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Chinese Journal of Electrical Engineering 2023年第3期9卷 99-110页

作者： Siti Nor Azlina M.Ghazali Muhamad Zahim Sujod Mohd Shawal Jadin Department of Electrical Engineering Universiti Malaysia PahangPekan 26600Malaysia Faculty of Electrical and Electronics Engineering Technology Universiti Malaysia PahangPekan 26600Malaysia

Nonlinear photovoltaic(PV)output is greatly affected by the nonuniform distribution of daily irradiance,preventing conventional protection devices from reliably detecting faults.Smart fault diagnosis and good maintenance systems are essential for optimizing the overall productivity of a PV system and improving its life cycle.Hence,a multiscale smart fault diagnosis model for improved PV system maintenance strategies is proposed.This study focuses on diagnosing permanent faults(open-circuit faults,ground faults,and line-line faults)and temporary faults(partial shading)in PV arrays,using the random forest algorithm to conduct time-series analysis of waveform length and autoregression(RF-WLAR)as the main features,with 10-fold cross-validation using Matlab/Simulink.The actual irradiance data at 5.86°N and 102.03°E were used as inputs to produce simulated data that closely matched the on-site PV output data.Fault data from the maintenance database of a 2 MW PV power plant in Pasir Mas Kelantan,Malaysia,were used for field testing to verify the developed model.The RF-WLAR model achieved an average fault-type classification accuracy of 98%,with 100%accuracy in classifying partial shading and line-line faults.

关键词： autoregressive PV fault diagnosis supervised machine learning simulation waveform length

Quantifying the thermal damping effect in underground vertical shafts using the nonlinear autoregressive with external input(NARX) algorithm

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International Journal of Mining Science and Technology 2019年第2期29卷 255-262页

作者： Pedram Roghanchi Karoly C.Kocsis Mineral Engineering Derpartment New Mexico Institute of Mining and Technology Mining Engineering Derpartment University of Nevada

As air descends the intake shaft, its infrastructure, lining and the strata will emit heat during the night when the intake air is cool and, on the contrary, will absorb heat during the day when the temperature of the air becomes greater than that of the strata. This cyclic phenomenon, also known as the "thermal damping effect" will continue throughout the year reducing the effect of surface air temperature variation. The objective of this paper is to quantify the thermal damping effect in vertical underground airways. A nonlinear autoregressive time series with external input(NARX) algorithm was used as a novel method to predict the dry-bulb temperature(Td) at the bottom of intake shafts as a function of surface air temperature. Analyses demonstrated that the artificial neural network(ANN) model could accurately predict the temperature at the bottom of a shaft. Furthermore, an attempt was made to quantify typical "damping coefficient" for both production and ventilation shafts through simple linear regression models. Comparisons between the collected climatic data and the regression-based predictions show that a simple linear regression model provides an acceptable accuracy when predicting the Tdat the bottom of intake shafts.

关键词： Underground mining Vertical openings Thermal damping effect Artificial neural network Nonlinear autoregressive with external input(NARX)

Goodness-of-fit tests for vector autoregressive models in time series

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Science China Mathematics 2010年第7期53卷 187-202页

作者： WU JianHong 1,& ZHU LiXing 21 College of Statistics and Mathematics,Zhejiang Gongshang University,Hangzhou 310018,China 2 Department of Mathematics,Hong Kong Baptist University,Hong Kong,China College of Statistics and Mathematics Zhejiang Gongshang University Hangzhou China Department of Mathematics Hong Kong Baptist University Hong Kong China

The paper proposes and studies some diagnostic tools for checking the goodness-of-fit of general parametric vector autoregressive models in time series. The resulted tests are asymptotically chi-squared under the null hypothesis and can detect the alternatives converging to the null at a parametric rate. The tests involve weight functions,which provides us with the flexibility to choose scores for enhancing power performance,especially under directional alternatives. When the alternatives are not directional,we construct asymptotically distribution-free maximin tests for a large class of alternatives. A possibility to construct score-based omnibus tests is discussed when the alternative is saturated. The power performance is also investigated. In addition,when the sample size is small,a nonparametric Monte Carlo test approach for dependent data is proposed to improve the performance of the tests. The algorithm is easy to implement. Simulation studies and real applications are carried out for illustration.

