大坝安全监控专家系统的知识管理机制

介绍了大坝安全监控专家系统的知识构成及表示,研究及实现了产生式规则和框架表示法在关系数据库中的存储与管理。这种数据库与知识库结合的结构一定程度上解决了知识库的建立与管理困难及知识获取“瓶颈”等问题。同时,本文还设计了适合大坝安全监控专家系统知识特点的知识编辑器。     

农村饮水安全评价专家系统方法及其应用

该文通过农村饮水安全评价的重要性、饮水安全评价的现状分析,引入农村饮水安全评价专家系统中的基于关系数据库的产生式规则知识表示方法、知识库与推理机的设计等关键技术,构建农村饮水安全评价专家系统方法,为饮水安全评价方法提供了新思路。     

开关控制逻辑在操作票自动生成系统中的应用

操作票就是针对特定操作任务，在满足操作规则基础上进行相关电气操作的操作序列 集，因此操作规则知识表示的好坏直接关系到系统的生命力。文中将开关控制逻辑理论用于 操作知识表示，并在此基础上开发了学习机，解决了知识获取问题。实践证明：用开关控制 逻辑表达操作规则能很好解决系统的通用性和用户可维护性。     

基于数据库的大坝安全监控专家系统知识库设计

知识库是整个专家系统的灵魂,知识库中知识的表达、组织与存储模式将影响推理机的推理效率,同时也会影响知识的更新与丰富,最终影响整个专家系统的智能水平。本文着重论述了用关系数据库来设计大坝安全监控专家系统知识库的方法,使知识库具有较好的知识扩展性和合理性,使专家系统的智能水平可以得到不断的提升。同时,利用关系数据库实体间的关系,很容易维护和使用存储在其间的知识,使整个专家系统具有较高效的推理效率。     

基于专家系统的城市电网规划进程智能化控制系统

城市电网规划是一项复杂的系统工程，一项完整的规划工作可能需要数十个步骤。为能更好地完成规划工作，结合关系数据库的特定存储机制和专家系统的规则推理与案例推理方法，开发了基于专家系统的城市电网规划进程智能化控制系统，对规划进程和用户工作状态进行智能化管理，从而显著提高了城市电网规划的效率。     

基于框架式规则的水轮机组故障诊断专家系统研究

为提高专家系统大容量知识的存储、组织、管理和使用效率，提出了框架式诊断规则的概念；以VC＋＋为开发工具和基于ADO数据库访问技术的诊断推理方法，建立了水轮机组故障诊断专家系统，工程应用实例表明：该系统具有构造简单，诊断效率高的优点。     

基于关系数据库的土石坝病险智能诊断专家系统设计

土石坝病险智能诊断专家系统的研制和应用对于土石坝的安全运行意义重大。文中介绍了利用ActiveX数据对象（ADO）数据库访问技术，结合关系数据库与规则库，实现基于关系数据库的土石坝病险智能诊断专家系统的设计方法。     

关系数据库中的查询优化

介绍了关系数据库查询原理分析及其查询的特点和优化，在此基础上，讨论了关系数据库中查询优化具体实现的途径。     

基于规则与人工神经网络的大坝安全监控混合知识库研究

详细介绍了大坝安全监控专家系统知识库应包含的知识内容，对知识库中的各类知识进行了系统的分析与分类。介绍了当前专家系统知识库的构造方法及知识表示特点，根据各类知识的描述特点，研究了采用规则和人工神经网络混合表示大坝安全评判知识的方法及知识库的物理结构，讨论了基于混合知识库的推理策略，分析了组建混合知识库应注意的若干技术问题。     

人工巡视信息处理在土坝安全评价中的应用

介绍了大坝安全监测中人工巡视的内容，研究了人工巡视信息的表达和组织形式，并实现了其在关系数据库中的存储。同时，在利用BP神经网络对大坝的运行状态进行综合推理时，把人工巡视作为一个成因因素加以考虑，使得推理结果更加趋于合理和客观。     

基于关联规则挖掘的径流长期预报模型研究

关联规则是一种重要的数据挖掘技术。现结合水文长期预报的特殊性,将关联规则挖掘分析方法应用于径流长期预报中。根据预报目标初选出预报因子,构成长期预报事务数据集。然后将其进行离散化处理,对离散化后的数据集进行关联规则分析,挖掘出满足事先设定的最小支持度和最小置信度的强关联规则,解释规则并建立模型。以嫩江江桥站汛期径流长期预报为例,挖掘出满足要求的强关联规则,这些强关联规则中蕴含着北太平洋海温变化和江桥汛期径流的关系,说明了关联规则挖掘分析方法在径流长期预报中的可行性。     

Rule-based fuzzy inference system for estimating the influent COD/N ratio and ammonia load to a sequencing batch reactor.

