The Short-Term Economical Operation Problem for Hydropower Station Using Chaotic Normal Cloud Model Based Discrete Shuffled Frog Leaping Algorithm

Water Resources Management - The short-term economical operation (STEO) in hydropower station is nonlinear mixed integer problem, satisfying complex hydrologic constraints simultaneously. In this...     

Multi-Strategy Improved Slime Mould Algorithm and its Application in Optimal Operation of Cascade Reservoirs

Water Resources Management - Slime mould algorithm (SMA) is extensively used in engineering applications such as the parameter estimation of photovoltaic models, image segmentation, economic...     

Application of the Shuffled Frog Leaping Algorithm for the Optimization of a General Large-Scale Water Supply System

A water supply system is a complex network of pipes, canals and storage and treatment facilities that collects, treats, stores, and distributes water to consumers. Increasing population and its associated demands requires systems to be expanded and adapted over time to provide a sustainable water supply. Comprehensive design tools are needed to assist managers determine how to plan for future growth. In this study, a general large-scale water supply system model was developed to minimize the total system cost by integrating a mathematical supply system representation and applying an improved shuffled frog leaping algorithm optimization scheme (SFLA). The developed model was applied to two hypothetical water communities. The operational strategies and the capacities for the system components including water transport and treatment facilities are model decision variables. An explicit representation of energy consumption cost for the transporting water in the model assists in determining the efficacy of satellite wastewater treatment facilities. Although the water supply systems studied contained highly nonlinear terms in the formulation as well as several hundred decisions variables, the stochastic search algorithm, SFLA, successfully found solutions that satisfied all the constraints for the studied networks.     

Random Bagging Classifier and Shuffled Frog Leaping Based Optimal Sensor Placement for Leakage Detection in WDS

Water Resources Management - Water Distribution Systems (WDS) are the large scale systems that demand design of enhanced leak detection and isolation techniques to prevent from water waste....     

梯级水库群联合优化调度算法研究综述

系统介绍了梯级水库群联合调度模型求解的常规优化方法和智能优化方法,其中常规优化方法包括动态规划法及其改进方法和大系统分解协调等方法,智能优化方法包括粒子群优化算法、遗传算法、差分进化算法等。在评述各种方法的基础上,给出了大规模水库群联合优化调度需要解决的问题:1模型系统求解的稳定性和求解效率的平衡问题;2考虑生态环境的多目标优化调度模式;3水库群优化调度模型描述方法;4优化模型输入和输出的不确定性问题;5机组高水头多振动区运行问题。同时,指出了智能优化算法的发展方向:1数学理论基础研究;2参数对算法结果的影响分析;3各种优化算法的改进与融合;4求解算法的并行计算和云计算技术。     

Adaptive Genetic Algorithm for Daily Optimal Operation of Cascade Reservoirs and its Improvement Strategy

For the specialty of cascade reservoirs optimization and the premature convergence of GA, several improvement strategies are presented in this paper. Firstly, solution space generation method is found application to generate feasible initial population. Secondly, chaos optimization is adopted to optimize initial population. Thirdly, new selective operators, trigonometric selective operators, are proposed to overcome the fitness requirement of non-negative and to maintain the diversity of population. Fourthly, adaptive probabilities of crossing and mutation are adopted in order to improve the convergence speed of GA. Besides, elitist strategy is used to ensure that the best individual can be remained in each generation. Furthermore, the performance of these proposed improvement strategies was checked against the historical improvement strategies by simulating optimal operation of Three Gorges cascade reservoirs premised on historical hourly inflows, and the comparison yields indications of superior performance. In these proposed improvement strategies, trigonometric selective operators are feasible and effective for optimizing operation of cascade reservoirs. These new selective operators could help GA to find a more excellent solution in the same algebra, and the performance of convergence speed is advanced. Adaptive probabilities of crossing and mutation have better performance than other improvement strategies, such as annealing chaotic mutation and simulated annealing of large probability of mutation, because this method realizes the twin goals of maintaining diversity in the population and advancing the convergence speed of GA.     

