基于电热耦合模型的宽温域锂离子电池SOC/SOP联合估计

准确的状态估计对于锂离子电池安全可靠运行具有重要意义,但由于非线性强,多参数耦合,实现宽温域多参数联合在线估计难度较大。考虑到温度影响,建立电热耦合模型,采用扩展卡尔曼滤波算法(EKF)在线辨识电池参数,通过电压及温度仿真验证了模型的准确性;然后针对无迹卡尔曼滤波算法(UKF)历史数据利用率低的问题,引入多新息理论(MI)改进UKF,改进后的算法在非电压平台区荷电状态(SOC)估计均方根误差不超过1.2%,相较于改进前误差降低了30%以上,并结合安时积分法设计切换算法,解决了MIUKF算法在磷酸铁锂电池电压平台区无法通过电压反馈修正SOC估计误差的问题,实现了宽温域复杂工况下全区间SOC的准确估计,在不同SOC初始值条件下验证了结合算法的准确性,均方根误差不超过3%,为峰值功率(SOP)估计提供了可靠的SOC值;最后将温度约束引入到SOP估计中,提出多约束条件下的SOP估计方法,结果表明在高温条件下,温度起到关键限制作用,可以防止电池温升过大,减少安全隐患。

含SOP的交直流混合配电网日前优化调度

为实现交直流混合配电网的高效运行,提出一种含智能软开关的交直流配电网优化调度方法。在对应用于交直流配电网的智能软开关工作原理进行阐述的基础上,建立含智能软开关的交直流配电网优化调度模型。通过线性化和凸松弛技术,将所建立的非线性优化模型转化为二阶锥规划模型,并且采用改进的50节点算例分析验证模型的有效性。算例结果表明,基于所建模型得到的智能软开关运行策略能够降低配电网运行损耗及改善电压越限的情况,显著提高混合配电网的经济性。

一种SOP端口不平衡时直流侧电压脉动抑制策略

当柔性多状态开关(SOP)端口电压不平衡时,会引起直流侧电压的波动,给整个系统的稳定性带来很大的影响。根据SOP的数学模型,对波动的原因进行了分析,提出一种基于二阶滑模控制的直流侧电压波动抑制策略;通过引入基于非线性干扰观测器的电容补偿器装置,对参考电压电流进行精确地跟踪。考虑多种端口电压不平衡的条件,在MATLAB/Simulink中搭建SOP仿真模型,仿真结果表明,该方法对直流侧电压波动抑制有明显的效果。

骨科手术机器人相关手术围术期护理SOP手册的临床实践与效果评价

探讨护理标准作业程序(Standard Operating Procedure,SOP) 手册在骨科手术机器人相关手术患者围术期的应用效果。方法 分类、细化和量化骨科手术机器人各相关术式护理关键控制点,设计并构建各术式的护理SOP,汇编成护理SOP手册应用于临床。收集患者的一般人口学资料,比较应用前后各观测指标。结果 骨科手术机器人相关手术护理SOP手册应用后,患者术前术后SAS评分显著低于对照组(P<0.001),且患者康复相关指标,疼痛控制指标明显优于对照组(P<0.001);护理质量及满意度均高于应用前(P<0.001),此外,应用后患者平均住院日显著降低(P<0.001)。结论 SOP手册应用于骨科手术机器人相关手术围术期的优质护理具有高度可操作性,可重复性,适合大范围推广普及,带来良好的经济效益和社会效益。

计及SOP参数优化的柔性配电网风险评估方法

为分析智能软开关(soft open point,SOP)连续调节能力对柔性配电网(flexible distribution network,FDN)风险的影响。首先,实现基于三点估计的FDN风险评估方法;采用三点估计法结合交直流交替迭代法和Gram-Charlier级数展开法进行FDN概率潮流计算,获得节点电压与支路有功功率的概率密度函数,使用越限偏移量结合风险偏好型效用函数构建严重度函数,根据风险评估理论建立并计算风险评估指标。其次,在此基础上,提出一种计及SOP参数优化的FDN风险评估方法;以系统总风险最低为目标,建立计及SOP参数优化的FDN风险评估模型,采用粒子群优化算法结合基于三点估计的FDN风险评估方法对其进行求解,用得到的结果去配置SOP,并对此FDN进行风险评估。以3个IEEE 33节点网络通过三端口SOP互联形成的FDN为例,验证了所提风险评估方法的有效性,分析了SOP连续调节能力以及不同接入位置对FDN风险的影响。