关键词： goodness-of-fit test maximin test nonparametric Monte Carlo test score type test time series vector autoregressive model

Nonlinear autoregressive models with heavy-tailed innovation

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Science China Mathematics 2005年第3期48卷 333-340页

作者： JIN Yang & AN Hongzhi School of Statistics, Renmin University of China, Beijing 100872, China Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100080, China Sch. of Statistics Renmin Univ. of China Beijing 100872 China Acad. of Mathematics and Syst. Sci. Chinese Acad. of Sci. Beijing 100080 China

In this paper, we discuss the relationship between the stationary marginal tail probability and the innovation's tail probability of nonlJnear autoregressive models. We show that under certain conditions that ensure the stationarity and ergodicity, one dimension stationary marginal distribution has the heavy-tailed probability property with the same index as that of the innovation's tail probability.

关键词： nonlinear autoregressive model, innovation, tail probability.

On tail behavior of nonlinear autoregressive functional conditional heteroscedastic model with heavy-tailed innovations

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Science China Mathematics 2005年第9期48卷 1169-1181页

作者： PAN Jiazhu WU Guangxu LMAM and School of Mathematical Sciences Peking University Beijing 100871 China LMAM and School of Mathematical Sciences Peking University Beijing 100871 China

We study the tail probability of the stationary distribution of nonparametric nonlinear autoregressive functional conditional heteroscedastic (NARFCH) model with heavytailed innovations. Our result shows that the tail of the stationary marginal distribution of an NARFCH series is heavily dependent on its conditional variance. When the innovations are heavy-tailed, the tail of the stationary marginal distribution of the series will become heavier or thinner than that of its innovations. We give some specific formulas to show how the increment or decrement of tail heaviness depends on the assumption on the conditional variance function. Some examples are given.

关键词： tail probability, stationary distribution, nonlinear AR model, nonlinear autoregressive functional conditional heteroscedastic model, heavy-tailed distribution.

SECOND　ORDER　ASYMPTOTIC　EFFICIENCY　IN　A　PARTLY　autoregressive　MODEL

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Systems Science and Mathematical Sciences 1996年第2期9卷 164-170页

作者： LIANG Hua(Institute of Systems Science, Academia Silica, Beijing 100080, China) 中科院系统科学所北京

ＳＥＣＯＮＤＯＲＤＥＲＡＳＹＭＰＴＯＴＩＣＥＦＦＩＣＩＥＮＣＹＩＮＡＰＡＲＴＬＹＡＵＴＯＲＥＧＲＥＳＳＩＶＥＭＯＤＥＬＬＩＡＮＧＨｕａ（ＩｎｓｔｉｔｕｔｅｏｆＳｙｓｔｅｍｓＳｃｉｅｎｃｅ，ＡｃａｄｅｍｉａＳｉｌｉｃａ... 详细信息

ＳＥＣＯＮＤＯＲＤＥＲＡＳＹＭＰＴＯＴＩＣＥＦＦＩＣＩＥＮＣＹＩＮＡＰＡＲＴＬＹＡＵＴＯＲＥＧＲＥＳＳＩＶＥＭＯＤＥＬＬＩＡＮＧＨｕａ（ＩｎｓｔｉｔｕｔｅｏｆＳｙｓｔｅｍｓＳｃｉｅｎｃｅ，ＡｃａｄｅｍｉａＳｉｌｉｃａ，Ｂｅｉｊｉｎｇ１０００８０，Ｃｈ...

关键词：篠econd order asymptotic efficiency partly autoregressive model piecewisepolynomial.