Integration of Shuffled Frog Leaping Algorithm and Support Vector Regression for Prediction of Water Quality Parameters

Quality of surface water is a serious factor affecting human health and ecological systems. Accurate prediction of water quality parameters plays an important role in the management of rivers. Thus, different methods such as (support vector regression) SVR have been employed to predict water quality parameters. This paper applies SVR to predict eight water quality parameters including (sodium (Na⁺), potassium (K⁺), magnesium (Mg⁺²), sulfates (SO₄ ^?2), chloride (Cl^?), power of hydrogen (pH), electrical conductivity (EC), and total dissolved solids (TDS)) at the Astane station in Sefidrood River, Iran. To achieve an efficient SVR model, the SVR parameters should be selected carefully. Commonly, various techniques such as trial and error, grid search and metaheuristic algorithms have been applied to estimate these parameters. This study presents a novel tool for estimation of quality parameters by coupling SVR and shuffled frog leaping algorithm (SFLA) . Results of SFLA-SVR compared with genetic programming (GP) as a capable method in water quality prediction. Using SFLA-SVR, average of RMSE for training and testing of six combinations of data sets for all of the water quality parameters improved 57.4 % relative to GP. These results indicate that the new proposed SFLA-SVR tool is more efficient and powerful than GP for determining water quality parameters.     

多目标免疫遗传算法在梯级水库优化调度中的应用研究

为了提高遗传算法在多目标梯级水库优化调度中的应用效果,在标准遗传算法的基础上引入了免疫机制,并将其应用到周公宅-皎口水梯级水库优化调度中。计算结果表明,免疫遗传算法较好地克服了标准遗传算法收敛速度慢、易陷入局部极值、早熟等弱点。     

梯级水库联合优化调度模型及其应用

为充分挖掘梯级水库间的水文补偿和库容补偿潜力,获取更高的综合利用效益,提出应用判别系数与水库蓄供水控制线相结合的方法,并结合免疫粒子群算法,以梯级电站发电量最大为目标,对水库蓄供水控制线进行优化。以乌江梯级水库为例,对其蓄供水方式进行了研究。联合调度结果表明,优化后的水库蓄供水控制线不仅可以合理控制水库蓄放水次序和蓄放水量,达到合理控制水库群运行方式的目的,而且在实际调度中具有良好的可操作性和抗风险性。     

梯级水库联合优化调度模型及其应用

为充分挖掘梯级水库间的水文补偿和库容补偿潜力,获取更高的综合利用效益,提出应用判别系数与水库蓄供水控制线相结合的方法,并结合免疫粒子群算法,以梯级电站发电量最大为目标,对水库蓄供水控制线进行优化。以乌江梯级水库为例,对其蓄供水方式进行了研究。联合调度结果表明,优化后的水库蓄供水控制线不仅可以合理控制水库蓄放水次序和蓄放水量,达到合理控制水库群运行方式的目的,而且在实际调度中具有良好的可操作性和抗风险性。     

基于改进调度函数的梯级水库群联合调度研究

梯级水库联合调度规律是指导梯级水库优化运行的重要手段,目前尚无完整的梯级水库群调度规律的理论体系.为此,提出一种可获取梯级水库群联合调度规律的改进调度函数方法,并应用逐步回归法对调度函数进行拟合及相关因子的选择.以乌江流域梯级水库群为例,开展其联合调度研究.结果表明,该方法计算结果较原设计方案,多年平均发电量比设计值增加了14.43亿kW.h,涨幅达5％,保证出力提高到2431.9 MW,经济效益显著.     

粒子群算法在梯级水库优化调度中的应用研究

针对PSO算法易陷入局部最优和后期收敛速度慢的缺陷,引入进化遗传算法中的"变异"算子并采用自适应的权重因子,以改进其全局优化能力和搜索效率。引入粒子矩阵,通过对粒子在多维空间中最优位置的搜索来实现逐时段的优化计算,并将该算法应用于金沙江复杂梯级水电能源系统中长期优化调度计算中。计算结果表明,智能优化方法在复杂系统全局优化问题上较常规数学优化算法有更优越的优化性能,从而为解决复杂水电能源非线性动力系统的全局优化计算问题提供了一种新的有效的方法。     

Cooperation Search Algorithm for Power Generation Production Operation Optimization of Cascade Hydropower Reservoirs

Water Resources Management - Over the past decades, the reservoirs operation plays an increasingly important role in coping with the serious water, food and energy crisis. However, the curse of...     

