Costs of nuclear accidents Implications for reactor choice

In this article the economic consequences of nuclear accidents are expressed as a surcharge on the capital costs of each new station. An accident at any nuclear station may cause electric power utilities elsewhere to reduce output from the same type of reactor, as happened after Three Mile Island. The size of the accident surcharge therefore increases with the worldwide use of a reactor type. Plausible assumptions for the UK give a surcharge of 7.5% of total capital costs for the pressurized water reactor (PWR), compared to 2.5% for a lesser-used design such as the advanced gas-cooled reactor (AGR). These results are subject to considerable uncertainty but they clearly indicate that the accident surcharge could be of some importance in the choice of reactor for future power stations.     

PMP估算中地形对山地暴雨增幅作用的影响

在山区可能最大降水(PMP)估算中,当确定移置附近气候相似区某场灾害性特大暴雨后,就必须定量估算山地地形对暴雨的增幅作用,以便在移置之前定量消除地形对被移置暴雨的影响。为了定量估算辐合雨分量及其时空分布形态,以福建莆田地区和香港地区两场不同类型的暴雨为例,基于福建东南沿海地区84个站点和香港地区77个站点1、3、6、12、24h的5个时段的历史年最大雨量资料和实测降雨资料,应用"分时段地形增强因子(SDOIF)"暴雨分割技术分割香港20080607和莆田19991009两场暴雨,探讨山区暴雨期间地形对暴雨的定量影响及其空间分布模式。研究发现山区地形对暴雨有着增幅作用,且随历时增加而逐渐增强,越靠近暴雨中心增强幅度也越显著。同时地形增强因子也受高程的影响,低海拔处地形增强因子的增速高于高海拔处。经暴雨分割后的辐合雨空间分布形态与实测降雨的形态也基本一致。可见基于极值暴雨历史系列统计特性分析及暴雨天气背景和地形地貌特征分析相结合的SDOIF暴雨分割技术,能够有效地反映特定地形对暴雨的增幅作用及其空间分布形态,为山区暴雨移置提供了可靠依据。     

Evaluating the measurement uncertainty at hydrogen refueling stations using a Bayesian non-parametric approach

The ability to evaluate measurement error at hydrogen refueling stations plays a vital role in the sustainability of the hydrogen vehicle industry. Most previous work in this application investigates the measurement accuracy of mass flow meters in controlled experiments, using testing equipment. The focus of our work is to estimate the measurement accuracy of fueling using data from hydrogen refueling stations collected under real operation. Accuracy is estimated by comparing the observed mass count readings with reference mass counts calculated using the pressure-volume-temperature method. To quantify the measurement uncertainty, we propose using Dirichlet process mixture models, a class of Bayesian non-parametric methods. The Dirichlet process mixture model approach is tested on five hydrogen refueling stations in real operation. Our results show that the model is able to capture the complex structure of the data and successfully estimate the probability distribution of measurement uncertainty. Our work demonstrates the effectiveness of the Bayesian non-parametric approach for evaluating the measurement uncertainty of hydrogen refueling stations.     

Estimation of consequence and damage caused by an organic hydride hydrogen refueling station

Organic hydride hydrogen refueling stations are currently being developed in Japan. For these stations, we estimate the consequence and damage caused by explosions and heat radiation after a hydrogen leak, and the acute toxicity caused by the leakage and dispersion of methylcyclohexane and toluene energy carriers. First, the organic hydride hydrogen refueling station is defined, and an accident scenario for four leak sizes of hydrogen and chemical leak accidents is set. Next, simulations of the blast wave pressure and heat radiation after the hydrogen leak and of atmospheric dispersion for the evaporation after liquid methylcyclohexane and toluene leaks are performed. Probit functions or threshold values are created for each type of effects caused by the explosion, heat and the inhalation effect on humans of toluene acute toxicity. Population data for the area surrounding the station are created in a 10-m mesh. The consequence and damage are estimated for each leak size. The results show that although the explosion and chemical leak affects the area around the refueling station, the effects are small in all of the accident scenarios. In contrast, although the area of the heat effect is limited to inside the refueling station, the burn damage is large, and there is a need for conducting quantitative risk assessment.     

