rotating machines.First,the LMD is described.The advantages,disadvantages and some improved LMD methods are presented.Then,a comprehensive review on applications of LMD in fault diagnosis of the rotating machinery is given.The review is divided into four parts:fault diagnosis of gears,fault diagnosis of rotors,fault diagnosis of bearings,and other LMD applications.In each of these four parts,a review is given to applications applying the LMD,improved LMD,and LMD-based combination methods,respectively.We give a summary of this review and some future potential topics at the end. 展开更多
关键词 local mean decomposition(lmd) SIGNAL processing GEAR ROTOR BEARING
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Application of a joint algorithm based on L-T to pulse pressure detection signal of fiber Fabry-Perot nano pressure sensor
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作者 FENG Fei QIN Li 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2021年第1期61-67,共7页
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)... 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. 展开更多
关键词 local mean decomposition(lmd) time-frequency peak filtering(TFPT) noise reduction Fabry-Perot(F-P)sensor
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Feature Extraction of Bearing Vibration Signals Using Second Generation Wavelet and Spline-Based Local Mean Decomposition 被引量:5
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作者 文成玉 董良 金欣 《Journal of Shanghai Jiaotong university(Science)》 EI 2015年第1期56-60,共5页
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 generatio... 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. 展开更多
关键词 second generation wavelet transform local mean decomposition(lmd) feature extraction fault diagnosis
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