乌江梯级水库发电优化调度会商决策支持系统研究与应用

发电调度是乌江梯级水库的主要任务,决策正确与否不仅关系到电网的安全经济运行,而且关系到梯级各电厂的经济利益。本文介绍了如何通过会议的形式,利用现代信息等高科技技术,集中研讨历史和实时水情、气象、电网需求和发展趋势,以对预先制定的各种可行调度方案做出科学的决策,从而获得满意的调度方案,为乌江梯级水库的安全、经济调度做出贡献。     

松涛灌区渠道梯级电站优化调度研究与应用

松涛水利工程管理局在松涛灌区（东干）渠道上建立的5座梯级电站由于地理位置分散,渠道调节性能差,发电效益不高,综合利用部门之间的矛盾突出。为此,对松涛灌区渠道梯级电站进行优化调度研究,以现代控制论、动态规划法为基础,根据动态规划理论,求解适合渠道梯级电站优化运行方式,并开展渠道梯级电站联合优化调度应用的探索,实践证明,松涛灌区梯级电站优化调度后效益显著。     

紧水滩水电厂梯级水库联合调度分析

结合紧水滩水电厂梯级水库联合调度的运行实践,提出了做好水库调度基础工作、减少弃水、提高发电水头、抓好设备技术改造、合理利用超蓄等提高梯级电站综合效益的途径。     

流域梯级水库群联合调度研究进展

在阐述国内外梯级水库群联合调度研究的基础上,系统分析了现阶段水库调度的新理念,讨论了梯级水库群联合优化调度在防洪和兴利方面的优点,论述了优化调度过程中确定性方法和不确定性方法的优缺点,并指出当前调度管理中存在调度管理信息化建设不够完善、对流域生态环境造成负面影响等问题。建议:1在运行模式方面将水库调度原则和电力市场原则相结合,在调度模型方面构建既符合实际又便于求解的模型,在模型求解方面引进新理论、新技术;2建立包含梯级水库计算机监测控制系统、水情自动测报系统等为一体的水库优化调度管理模式;3将生态调度纳入联合调度统一考虑,积极探索梯级水库群防洪、兴利与生态环境相互协调、统一的水库综合调度方式,将水库群对河流生态和库区水环境造成的负面影响控制在可承受范围内。     

黄河上游梯级水库防凌优化调度方案研究

针对黄河上游凌汛期龙羊峡水库出库流量调度问题,在分析上游梯级水库防凌调度原则及调度运行方式的基础上,考虑青海电网电量平衡的需求,建立了黄河上游梯级水库防凌优化调度模型,并提出了改进的微粒群算法用于求解该模型。结果表明:凌汛期在保证龙羊峡水库出库总水量不变的情况下,通过调整龙羊峡水库在各时段的出库水量,既满足了青海电网电量平衡的要求,优化了青海电网电量结构,同时也增加了刘家峡水库防凌库容;通过优化调度,增加了凌汛期梯级水电站的可发电量,提高了梯级电站效益。     

小浪底与西霞院水库联合优化调度

小浪底、西霞院水库联合优化调度是指在小浪底水库泄洪、排沙、调水调沙运用时,西霞院水库水位降至减淤限制水位131 m以下,在满足黄河水调指令的前提下兼顾水库减淤和发电效益;在小浪底水库供水运用时,西霞院水库反调节运用,通过小浪底、西霞院水库发电计算机集中监控系统自动实现两库发电计划编制及发电经济运行,在满足黄河水调指令的前提下,提高两库整体发电效益。联合优化调度取得了显著的效果:西霞院水库下泄流量精确满足黄河水调指令要求,保障了黄河下游防洪、减淤、供水、灌溉安全;小浪底水库淤积减缓,西霞院水库多年冲淤基本平衡,保持了水库库容;小浪底水库发电满足了河南电网的调峰需求,西霞院水库发电量逐年提高。同时,建议国家改进电网调度体制,促进流域梯级水库联合调度,提高整体发电效益。     

改进的遗传算法在水库调度中的应用

为了改善遗传算法在水库优化调度中的应用效果,采用自适应遗传算法和广度变异模块相结合的分层收敛算法:第一层采用广度变异和外部存档的方式改善种群的多样性;第二层嵌套广度变异模块,并采用自适应遗传算法进行全局搜索。通过比较自适应遗传算法和分层进化算法,结果显示:基于遗传算法的分层算法具有高效的全局搜索能力,避免了自适应遗传算法陷入局部最优的缺陷,在一定收敛条件下得到了更接近全局最优的目标值。