Higher-order expansions of powered extremes of logarithmic general error distribution

In this paper,Let M_(n)denote the maximum of logarithmic general error distribution with parameter v≥1.Higher-order expansions for distributions of powered extremes M_(n)^(p)are derived under an optimal choice of normalizing constants.It is shown that M_(n)^(p),when v=1,converges to the Frechet extreme value distribution at the rate of 1/n,and if v>1 then M_(n)^(p)converges to the Gumbel extreme value distribution at the rate of(loglogn)^(2)=(log n)^(1-1/v).

Research on the IL-Bagging-DHKELM Short-Term Wind Power Prediction Algorithm Based on Error AP Clustering Analysis

With the continuous advancement of China’s“peak carbon dioxide emissions and Carbon Neutrality”process,the proportion of wind power is increasing.In the current research,aiming at the problem that the forecasting model is outdated due to the continuous updating of wind power data,a short-term wind power forecasting algorithm based on Incremental Learning-Bagging Deep Hybrid Kernel Extreme Learning Machine(IL-Bagging-DHKELM)error affinity propagation cluster analysis is proposed.The algorithm effectively combines deep hybrid kernel extreme learning machine(DHKELM)with incremental learning(IL).Firstly,an initial wind power prediction model is trained using the Bagging-DHKELM model.Secondly,Euclidean morphological distance affinity propagation AP clustering algorithm is used to cluster and analyze the prediction error of wind power obtained from the initial training model.Finally,the correlation between wind power prediction errors and Numerical Weather Prediction(NWP)data is introduced as incremental updates to the initial wind power prediction model.During the incremental learning process,multiple error performance indicators are used to measure the overall model performance,thereby enabling incremental updates of wind power models.Practical examples show the method proposed in this article reduces the root mean square error of the initial model by 1.9 percentage points,indicating that this method can be better adapted to the current scenario of the continuous increase in wind power penetration rate.The accuracy and precision of wind power generation prediction are effectively improved through the method.

Anomaly-Resistant Decentralized State Estimation Under Minimum Error Entropy With Fiducial Points for Wide-Area Power Systems

This paper investigates the anomaly-resistant decentralized state estimation(SE) problem for a class of wide-area power systems which are divided into several non-overlapping areas connected through transmission lines. Two classes of measurements(i.e., local measurements and edge measurements) are obtained, respectively, from the individual area and the transmission lines. A decentralized state estimator, whose performance is resistant against measurement with anomalies, is designed based on the minimum error entropy with fiducial points(MEEF) criterion. Specifically, 1) An augmented model, which incorporates the local prediction and local measurement, is developed by resorting to the unscented transformation approach and the statistical linearization approach;2) Using the augmented model, an MEEF-based cost function is designed that reflects the local prediction errors of the state and the measurement;and 3) The local estimate is first obtained by minimizing the MEEF-based cost function through a fixed-point iteration and then updated by using the edge measuring information. Finally, simulation experiments with three scenarios are carried out on the IEEE 14-bus system to illustrate the validity of the proposed anomaly-resistant decentralized SE scheme.

Evaluation of Generalized Error Function via Fast-Converging Power Series

A generalized form of the error function, Gp(x)=pΓ(1/p)∫0xe−tpdt, which is directly associated with the gamma function, is evaluated for arbitrary real values of p>1and 0x≤+∞by employing a fast-converging power series expansion developed in resolving the so-called Grandi’s paradox. Comparisons with accurate tabulated values for well-known cases such as the error function are presented using the expansions truncated at various orders.

APPROXIMATE POWER OF HETEROSCEDASTICITY TEST IN NONLINEAR MODELS WITH ARIMA（0,1,0） ERRORS

This paper presents an approach for estimating power of the score test, based on an asymptotic approximation to the power of the score test under contiguous alternatives. The method is applied to the problem of power calculations for the score test of heteroscedasticity in European rabbit data （Ratkowsky, 1983）. Simulation studies are presented which indicate that the asymptotic approximation to the finite-sample situation is good over a wide range of parameter configurations.

