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proportion of wind power is increasing.In the current research,aiming at the problem that the forecasting model is outdated due to the continuous updating of wind power data,a short-term wind power forecasting algorithm based on Incremental Learning-Bagging Deep Hybrid Kernel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.

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.

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.

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.

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.

基于误差幅空特性分析的空间负荷预测误差评价方法

对空间负荷预测误差进行有效评价是客观认识预测结果,指导预测结果合理应用的前提。然而,现有空间负荷预测误差评价的研究存在对误差的空间分布不考虑或考虑不充分导致评价不准确的问题。为此,提出一种基于误差幅空特性分析的空间负荷预测误差评价方法。首先,从空间负荷预测误差幅值大小和空间分布对电网规划影响的角度出发,对误差的幅空特性进行详细分析;其次,利用运输问题的数学模型来表征正负误差的幅空抵消特性,使用各空间邻近度–幅值误差值曲线与x轴围成面积之和来表征剩余未抵消误差的幅空叠加特性;然后,分别通过伏格尔法和各梯形面积累加公式来计算正负误差的幅空抵消影响值和剩余未抵消误差的幅空叠加影响值,并在此基础上构建空间负荷预测误差评价指标;最后,基于误差对电网规划的实际影响给出对误差评价指标性能的检验方法。算例分析表明,与传统方法相比,该文所提误差评价方法从幅值和空间两个维度实现了对空间负荷预测误差更为全面的评估,与误差对电网规划影响的实际情况贴近度更高。

考虑风电出力不确定性的多源联合系统双层优化调度

针对含风-火-储的多源联合系统,风电出力具有不确定性的特点,风机在特定时间段内的预测功率与实际功率之间存在误差,当风机实际出力无法满足调度计划中安排的功率时会导致系统经济效益大幅下降。为此,文中提出了考虑风电预测误差和需求侧响应的双层优化策略,上层模型以风电、火电和可平移负荷总运行成本最少为目标,采用改进粒子群算法(Improved Particle Swarm Algorithm, IPSO)制定火电和可平移负荷的最优调度策略,然后通过Gibbs法对风机最大出力预测误差的概率密度函数进行抽样获取一定量的样本,得到各样本上层电源的功率缺额;下层模型以储能和可中断负荷总运行成本最少为目标,采用线性规划方法对冲上层电源功率缺额,进而制定下层模型电源调度策略。在大量抽样样本背景下,通过对比各样本总成本函数值的期望和方差验证了所提双层优化策略的经济性和有效性。

Microwave-Assisted Transition Metal Nanostructure Synthesis: Power-Law Signature Verification

A power-law (y = cxn) signature between process energy budget (kJ) and process energy density (kJ·ml-1) of microwave-assisted synthesis of silver and gold nanostructures has been recently described [Law and Denis. AJAC, 14(4), 149-174, (2023)]. This study explores this relation further for palladium, platinum, and zinc oxide nanostructures. Parametric cluster analysis and statistical analysis is used to test the power-law signature of over four orders of magnitude as a function of six microwave applicator-types metal precursor, non-Green Chemistry synthesis and claimed Green Chemistry. It is found that for the claimed Green Chemistry, process energy budget ranges from 0.291 to 900 kJ, with a residual error ranging between −33 to +25.9 kJ·ml-1. The non-Green Chemistry synthesis has a higher process energy budget range from 3.2 kJ to 3.3 MJ, with a residual error of −33.3 to +245.3 kJ·ml-1. It is also found that the energy profile over time produced by software controlled digestion applicators is poorly reported which leads to residual error problematic outliers that produce possible phase-transition in the power-law signature. The original Au and Ag database and new Pd, Pt and ZnO database (with and without problematic outliers) yield a global microwave-assisted synthesis power-law signature constants of c = 0.7172 ± 0.3214 kJ·ml-1 at x-axes = 0.001 kJ, and the exponent, n = 0.791 ± 0.055. The information in this study is aimed to understand variations in historical microwave-assisted synthesis processes, and develop new scale-out synthesis through process intensification.

基于最大均值差异的能量侧信道泄露量化评估

能量侧信道分析是通过对密码设备运行时的能量消耗进行分析,推导出运行时的操作及操作涉及的敏感中间值.对密码设备进行能量泄露量化评估是分析密码设备信息泄露程度的重要手段,目前主流的评估方案主要关注于能量迹上单个样本点的泄露,并未充分考虑高阶攻击模型下的泄露评估问题,对于采用掩码防御措施的密码芯片来说,一旦发生泄露,通常表现为多变量联合泄露,因此采用传统的单样本点方法进行泄露评估会存在假阴性的问题.本文研究多点联合泄露评估问题,引入最大均值差异方法,提取能量迹的多变量联合特征,构建基于最大均值差异的能量泄露量化评估模型,提供了一种有效的能量侧信道泄露量化评估方法.通过实现无防御对策和有防御对策的AES算法,使用DPA contest v2、ASCAD v1和自采能量迹数据集进行实验,结果表明,基于最大均值差异的泄露量化评估方法能够有效降低单样本点检测方法出现假阴性的风险,HAC、MTD和Bartlett-F检验的对照结果也进一步验证了该方法的有效性.