Risk modelling of a hydrogen gasholder using Fuzzy Bayesian Network (FBN)

Gasholder is one of the principal types of storage for gaseous hydrogen. It plays an important role in the hydrogen production. Nonetheless, hydrogen leakage in gasholders may lead to great hazard and dire consequences such as fire and explosion. Therefore, safety analysis is vital for preventing such potential accidents. Root causes of hydrogen leakage and possible consequences were obtained using Bow-Tie analysis (BT). Then, to relax the limitation of BT in modelling uncertainties and conditional dependency, a Bayesian Network (BN) model for gasholder leakage was established by converting BT to BN (BTBN). In the meantime, in order to cope with the uncertainty of the failure data, the fuzzy logic based on expert judgment was applied. Unlike the traditional FTA, the proposed approach can be used for backward inference (i.e. accident tracing) of systems, which is particularly important to find the most critical causes of accident scenarios Based on the results of the study, the main influencing factors to the hydrogen gasholder leakage were human factor, that is, operation error, inspection not specified, inspection not performed and delay of inspection. The events missle (due to domino), lightning, vehicle collision, downstream compressor failure were the second level critical events in the failure of gasholder.     

基于软层次模型的继电保护风险评估

针对继电保护系统风险评估的不确定性,采用软层次模型评估继电保护风险。首先从风险的定义出发,阐述事故发生概率和损失之间的关系;其次根据负荷等级及减供负荷判断事故类型,根据历史统计数据计算该类型下事故发生的概率,利用停电损失函数计算事故发生后对电力行业及各类用户带来的损失;再采用模糊信息粒化算法处理数据,计算继电保护系统的风险并采用以模糊极大集和模糊极小集为参照基准,以海明距离为测量工具的模糊排序方法,对多个地区的风险进行排序。最终通过实例分析验证了采用软层次模型进行继电保护风险评估的正确性和有效性。研究成果可为风险管理决策提供参考。     

Dynamic risk assessment of hybrid hydrogen-gasoline fueling stations using complex network analysis and time-series data

Since hybrid hydrogen-gasoline fueling stations store two types of hazardous chemicals, the number of victims might be substantially higher than at gas or hydrogen fueling stations during a leakage or explosion. Therefore, it is crucial to conduct an in-depth analysis and risk assessment of hybrid hydrogen-gasoline fueling stations. We establish a time series risk assessment model and use complex network analysis to analyze potential fire and explosion events in hybrid hydrogen-gasoline fueling stations. The complex network model is used to assess the structural characteristics of the complex hybrid hydrogen-gasoline fueling stations, extract the accident causal chain, and explain the relationship between the accident causal factors and the system's risk from a multi-dimensional perspective. Subsequently, time-ordered weighted averaging (TOWA) and time-ordered weighted geometric averaging (TOWGA) operators are incorporated into the complex network model. The TOWA-TOWGA hybrid operator combines the evaluation values of the summer and winter periods to obtain the dynamic risk assessment results. The static and dynamic assessment results are used to determine the degree of influence of the accident causal factors on the system risk in different periods and dimensions. The information is suitable for developing highly targeted measures to prevent/control high-risk disaster events in hybrid hydrogen-gasoline fueling stations.     

基于数据挖掘和遗传小波神经网络的光伏电站发电量预测

为了解决光伏发电预测不确定性问题,进一步提高光伏电站发电量的预测精度。提出一种基于数据挖掘和遗传小波神经网络的光伏电站发电混合预测模型,利用K均值聚类算法对历史数据进行分类,并对传统BP神经网络进行改进。以BP神经网络为基础,引入小波分析构建小波神经网络,同时利用遗传算法对网络的初始参数进行全局寻优得到最优参数,利用交叉熵函数对学习规则进行改进。改进后的网络模型既具有小波分析的良好的局部时域和频域特性,又具有全局搜索能力,可增大跳出局部最优的可能性,同时拥有更快的收敛能力和稳定性。实验结果验证了该算法的有效性。     

一种改进的合成流量法及其在水文预报中的应用

合成流量法对下游站流量作出预报的关键是确定各上游站流量到达下游站的时间。在实际中常采用平均传播时间,然而不同水情下真实传播时间与平均传播时间存在一定的差距,影响预报精度。对此,提出时间窗口概念,以平均传播时间为中心,向前、后各开一个时间窗口,用上游各站时间窗口内流量的线性组合表示其到达下游站的流量,以提高模型对不同水情的适应能力和预报精度。以三峡水库2009~2015年流量数据率定参数,以2016~2018年流量数据作为预报对象,试验结果表明时间窗口法能够显著改善预报精度。     

Li-ion battery SOC estimation method based on the reduced order extended Kalman filtering

The extended Kalman filter (EKF) method for SOC estimation has some problems such as the lack of an accurate model, and model errors due to the variation in the parameters of the model due to the nonlinear behavior of a battery. To solve the aforementioned issues, this paper proposes a reduced order EKF including the measurement noise model and data rejection. In order to do so, the model of a battery in the EKF is simplified into the type of reduced order to decrease the calculation time. Additionally, to compensate the model errors caused by the reduced order model and variation in parameters, a measurement noise model and data rejection are implemented because the model accuracy is critical in the EKF algorithm in order to obtain a good estimation. Finally, the proposed algorithm is verified by short and long term experiments.     