A Conceptual Design Study for the Error Field Correction Coil Power Supply in JT-60SA

This paper describes a conceptual design study for the circuit configuration of the Error Field Correction Coil （EFCC） power supply （PS） to maximize the expected performance with reasonable cost in JT-60SA. The EFCC consists of eighteen sector coils installed inside the vacuum vessel, six in the toroidal direction and three in the poloidal direction, each one rated for 30 kA-turn. As a result, star point connection is proposed for each group of six EFCC coils installed cyclically in the toroidal direction for decoupling with poloidal field coils. In addition~ a six phase inverter which is capable of controlling each phase current was chosen as PS topology to ensure higher flexibility of operation with reasonable cost.

Lag compensation errors in active power filters based on DSP controllers

The growing problems of harmonic pollution on coal mine power lines caused by high-power DC drive systems has increased the use of active power filters.We analyzed compensation errors caused by the time lag in the detecting circuits of an active power filter based on DSP control.We derived a mathematical model for the compensation error starting from the error estimation when a single distortion frequency is present.This model was then extended to the case where multiple frequencies are present in the distortion.A formula for a general theory of compensation error with fixed load and fixed lag time is presented.The theoretical analysis and experimental results show that the delay time of an active power filter mainly arises from the sampling time.Lower sampling frequencies introduce larger compensation errors in the active power filter reference current.

Day-Ahead Probabilistic Load Flow Analysis Considering Wind Power Forecast Error Correlation

Short-term power flow analysis has a significant influence on day-ahead generation schedule. This paper proposes a time series model and prediction error distribution model of wind power output. With the consideration of wind speed and wind power output forecast error’s correlation, the probabilistic distributions of transmission line flows during tomorrow’s 96 time intervals are obtained using cumulants combined Gram-Charlier expansion method. The probability density function and cumulative distribution function of transmission lines on each time interval could provide scheduling planners with more accurate and comprehensive information. Simulation in IEEE 39-bus system demonstrates effectiveness of the proposed model and algorithm.

Suppression of laser power error in a miniaturized atomic co-magnetometer based on split ratio optimization

A miniaturized atomic spin-exchange relaxation-free(SERF)co-magnetometer measures angular velocity using a balanced polarimetry technique which is easily affected by the laser power.A laser power closed-loop control system is usually used to suppress the fluctuation of the laser power.Although this method can greatly eliminate the fluctuation of the in-loop laser power(the feedback laser),it cannot fully eliminate the fluctuation of the out-of-loop laser power(the signal measurement laser).This leads to SERF gyroscope laser power error,which reduces the inertial measurement accuracy.In this paper,the influence mechanism of the split ratio(the ratio of the in-loop laser power to the out-of-loop laser power)on the out-of-loop laser power control accuracy is analyzed by establishing a laser power transmission model inside and outside the loop.Moreover,a method is developed to improve the out-of-loop laser power stability by optimizing the split ratio.Comparative experiments showed that the relative Allan standard deviation of the out-of-loop laser power decreased from 5.48×10^(-6)to 2.62×10^(-6)at 100 s,and decreased by an order of magnitude from 1.76×10^(-5)to 3.30×10^(-6)at1000 s.Correspondingly,the rate ramp coefficient in the Allan standard deviation curve of the SERF gyroscope test data decreased from 1.312[(°/h)/h]to 0.246[(°/h)/h].And the bias stability increased from 0.032°/h to 0.019°/h.Therefore,the proposed method can improve the long-term stability of the probe laser power and effectively suppress the laser power error of the SERF gyroscope.

A Simulation Based Comparison of Correlation Coefficients with Regard to Type I Error Rate and Power

In this simulation study, five correlation coefficients, namely, Pearson, Spearman, Kendal Tau, Permutation-based, and Winsorized were compared in terms of Type I error rate and power under different scenarios where the underlying distributions of the variables of interest, sample sizes and correlation patterns were varied. Simulation results showed that the Type I error rate and power of Pearson correlation coefficient were negatively affected by the distribution shapes especially for small sample sizes, which was much more pronounced for Spearman Rank and Kendal Tau correlation coefficients especially when sample sizes were small. In general, Permutation-based and Winsorized correlation coefficients are more robust to distribution shapes and correlation patterns, regardless of sample size. In conclusion, when assumptions of Pearson correlation coefficient are not satisfied, Permutation-based and Winsorized correlation coefficients seem to be better alternatives.