A fuzzy inference system using sensor measurements was developed to estimate the influent COD/N ratio and ammonia load. The sensors measured ORP, DO and pH. The sensor profiles had a close relationship with the influent COD/N ratio and ammonia load. To confirm this operational knowledge for constructing a rule set, a correlation analysis was conducted. The results showed that a rule generation method based only on operational knowledge did not generate a sufficiently accurate relationship between sensor measurements and target variables. To compensate for this defect, a decision tree algorithm was used as a standardized method for rule generation. Given a set of inputs, this algorithm was used to determine the output variables. However, the generated rules could not estimate the continuous influent COD/N ratio and ammonia load. Fuzzified rules and the fuzzy inference system were developed to overcome this problem. The fuzzy inference system estimated the influent COD/N ratio and ammonia load quite well. When these results were compared to the results from a predictive polynomial neural network model, the fuzzy inference system was more stable.     

Predictive Temporal Data-Mining Approach for Evolving Knowledge Based Reservoir Operation Rules

The persistent problem in reservoir operation is that the derived optimal releases fail to incorporate the decision maker or reservoir operators’ knowledge into reservoir operation models. The reservoir operators’ knowledge is specific to that particular reservoir and incorporating such an experienced knowledge will help to derive field reality based operation rules. The available historical reservoir operation databases are the representative samples of reservoir operators’ knowledge or experience. Thus, an attempt has been made that deals with the development of a methodological framework to recover or explore the historical reservoir operation database to derive the reservoir operators’ knowledge as operational rules. The developed methodological framework utilizes the strength and capability of recently developed predictive datamining algorithms to recover the knowledge from large historical database. Predictive data-mining algorithms such as a) classifier: Artificial Neural Network (ANN), and b) regression: Support Vector Regression (SVR) have been used for single reservoir operation data-mining (SROD) modelling framework to explore the temporal dependence between different variables of reservoir operation. The rules of operation or knowledge learned from the training database have been used as guiding rules for predicting the future reservoir operators’ decision on operating the reservoir for the given condition on the inflow, initial storage, and demand requirements. The developed SROD model was found to be efficient in exploring the hidden relationships that exist in a single reservoir system.     

基于PID参数自整定的河工模型尾门控制

设计了尾门自动控制系统,运用水位仪实时采集尾门水位数据,反馈调节电机转速驱动翻板门,实现水位控制。采用PID模型作为电机转速控制器,测试了固定PID参数条件下,系统在平原河流和山区河流河工模型中水位控制效果,山区河流水位最大控制偏差达到1.3 mm,无法满足规范要求。针对该问题运用产生式学习法总结了尾门控制中PID参数调节规则,构建了以水位跟踪偏差、水位涨落速度、涨落预期及跟踪相位为关键要素的知识库,确立了12条控制策略。尾门控制时根据实测数据与知识库规则自主选择对应控制策略,实现PID参数的自整定。工程实践表明,基于PID参数自整定的尾门控制系统水位控制最大偏差为0.5 mm。     

破产理论在区域水量分配中的应用

介绍了破产理论在区域水量分配问题中的应用。分析了传统破产理论在水量分配问题中的有效性和局限性,考虑配水单位水资源丰富程度、用水效率和最低供水保障率3个因素,对破产理论传统的分配规则进行优化改进,并实例应用于广东省东江流域各地市的水资源竞争性分配中。结果表明:(1)改进后的分配规则能综合考虑各分水单位水资源丰富程度和用水效率等各方面因素,合理排列分水单位的配水优先等级,确保高优先级单位水量供给,同时保证低优先级单位的最低供水标准;(2)面对未来不确定性来水条件,当可供水总量逐渐减少时,不同配水优先级单位具有不同的反应速率,高配水优先等级的配水单位其供水满足率的下降速率更趋缓慢,进一步体现了其优先配水地位。改进后的破产理论规则应用于区域水量分配中能够得到合理的分配结果,而且能根据问题的不同要求进行规则调整,极具灵活性,可以推广应用于其他地区。     

Use of bankruptcy methods for resolving interprovincial water conflicts over transboundary river: Case study of Indus River in Pakistan