基于证据KNN分类器的蓄电池一致性诊断

通过串并联方式组成的大容量储能电站在长期运行过程中,会不可避免地出现电池单体不一致性的问题。及时识别出有可能存在异常的电池单体,为储能电站评估电池的健康状态提供数据支撑的同时,还可降低储能电站系统性运行风险。该研究提出一种不依赖于电池模型以及经验数据的,基于证据K近邻（KNN）分类器的储能电池一致性诊断方法。该方法利用储能电站一簇电池中的大量单体电池电压与温度运行数据,使用证据KNN分类器构建能表征电池电压、温度一致性的诊断模型,并通过异常反演算法,准确识别出异常的单体电池。     

Near-term location planning of hydrogen refueling stations in Yokohama City

Establishing hydrogen refueling stations is key to transition into a hydrogen economy. To achieve this, a near-term, city-level roll-out plan is required, as Japan is shifting from the demonstration to implementation stage of a hydrogen economy. The aim of this study was to devise a plan to identify near-term locations to build hydrogen refueling stations in Yokohama City, Japan. Our plan provides information on the potential location of hydrogen refueling stations for 2020–2030. We considered mobile and parallel-siting type refueling stations; the locations of these stations were determined by matching the supply and demand estimated from hybrid vehicle ownership data and the available space in existing gas stations based on a safety perspective. The results reaffirmed the importance of planning the locations of hydrogen refueling stations and highlighted the suitability of using mobile-type stations. This was based on the uncertainty in fuel demand for fuel cell vehicles during the implementation stage of the hydrogen economy.     

Proposed model of potential accident process at hydrogen refueling stations based on multi-level variable weight fuzzy Petri net

Hydrogen is becoming more popular as a fuel for vehicles. It is stored and dispensed at hydrogen refueling stations. Once the hydrogen in hydrogen refueling stations leaks, it easily forms a combustible cloud, and can explode by encountering a spark. It is therefore important for the safe and stable operation of hydrogen refueling stations to analyze the evolution of a leakage and explosion accident, clarify the causes and processes of the accident, and prevent the spread of risks. This paper proposes a model using multi-level variable weight fuzzy Petri net. On the basis of hierarchical consideration of the development of the accident, it adds a variable weight factor, which can quantify information in the development of the accident. According to the calculated results, the evolutionary path of risk and the most likely initial cause of the accident are deduced. Finally, taking the leakage and explosion accident of an urban hydrogen refueling station as an example, the usability and effectiveness of the model are verified.     

汽轮机组断油烧瓦典型案例分析

汽轮机润滑油系统是保证汽轮机安全运行的重要系统,润滑油系统故障会造成断油烧瓦事故。断油烧瓦不是新课题,但判明断油烧瓦原因很重要,这涉及制定更合理的监管和预防措施。文章简述了印尼某电厂1号汽轮机停机过程中交流润滑油泵因线路故障停泵,同时润滑油系统未能切换至直流润滑油泵供油所引起的断油烧瓦事故。通过对事故经过的调查,分析了交、直流润滑油泵未正常联锁的原因,找出预防润滑油断油事故的措施。     

基于李雅普诺夫稳定性理论的抽水蓄能机组安全阈值整定方法

随着抽水蓄能电站数量的不断增加,其安全监控与管理就变得极为重要。抽水蓄能机组经常面临工况的急剧转换如突发事件甩负荷等,可能会给运行管理带来安全隐患。为此,基于李雅普诺夫稳定性理论提出了构建安全系统模型的安全阈值整定方法,并利用河北张河湾抽水蓄能电站的实际运行数据,对其进行了动态系统建模,完成了安全模型的构建及安全阈值的整定。将整定的阈值作为停机标准,对抽水蓄能机组实际运行参数进行动态实时监控,保证了机组安全平稳运行。     

Risk analysis on mobile hydrogen refueling stations in Shanghai

To better understand the hazards and risks associated with the mobile hydrogen refueling stations, a risk analysis was preformed to improve the safety of the operation. The risks to the station personnel and to the public were discussed separately. Results show that the stationary risks of the mobile stations to the personnel and refueling customers are lower than the risk acceptance criteria over an order of magnitude, so the occupational risks and the risks to customers are completely acceptable. The third party risks can be acceptable as long as the appropriate mitigation measures, especially well designed parking area and operation time, are implemented. Leak from booster compressors is the main risk contributor to the stationary risks due to the highest failure rates according to the generic data and the worst harm effects based on the consequence evaluations. However, the failure of the tube storages will result in the largest financial loss, though the likelihood of this scenario is much less than that of failure from booster compressors. As for the road risks of the mobile stations, they can be acceptable as long as the appropriate mitigation measures, especially well-planned itinerary and transport time, are implemented.     