基于SOP的公立医院“一院多区”行政后勤职能部门定岗定编路径研究与实践思考

公立医院优质高效的行政后勤人力资源配置是医院高质量发展的关键环节,科学合理的定岗定编流程具有重要的实践意义。本研究通过梳理定岗定编方法与原则,结合工作实践,研究医院“一院多区”定岗定编标准操作规程(SOP),以期突破行政后勤部门人员管理的瓶颈,为公立医院后勤职能部门定岗定编的实际操作提供参考。通过近年来的操作实践,认为公立医院“一院多区”行政后勤职能部门定岗定编工作应建立标准化的操作流程,加强信息化建设,通过多院区联动,提升资源统筹与利用效率,实践中持续反馈与改进,从而动态提升管理水平,提升人力资源配置效率。

The Probability Density Function Related to Shallow Cumulus Entrainment Rate and Its Influencing Factors in a Large-Eddy Simulation

The process of entrainment-mixing between cumulus clouds and the ambient air is important for the development of cumulus clouds.Accurately obtaining the entrainment rate(λ)is particularly important for its parameterization within the overall cumulus parameterization scheme.In this study,an improved bulk-plume method is proposed by solving the equations of two conserved variables simultaneously to calculateλof cumulus clouds in a large-eddy simulation.The results demonstrate that the improved bulk-plume method is more reliable than the traditional bulk-plume method,becauseλ,as calculated from the improved method,falls within the range ofλvalues obtained from the traditional method using different conserved variables.The probability density functions ofλfor all data,different times,and different heights can be well-fitted by a log-normal distribution,which supports the assumed stochastic entrainment process in previous studies.Further analysis demonstrate that the relationship betweenλand the vertical velocity is better than other thermodynamic/dynamical properties;thus,the vertical velocity is recommended as the primary influencing factor for the parameterization ofλin the future.The results of this study enhance the theoretical understanding ofλand its influencing factors and shed new light on the development ofλparameterization.

Time series prediction of reservoir bank landslide failure probability considering the spatial variability of soil properties

Historically,landslides have been the primary type of geological disaster worldwide.Generally,the stability of reservoir banks is primarily affected by rainfall and reservoir water level fluctuations.Moreover,the stability of reservoir banks changes with the long-term dynamics of external disastercausing factors.Thus,assessing the time-varying reliability of reservoir landslides remains a challenge.In this paper,a machine learning(ML)based approach is proposed to analyze the long-term reliability of reservoir bank landslides in spatially variable soils through time series prediction.This study systematically investigated the prediction performances of three ML algorithms,i.e.multilayer perceptron(MLP),convolutional neural network(CNN),and long short-term memory(LSTM).Additionally,the effects of the data quantity and data ratio on the predictive power of deep learning models are considered.The results show that all three ML models can accurately depict the changes in the time-varying failure probability of reservoir landslides.The CNN model outperforms both the MLP and LSTM models in predicting the failure probability.Furthermore,selecting the right data ratio can improve the prediction accuracy of the failure probability obtained by ML models.

Synergetic optimization operation method for distribution network based on SOP and PV

The integration of distributed generation brings in new challenges for the operation of distribution networks,including out-of-limit voltage and power flow control.Soft open points(SOP)are new power electronic devices that can flexibly control active and reactive power flows.With the exception of active power output,photovoltaic(PV)devices can provide reactive power compensation through an inverter.Thus,a synergetic optimization operation method for SOP and PV in a distribution network is proposed.A synergetic optimization model was developed.The voltage deviation,network loss,and ratio of photovoltaic abandonment were selected as the objective functions.The PV model was improved by considering the three reactive power output modes of the PV inverter.Both the load fluctuation and loss of the SOP were considered.Three multi-objective optimization algorithms were used,and a compromise optimal solution was calculated.Case studies were conducted using an IEEE 33-node system.The simulation results indicated that the SOP and PVs complemented each other in terms of active power transmission and reactive power compensation.Synergetic optimization improves power control capability and flexibility,providing better power quality and PV consumption rate.

Effectiveness of antibiotic prophylaxis for acute esophageal variceal bleeding in patients with band ligation: A large observational study