考虑光伏随机性的交直流混合配电网鲁棒机会约束安全域模型

为实现高比例光伏接入下交直流混合配电网的安全评估,构建了交直流混合配电网的鲁棒机会约束安全域模型。首先,提出一种基于区间划分的自适应带宽核密度估计方法,以获取光伏预测误差概率分布,构建光伏出力机会约束不确定集合;其次,采用功率与节点电压幅值比值为状态变量,在已有交流线性化潮流模型的基础上推导一种交直流混合配电网线性化潮流模型;然后,提出交直流混合配电网鲁棒机会约束安全域的概念和模型,结合列与约束生成算法和径向迭代搜索算法生成可观测的鲁棒机会约束安全域空间。最后,算例分析验证了所提模型能够在鲁棒机会约束安全域上准确、直观的反映光伏出力的随机性,进而为交直流混合配电网的准确安全评估提供支撑。

广义频分复用系统峰均功率比抑制算法

为了降低广义频分复用系统存在着多载波系统中固有的高峰均功率比问题,本文提出了一种适用于广义频分复用系统的子载波抑制高峰均功率比算法。该算法以限幅法为基础,降低了系统的高峰均功率比,但与限幅法不同的是,该算法几乎不会对带外性能及误码率性能造成影响。由于子载波抑制高峰均功率比算法的峰均功率比抑制性能略差于限幅法,因此本文进一步提出将部分序列传输与子载波抑制高峰均功率比结合来进一步抑制系统高峰均功率比的算法。仿真表明:本文提出的算法的高峰均功率比抑制性能与限幅法相同,且不会对系统的带外与误码率性能造成影响。

计及误差信息的自适应超短期风速预测模型

为提升超短期风速预测精度,提出一种计及误差信息的自适应混合预测模型。应用自适应噪声的完备集合经验模态分解模型与鲸鱼优化的变分模态分解模型分别对风速样本数据与预测误差进行分解,同时计算各子序列的模糊熵以判断序列复杂程度。在此基础上,应用鲸鱼优化的长短期网络预测复杂程度较高的序列,差分自回归移动平均模型预测复杂程度较低的序列。最后,将初始风速预测结果和风速误差预测值相加得到基于误差修正的超短期风速预测值。结果表明,修正预测误差与考虑分解策略可有效提升点预测的性能,与基准模型相比,所提模型在多场景下均具备优良的预测精度。

基于Vine Copula的梯级水库短期发电调度风险估计

基于能准确描述高维变量相关关系的Vine Copula,考虑短期径流预报误差的空间相关性,构建了梯级水库短期发电调度风险估计模型,并将模型应用于长江上游溪洛渡、向家坝和三峡水库,分析了径流预报误差带来的单一水库、梯级水库短期发电调度风险。结果表明:基于C-vine Copula构建的联合分布能较好地描述屏山站、朱沱站、寸滩站和武隆站的日径流预报误差特性;随着水库可调节安全区间范围增大,单一水库发电量不足风险率、弃水风险率均越来越小,梯级水库发电量不足、弃水联合风险率和同现风险率越来越小,即水库调节库容越大,其承担的风险也就越小。

适用多功率的最近电平调制下MMC子模块开路故障诊断策略

模块化多电平换流器(MMC)子模块(SM)发生故障时,快速检测到故障并定位故障SM是提升MMC可靠性的关键。现有研究基于经验的阈值设置在不同运行功率间难以推广。针对于此,提出一种适用于不同运行功率的最近电平调制(NLM)策略下MMC子模块开路故障诊断策略。所提策略通过判断SM电容电压预测值与实际值间的绝对误差是否超出阈值来检测并定位故障。通过数学推导给出了设置阈值的可靠依据,并验证了当运行功率发生改变时无需重新手动设置阈值,与现有研究相比降低了阈值设置难度。此外,所提策略整合了现有策略的优点,包括无需额外硬件,计算负担小,诊断速度快(<20ms),适用于多个SM发生故障的情形等。同时,通过理论分析验证了所提策略不仅适用于NLM策略的情形,在调整SM开关函数的预测方法后可推广至其他MMC调制策略。在硬件在环平台中的实验结果验证了该策略可以在不同功率点准确、快速地诊断出两种MMC子模块开路故障。