2011年中国核电发展状况、未来趋势及政策建议

2011年我国核电行业获得较快发展,核电装机及发电量显著增加,各机组总体运行稳定,核电设备国产化大步前进、装备制造能力及建设安装能力快速提升。我国实验快堆实现并网运行,先进核电技术获得突破。尽管存在日本福岛311核事故影响,我国核电发展暂时减缓。由于我们进行了认真的总结、反思及采取相应措施,总体上有利于我国核电行业的长期健康发展,未来我国核电发展预期前景依然光明。     

中日与俄罗斯油气合作中的竞争态势分析

俄罗斯是全球油气生产大国,与日本的经济互补性强,但双方的能源合作尚处于较低水平,2011年俄对日石油出口仅占日本石油进口总量的4％,在日本进口的LNG中来自俄罗斯的气源仅占9％,两国在油气合作方面应该大有潜力可挖。中日两国一直在寻求通过油气进口多元化保障自身的能源安全,日俄能源合作对中俄能源合作会带来冲击和影响。对于与中日两国的能源合作,俄罗斯所持态度一直比较中立。俄罗斯采取保障自身利益最大化、追求出口多元化的对外能源合作策略,而且其油气出口重心不在亚太地区,再加上日本遏制中国和平崛起的政治意图,这就在客观上决定了中日在与俄能源合作中的竞争不可避免,且具有长期性和艰巨性。日本一直在与俄罗斯能源合作问题上排挤和提防中国,争取与俄罗斯开展长期稳定的油气合作,对中国具有重要战略意义。中国应该从战略高度认识中日两国在与俄罗斯能源合作中的竞争关系,树立竞争意识,力争在与日本的竞争中取得先机。同时要充分利用国际能源行业结构变革的机遇。加快国内天然气价格改革步伐,为中俄能源合作创造良好的经济环境。     

基于机器学习算法与SCADA系统的风电机组变桨系统变频器的故障预警方法研究

变桨系统是风电机组的关键设备,但由于风电机组长期处于复杂的工作环境,导致变桨系统故障成为风电机组故障中最常见的故障之一,而变桨系统变频器故障在变桨系统故障中的占比很高。基于此,提出了一种变桨系统变频器的故障预警方案,分析SCAdA系统数据,将机器学习算法应用于故障预警,并将模型温度残差作为故障预警的指标;然后,针对随机因素干扰模型温度残差的问题,提出采用滑动时间窗残差估计方法分析预警模型的温度残差趋势,依此判断变浆系统变频器的状态,进而评估变桨系统的运行状态;最后,以新疆维吾尔自治区某风电场的运行数据为例,对提出的变桨系统变频器的故障预警方法进行了验证,结果表明,在所建立的模型及滑动时间窗残差估计方法的基础上能及时对变浆系统变频器的潜在故障做出预警,验证了该方案的可行性和有效性。     

Uncertainty assessment of a semi-empirical output voltage model for proton exchange membrane fuel cells

An accurate model of proton exchange membrane fuel cell (PEMFC) is essential for its characterization, performance analysis, and design of optimal control strategies. However, due to various disturbances and measurement noise in practical operation, a PEMFC presents high stochasticity and parameter uncertainty. Therefore, a semi-empirical output voltage model parameter estimation method based on variational Bayes (VB) PEMFC is proposed and combined with the Sobol sensitivity analysis method to analyze the relationship between the parameters of the model to be identified and the effect of the output voltage of the model under different noise and operating conditions. The numerical results show that the VB method is able to quantify the uncertainty of the parameter estimation results and has higher computational accuracy compared with the expectation maximization (EM) method. Compared with the Markov chain Monte Carlo (MCMC) method, the VB method is able to greatly reduce the computational effort and takes less time while satisfying the accuracy. Meanwhile, the sensitivity of the model parameters to be identified to the output voltage of the model under different noise and operating conditions is quantified using the Sobol method, which explains the variation of the posterior probability distribution results obtained using the VB method under different noise and operating conditions.