BACKGROUND Esophageal variceal bleeding is a severe complication associated with liver cirrhosis and typically necessitates endoscopic hemostasis.The current standard treatment is endoscopic variceal ligation(EVL),and Western guidelines recom-mend antibiotic prophylaxis following hemostasis.However,given the impro-vements in prognosis for variceal bleeding due to advancements in the management of bleeding and treatments of liver cirrhosis and the global concerns regarding the emergence of multidrug-resistant bacteria,there is a need to reassess the use of routine antibiotic prophylaxis after hemostasis.AIM To evaluate the effectiveness of antibiotic prophylaxis in patients treated for EVL.METHODS We conducted a 13-year observational study using the Tokushukai medical database across 46 hospitals.Patients were divided into the prophylaxis group(received antibiotics on admission or the next day)and the non-prophylaxis group(did not receive antibiotics within one day of admission).The primary outcome was composed of 6-wk mortality,4-wk rebleeding,and 4-wk spontaneous bacterial peritonitis(SBP).The secondary outcomes were each individual result and in-hospital mortality.A logistic regression with inverse probability of treatment weighting was used.A subgroup analysis was conducted based on the Child-Pugh classification to determine its influence on the primary outcome measures,while sensitivity analyses for antibiotic type and duration were also performed.RESULTS Among 980 patients,790 were included(prophylaxis:232,non-prophylaxis:558).Most patients were males under the age of 65 years with a median Child-Pugh score of 8.The composite primary outcomes occurred in 11.2%of patients in the prophylaxis group and 9.5%in the non-prophylaxis group.No significant differences in outcomes were observed between the groups(adjusted odds ratio,1.11;95%confidence interval,0.61-1.99;P=0.74).Individual outcomes such as 6-wk mortality,4-wk rebleeding,4-wk onset of SBP,and in-hospital mortality were not significantly different between the groups.The primary outcome did not differ between the Child-Pugh subgroups.Similar results were observed in the sensitivity analyses.CONCLUSION No significant benefit to antibiotic prophylaxis for esophageal variceal bleeding treated with EVL was detected in this study.Global reassessment of routine antibiotic prophylaxis is imperative.

基于DR和SOP协调的配电网分布式光伏接纳能力提升方法研究

为了有效解决新能源光伏(PV)接入配电网后所带来的电压越线、线路过载以及配电网潮流双向流动等问题,提出基于储能的需求响应(DR)和智能软开关(SOP)协调的配电网优化方法。先构建基于储能的DR和SOP的配电网优化模型,并以配电网中接入新能源PV功率最大、SOP自身损耗最小、储能(ESS)运维成本最小等为优化目标函数,构建PV接入配电网后的多时段优化模型;然后采用二阶锥松弛技术将构建的配电网不同时段的优化模型转换成混合整数二阶锥规划(MISOCP)问题,并通过MATLAB中MOSEK算法包对转换后的模型进行求解;最后采用IEEE33节点系统进行算例及仿真验证,并对不同控制策略下的仿真结果进行对比及分析。算例及仿真结果表明:基于储能的DR和SOP共同作用的配电网优化方法能实现对负荷峰谷差的平抑和提升配电网的电压质量,并在保证配电网系统安全性的前提下能有效提升新能源PV接入后配电网的接纳能力。

Probability Distribution Characteristics of Strong Nonlinear Waves Under Typhoon Conditions in the Northern South China Sea

The generation and propagation mechanism of strong nonlinear waves in the South China Sea is an essential research area. In this study, the third-generation wave model WAVEWATCH Ⅲ is employed to simulate wave fields under extreme sea states. The model, integrating the ST6 source term, is validated against observed data, demonstrating its credibility. The spatial distribution of the occurrence probability of strong nonlinear waves during typhoons is shown, and the waves in the straits and the northeastern part of the South China Sea show strong nonlinear characteristics. The high-order spectral model HOS-ocean is employed to simulate the random wave surface series beneath five different platform areas. The waves during the typhoon exhibit strong nonlinear characteristics, and freak waves exist. The space-varying probability model is established to describe the short-term probability distribution of nonlinear wave series. The exceedance probability distributions of the wave surface beneath different platform areas are compared and analyzed. The results show that with an increase in the platform area, the probability of a strong nonlinear wave beneath the platform increases.

计及SOP损耗的交直流配电网三相区间状态估计

随着分布式电源及智能软开关(SOP)等新型设备投入配电网运行,需要对交直流配电网进行精确的状态估计。首先构建了交直流配电网的区间状态估计模型,考虑三相不对称性与交直流之间的耦合;其次,为计及SOP的损耗,增设SOP两侧支路电流作为状态变量,完善了区间状态估计中的雅克比矩阵;最后采用基于迭代运算的线性规划方法进行区间状态估计。在修改IEEE33节点算例上进行仿真,结果表明所提方法能保证计及SOP损耗的状态估计结果的准确性。