Sustainable transboundary water governance is often challenged by conflicts between agents, which necessitates the design of cooperative and self‐enforcing alternatives to facilitate equitable water distribution. A pervasive and critical problem related to many transboundary rivers is that the total allocation or demand of riparian states is usually much more than that of the total available water. This problem is a major cause of disputes, both nationally and internationally. A key challenge concerns how to allocate the available water among riparian states with competing and often conflicting needs under an uncertain supply–demand gap. To address this pervasive allocation problem related to transboundary rivers, the bankruptcy method is used. The bankruptcy method distributes water among riparian states when their total demand exceeds the total available water. We investigate the utility of this method in the Indus River – a river that is shared among the four provinces of Pakistan, namely, Punjab, Sindh, Baluchistan and Khyber Pakhtunkhwa (KPK) – using five commonly used bankruptcy rules and the Shapely value. Two new bankruptcy rules namely the “groundwater‐based rule” and “the proposed rule” are also proposed to address the usage of groundwater: the land affected by salinity and the gross domestic product (GDP) of each province. Additionally, this paper introduces a new method to compare and contrast the bankruptcy rules, the Shapely value and the two proposed rules. The findings suggest that the groundwater‐based rule has the lowest dispersion and is the preferred method for water allocation in the Indus River Basin. The use of the bankruptcy rules, the Shapely value and the two proposed methods has the potential to address the supply–demand mismatches of shared rivers. The proposed framework for selecting the best rule is recommended as an effective tool to facilitate negotiation over practical water allocation within transboundary river basins.     

耦合出力破坏深度与弃电准则的梯级水风光互补调度规则研究

梯级水电调度规则是指导控制型水库发电蓄放水的重要依据。随机、波动、间歇性新能源的接入改变了梯级水电运行边界，增加了缺电、弃电风险，导致仅考虑径流季节性波动的调度规则不再适用。依托贵州某梯级水风光综合基地实际工程，剖析了导致缺电、弃电的因素，并从规则形式和模型构建角度给出解决方案。在标准调度规则的基础上，结合对冲规则、满蓄规则，并考虑输电通道容量限制与新能源消纳规则，提出梯级水风光六段式互补调度规则，以减少缺电、弃电;提出出力破坏深度指标度量缺电风险、提出弃电准则避免非必要弃电，并以规则参数为决策变量构建多目标参数模拟优化模型;采用目标优先级非支配排序遗传算法优化规则参数。设置多种新能源装机容量场景、输电通道容量场景和对比规则，从多角度验证本文方法的有效性。结果表明，本文方法能够显著降低梯级水风光综合基地缺电、弃电风险，提高发电量，其中出力破坏深度指标使缺电程度平均降低36.04%、弃电准则使弃电平均减少7.96%。最后，绘制更加简明、直观的梯级水风光互补调度图，以便于实际应用。     

Improving the Summer Power Generation of a Hydropower Reservoir Using the Modified Multi-Step Ahead Time-Varying Hedging Rule

This paper aims to improve summer power generation of the Yeywa Hydropower Reservoir in Myanmar using the modified multi-step ahead time-varying hedging (TVH) rule as a case study. The results of the TVH rules were compared with the standard operation policy (SOP) rule, the binary standard operation policy (BSOP) rule, the discrete hedging (DH) rule, the standard hedging (SH) rule, the one-point hedging (OPH) rule, and the two-point hedging (TPH) rule. The Multi-Objective Genetic Algorithm (MOGA) was utilized to drive the optimal Pareto fronts for the hedging rules. The results demonstrated that the TVH rules had higher performance than the other rules and showed improvements in power generation not only during the summer period but also over the entire period.

     

Evaluation of Real-Time Operation Rules in Reservoir Systems Operation

Reservoir operation rules are logical or mathematical equations that take into account system variables to calculate water release from a reservoir based on inflow and storage volume values. In fact, previous experiences of the system are used to balance reservoir system parameters in each operational period. Commonly, reservoir operation rules have been considered to be linear decision rules (LDRs) and constant coefficients developed by using various optimization procedures. This paper addresses the application of real-time operation rules on a reservoir system whose purpose is to supply total downstream demand. Those rules include standard operation policy (SOP), stochastic dynamic programming (SDP), LDR, and nonlinear decision rule (NLDR) with various orders of inflow and reservoir storage volume. Also, a multi-attribute decision method, elimination and choice expressing reality (ELECTRE)-I, with a combination of indices, objective functions, and reservoir performance criteria (reliability, resiliency, and vulnerability) are used to rank the aforementioned rules. The ranking method employs two combinations of indices: (1) performance criteria and (2) objective function and performance criteria by using the same weights for all criteria. Results show that the NLDR gives an appropriate rule for real-time operation. Moreover, NLDR validation is presented by testing predefined curves for dry, normal, and wet years.