SERF原子耦合磁强计抽运光功率误差分析

基于无自旋交换弛豫(Spin exchange relaxation-free,SERF)的原子自旋耦合磁强计同时具有敏感角速率和抑制环境磁场扰动的能力,是一种很有前途的长期导航应用工具.抽运光功率误差从标度因数和零偏稳定性两个方面限制了SERF耦合磁强计的长期稳定性,目前针对SERF惯性测量的抽运光误差分析均为分析标度因数误差,缺乏对零偏稳定性的分析.为了分析抽运光功率对系统的零偏稳定性影响,本文基于泰勒展开,将K–Rb–^(21)Ne耦合磁强计动力学系统由非线性系统简化为线性时不变系统,并基于状态空间方法推导了抽运光功率频率响应模型,最后在SERF耦合磁强计上对该模型进行了实验验证.理论和实验结果表明,耦合磁强计的磁光非正交将在光功率传递函数中引入微分环节,导致在耦合磁强计工作带宽里,耦合磁强计的输出信号与抽运光功率近似成比例环节.本文为分析SERF耦合磁强计中抽运光功率波动引起的惯性测量误差提供了精确的模型,为后续进行抽运光功率抑制提供了理论支持.

基于LSTM-CGAN的风电场景生成方法

针对传统风电场景生成方法未充分利用风电功率的预测误差,以及未合理考虑风电序列时间相关性的问题,提出了一种基于LSTM-CGAN(Long Short-Term Memory Conditional Generative Adversarial Network)的风电场景生成方法。该方法在条件生成对抗网络模型的训练过程中引入了符合风电预测误差分布的随机噪声,同时使用深度长短期记忆网络搭建条件生成对抗网络的生成器和判别器。算例结果表明,所提方法生成的场景集对风电真实场景的覆盖率能够保持在98%以上,刻画风电的不确定性也不会过于保守,能够更好地学习到风电序列的时间相关性。

Excel和Power Point在物理实验中的应用

物理实验离不开填表、计算和作图,我们利用Excel进行快速填充表格和快速计算;Power Point具有田字型虚线网格线,即坐标纸,且可根据需要调整网格的大小,使得作图快速准确,从而,提高物理实验的质量.

Effect of lens constants optimization on the accuracy of intraocular lens power calculation formulas for highly myopic eyes

AIM: To evaluate the effect of different lens constant optimization methods on the accuracy of intraocular lens(IOL) power calculation formulas for highly myopic eyes.METHODS: This study comprised 108 eyes of 94 consecutive patients with axial length(AL) over 26 mm undergoing phacoemulsification and implantation of a Rayner(Hove, UK) 920H IOL. Formulas were evaluated using the following lens constants: manufacturer’s lens constant, User Group for Laser Interference Biometry(ULIB) constant, and optimized constant for long eyes. Results were compared with Barrett Universal II formula, original Wang-Koch AL adjustment method, and modified Wang-Koch AL adjustment method. The outcomes assessed were mean absolute error(MAE) and percentage of eyes with IOL prediction errors within ±0.25, ±0.50, and ±1.0 diopter(D). The nonparametric method, Friedman test, was used to compare MAE performance among constants.RESULTS: Optimized constants could significantly reduce the MAE of SRK/T, Hoffer Q, and Holladay 1 formulas compared with manufacturer’s lens constant, whereas the percentage of eyes with IOL prediction errors within ±0.25, ±0.50, and ±1.0 D had no statistically significant differences. Optimized lens constant for long eyes alone showed non-significant refractive advantages over the ULIB constant. Barrett Universal II formula and formulas with AL adjustment showed significantly higher accuracy in highly myopic eyes(P<0.001). CONCLUSION: Lens constant optimization for the subset of long eyes reduces the refractive error only to a limited extent for highly myopic eyes.

Low complexity power control approach based on MMSE detection for V-BLAST

A low complexity Per-Antenna Power Control （PAPC） approach based on Minimum Mean Squared Error （MMSE） detection for V-BLAST is proposed in this paper. The PAPC approach is developed for minimizing the Bit Error Rate （BER） averaged over all substreams when the data throughput and the total transmit power keep constant over time. Simulation results show that the Power-controlled V-BLAST （P-BLAST） outperforms the conventional V-BLAST in terms of BER performance with MMSE detector, especially in presence of high spatial correlation between antennas. However, the additional complexity for P-BLAST is not high. When MMSE detector is adopted, the P-BLAST can achieve a comparable BER performance to that of conventional V-BLAST with Maximum Likelihood （ML） detector but with low complexity.