SOP8功率MOSFET结壳热阻与封装可靠性研究

为研究SOP8双MOS芯片结壳热阻与封装可靠性,建立了封装芯片模型。运用有限元软件通过构建热-结构模块仿真了在EME-E115与CEL-1702HF两种塑封材料下的芯片结壳热阻情况,分析了热量在封装芯片内部的主要传递路径。对比分析了两种塑封仿真下塑封料外壳体、MOSFET、引线框架的变形与应力情况,研究了粘接层厚度变化对MOSFET最大等效应力的影响。研究结果表明,在SOP8双MOS芯片的内部,热量主要是沿着引线框架基板向塑封料底部进行传递。粘接焊料增厚50μm,MOSFET结温增幅未超过0.1℃,结温点到塑封料底面中心的热阻升高约1.3℃·W^(-1)。相比于EME-E115塑封料,使用CEL-1702HF塑封料进行封装仿真时,可使功率MOSFET的结壳热阻降低约20%,且芯片封装体在变形、应力方面均具有明显优势。增大粘接焊料的厚度可以有效减小MOSFET的应力。EME-E115与CEL-1702HF塑封下的MOSFEF最大等效应力在粘接焊料厚度分别超过40μm和50μm后均出现了返升的仿真结果。

电动汽车锂电池SOC与SOP在线联合监测设计

考虑到电池荷电状态SOC和峰值功率SOP是影响电动汽车动力源锂电池的重要参数,设计了一款锂电池SOC与SOP在线联合监测器。系统以STC12C5A60S2单片机作为主控制器,由电压、电流采集模块完成电动汽车电池工作状态信息的采集,经主控制器的分析与处理,完成电池荷电状态SOC、峰值功率SOP的在线估算,通过蓝牙技术、OLED显示模块及手机App模块的设计与应用,最终实现电池SOC与SOP的便携式在线联合监测功能。测试结果表明,该系统运行稳定,估算准确,为电动汽车锂电池的应用和推广提供了市场参考价值。

SOP制酸装置降低排放尾气中NO_(x)含量的研究

简述了SOP制酸装置工艺流程和运行参数,阐述了燃烧过程中NO_(x)的生成机理及为降低NO_(x)含量所采取的措施,可作为SOP制酸装置控制NO_(x)排放的参考。

Probability Laws Derived from the Gamma Function

Several densities or probability laws of continuous random variables derive from the Euler Gamma function. These laws form the basis of sampling theory, namely hypothesis testing and estimation. Namely the gamma, beta, and Student law, through the chi-square law and the normal law are all distributions resulting from applications of Euleur functions.

基于SOP拓扑结构的配电网系统故障定位方法

针对传统配电网系统中故障定位方法存在故障定位信息回馈速度较慢的问题,研究了基于SOP拓扑结构的改进配电网系统,减少了系统因自身原因导致的故障。基于数据型乘法器加强了系统全局的故障检出定位的能力,同时增强硬件的抗干扰能力。研究基于MTG定位算法,可以通过对于故障产生原因权重的判断,提高对于定位的精确度。通过仿真试验表明,所设计的硬件抗干扰电路在干扰数据量为10G时抗干扰率能达到97.25%,具有很好的抗干扰能力。

On the Application of Mixed Models of Probability and Convex Set for Time-Variant Reliability Analysis

In time-variant reliability problems,there are a lot of uncertain variables from different sources.Therefore,it is important to consider these uncertainties in engineering.In addition,time-variant reliability problems typically involve a complexmultilevel nested optimization problem,which can result in an enormous amount of computation.To this end,this paper studies the time-variant reliability evaluation of structures with stochastic and bounded uncertainties using a mixed probability and convex set model.In this method,the stochastic process of a limit-state function with mixed uncertain parameters is first discretized and then converted into a timeindependent reliability problem.Further,to solve the double nested optimization problem in hybrid reliability calculation,an efficient iterative scheme is designed in standard uncertainty space to determine the most probable point(MPP).The limit state function is linearized at these points,and an innovative random variable is defined to solve the equivalent static reliability analysis model.The effectiveness of the proposed method is verified by two benchmark numerical examples and a practical engineering problem.

Adaptive Update Distribution Estimation under Probability Byzantine Attack

The secure and normal operation of distributed networks is crucial for accurate parameter estimation.However,distributed networks are frequently susceptible to Byzantine attacks.Considering real-life scenarios,this paper investigates a probability Byzantine(PB)attack,utilizing a Bernoulli distribution to simulate the attack probability.Historically,additional detection mechanisms are used to mitigate such attacks,leading to increased energy consumption and burdens on distributed nodes,consequently diminishing operational efficiency.Differing from these approaches,an adaptive updating distributed estimation algorithm is proposed to mitigate the impact of PB attacks.In the proposed algorithm,a penalty strategy is initially incorporated during data updates to weaken the influence of the attack.Subsequently,an adaptive fusion weight is employed during data fusion to merge the estimations.Additionally,the reason why this penalty term weakens the attack has been analyzed,and the performance of the proposed algorithm is validated through simulation experiments.