On the Asymptotic Distribution of the Generalized Partial Inverse Correlation for autoregressive Moving-Average Proceses

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Systems Science and Systems Engineering 1998年第1期8卷 113-120页

作者： $$$$ L Guoying Department of Mathematics, Shanxi University, Tai Yuan, 030006 CHEN Min Institute of Systems Science, Academia Sinica, Beijing, 100080

ＯｎｔｈｅＡｓｙｍｐｔｏｔｉｃＤｉｓｔｒｉｂｕｔｉｏｎｏｆｔｈｅＧｅｎｅｒａｌｉｚｅｄＰａｒｔｉａｌＩｎｖｅｒｓｅＣｏｒｒｅｌａｔｉｏｎｆｏｒＡｕｔｏｒｅｇｒｅｓｉｖｅＭｏｖｉｎｇ－ＡｖｅｒａｇｅＰｒｏｃｅｓｅｓ⒇Ｌ... 详细信息

ＯｎｔｈｅＡｓｙｍｐｔｏｔｉｃＤｉｓｔｒｉｂｕｔｉｏｎｏｆｔｈｅＧｅｎｅｒａｌｉｚｅｄＰａｒｔｉａｌＩｎｖｅｒｓｅＣｏｒｒｅｌａｔｉｏｎｆｏｒＡｕｔｏｒｅｇｒｅｓｉｖｅＭｏｖｉｎｇ－ＡｖｅｒａｇｅＰｒｏｃｅｓｅｓ⒇ＬＵＧｕｏｙｉｎｇＤｅｐａｒｔｍｅｎ...

关键词： autoregressive Asymptotic

SOME LEAST SQUARES ESTIMATES OF THE autoregressive MODELS

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Numerical Mathematics A Journal of Chinese Universities(English Series) 1999年第1期8卷 113-124页

作者：林正华盛中平王嘉松

In this paper, we present some iterative methods for solving lth order autoregressive models, prove global convergence for l=1 case, and the numerical results of new algorithms seem to be more efficient than the ones ... 详细信息

关键词： autoregressive model iterative method convergence.

Improving the autoregressive Modeling Method in Random Noise Suppression of GPR Data Using Undecimated Discrete Wavelet Transform

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Journal of Signal and Information Processing 2018年第1期9卷 24-35页

作者： Behrooz Oskooi Amin Ebrahimi Bardar Alireza Goodarzi Institute of Geophysics University of Tehran Tehran Iran Kerman Graduate University of Advanced Technology Kerman Iran

Geophysics has played a significant and efficient role in studying geological structures over the past decades as the goal of geophysical data acquisition is to investigate underground phenomena with the highest possible level of accuracy. The ground penetrating radar (GPR) method is used as a nondestructive method to reveal shallow structures by beaming electromagnetic waves through the Earth and recording the received reflections, albeit inevitably, along with random noise. Various types of noise affect GPR data, among the most important of which are random noise resulting from arbitrary motions of particles during data acquisition. Random noise which exists always and at all frequencies, along with coherent noise, reduces the quality of GPR data and must be reduced as much as possible. Over the recent years, discrete wavelet transform has proved to be an efficient tool in signal processing, especially in image and signal compressing and noise suppression. It also allows for obtaining an accurate understanding of the signal properties. In this study, we have used the autoregression in both wavelet and f-x domains to suppress random noise in synthetic and real GPR data. Finally, we compare noise suppression in the two domains. Our results reveal that noise suppression is conducted more efficiently in the wavelet domain due to decomposing the signal into separate subbands and exclusively applying the method parameters in autoregression modeling for each subband.

关键词： Ground Penetrating Radar Random Noise Undecimated Discrete Wavelet Transform autoregressive Filter

Estimation for Nonnegative First-Order autoregressive Processes with an Unknown Location Parameter

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Applied Mathematics 2012年第12期3卷 2133-2147页

作者： Andrew Bartlett William McCormick University of Georgia Athens USA

Consider a first-order autoregressive processes , where the innovations are nonnegative random variables with regular variation at both the right endpoint infinity and the unknown left endpoint θ. We propose estimates for the autocorrelation parameter f and the unknown location parameter θ by taking the ratio of two sample values chosen with respect to an extreme value criteria for f and by taking the minimum of over the observed series, where represents our estimate for f. The joint limit distribution of the proposed estimators is derived using point process techniques. A simulation study is provided to examine the small sample size behavior of these estimates.

关键词： Nonnegative Time Series autoregressive Processes Extreme Value Estimator Regular Variation Point Processes