Research on energy storage capacity configuration for PV power plants using uncertainty analysis and its applications

Compensating for photovoltaic(PV)power forecast errors is an important function of energy storage systems.As PV power outputs have strong random fluctuations and uncertainty,it is difficult to satisfy the grid-connection requirements using fixed energy storage capacity configuration methods.In this paper,a method of configuring energy storage capacity is proposed based on the uncertainty of PV power generation.A k-means clustering algorithm is used to classify weather types based on differences in solar irradiance.The power forecast errors in different weather types are analyzed,and an energy storage system is used to compensate for the errors.The kernel density estimation is used to fit the distributions of the daily maximum power and maximum capacity requirements of the energy storage system;the power and capacity of the energy storage unit are calculated at different confidence levels.The optimized energy storage configuration of a PV plant is presented according to the calculated degrees of power and capacity satisfaction.The proposed method was validated using actual operating data from a PV power station.The results indicated that the required energy storage can be significantly reduced while compensating for power forecast errors.

Critical power of keyhole formation in CW Nd:YAG laser deep penetration welding

The energy model was founded to calculate the critical power of keyhole formation by using the limit principle in CW （ continuous wave ） Nd： YAG laser deep penetration welding process. The model was validated by experiments. The results show that ＇.there are two errors between the calculated critical power of keyhole formation and that of experiments ： one is that the calculated results is less than those of experiments, which is caused by not considering the energy loss by heat conduction in the model of keyhole formation. The other is that there is 0. 9 mm error between the axis of the calculated curve of critical power with location of laser focus and that of experimental curve, which is induced by the excursion of laser focus in laser deep penetration welding. At last, the two errors were revised according to the analyses of the errors.

BP-LMS-based BDS-3 power system positioning method

The BeiDou-3 navigation satellite system(BDS-3)provides a full-domain high-precision positioning service for the power system to ensure safe and stable operation.However,BDS-3 power system positioning faces certain challenges,such as complex electromagnetic interference and incomplete error elimination.Herein,a back propagation neural network-improved least mean square(BP-LMS)adaptive filtering method is proposed for the BDS-3 full-domain and high-precision power system positioning,which utilizes the loss function to update the weight of the BP hidden layer,computes the pseudo compensation range,and eliminates the impact of electromagnetic interference to enhance the accuracy of power system positioning.Simulation results confirm the superior performance of BP-LMS in positioning accuracy and error elimination.Compared with LMS and normalized least mean square(NLMS),the filtering error of the proposed BP-LMS adaptive filtering method is decreased by 57.14%and 51.38%,respectively.

Group-Based Successive Interference Cancellation for Multi-Antenna NOMA System with Error Propagation

Non-orthogonal multiple access(NOMA)is viewed as a key technique to improve the spectrum efficiency and solve the issue of massive connectivity.However,for power domain NOMA,the required overall transmit power should be increased rapidly with the increasing number of users in order to ensure that the signal-to-interference-plus-noise ratio reaches a predefined threshold.In addition,since the successive interference cancellation(SIC)is adopted,the error propagation would become more serious as the order of SIC increases.Aiming at minimizing the total transmit power and satisfying each user’s service requirement,this paper proposes a novel framework with group-based SIC for the deep integration between power domain NOMA and multi-antenna technology.Based on the proposed framework,a joint optimization of power control and equalizer design is investigated to minimize transmit power consumption for uplink multi-antenna NOMA system with error propagations.Based on the relationship between the equalizer and the transmit power coefficients,the original problem is transformed to a transmit power optimization problem,which is further addressed by a parallel iteration algorithm.It is shown by simulations that,in terms of the total power consumption,the proposed scheme outperforms the conventional OMA and the existing cluster-based NOMA schemes.

On the Distribution of Type II Errors in Hypothesis Testing

When a statistical test of hypothesis for a population mean is performed, we are faced with the possibility of committing a Type II error by not rejecting the null hypothesis when in fact the population mean has changed. We consider this issue and quantify matters in a manner that differs a bit from what is commonly done. In particular, we define the probability distribution function for Type II errors. We then explore some interesting properties that we have not seen mentioned elsewhere for this probability distribution function. Finally, we discuss several Maple procedures that can be used to perform various calculations using the distribution.