基于粒子群算法的灌溉水泵系统优化设计与运行

文章提出了一种相对较新的用于灌溉抽水系统优化设计和运行的管理模式。该管理模式利用粒子群优算法化建立并求解了一个两步优化模型。新提出的模型通过对所有可行的泵机组组合进行详尽的枚举搜索后,在所需时间段内处理给定的需求曲线,然后调用粒子群优化算法搜索每个集合的最优解。在优化机组的运行问题后,计算所有机组的运行总成本和初始投资折旧,确定最优的机组组合,并制定相应的运行策略。研究将所提出的模型用于实际泵站系统的设计和运行后,将结果与优化算法的结果进行比较。结果表明,所提出的模式与粒子群优化算法相结合是一种用于实际灌溉泵系统设计和运行的通用管理模型。     

Performance Assessment of a Coupled Particle Swarm Optimization and Network Flow Programming Model for Optimum Water Allocation

Water resources allocation problems are mainly categorized in two classes of simulation and optimization. In most cases, optimization problems due to the number of variables, constraints and nonlinear feasible search space are known as a challenging subject in the literature. In this research, by coupling particle swarm optimization (PSO) algorithm and a network flow programming (NFP) based river basin simulation model, a PSO-NFP hybrid structure is constructed for optimum water allocation planning. In the PSO-NFP model, the NFP core roles as the fast inner simulation engine for finding optimum values for a large number of water discharges in the network links (rivers and canals) and nodes (reservoirs and demands) while the heuristic PSO algorithm forms the outer optimization cover to search for the optimum values of reservoirs capacities and their storage priorities. In order to assess the performance of the PSO-NFP model, three hypothetical test problems are defined, and their equivalent nonlinear mathematical programs are developed in LINGO and the results are compared. Finally, the PSO-NFP model is applied in solving a real river basin water allocation problem. Results indicate that the applied method of coupling PSO and NFP has an efficient ability for handling river basin-scale water resources optimization problems.     

改进粒子群优化算法在水文模型参数优化中的应用研究

引入改进的粒子群优化算法,对垂向混合产流模型计算参数进行优化,并对比参数优化前后水文模拟精度。研究结果表明：改进的粒子群优化算法模型可较快完成参数优化,相比于参数优化前,垂向混合产流模型年尺度模拟相对误差减少6.15%,模拟的过程确定性系数平均提高0.11;在次洪模拟中,模拟相对误差平均减少3.03%,模拟的洪水过程确定性系数平均提高0.19,水文模拟精度得到较大程度提高。研究成果对于区域水文模型参数优化提供参考价值。     

改进粒子群算法在梯级水库优化调度中的应用

针对粒子群优化算法搜索精度不高和易陷入局部最优解的缺陷,结合梯级水库联合优化调度的实际问题,提出了惯性权重的余弦处理机制和自适应选择学习对象策略,并结合分层交叉思想对粒子群算法进行了改进。实例计算表明:该算法合理、可靠,有效地提高了计算精度与效率。     

混沌粒子群优化算法在马斯京根模型参数优化中的应用

针对目前马斯京根模型参数率定中存在的求解复杂、精度不高等问题,本文将混沌搜索机制引入粒子群优化算法中,构建混沌粒子群优化算法对马斯京根模型参数进行率定。这种方法利用混沌运动的遍历性,改善了粒子群优化算法的全局寻优能力,避免算法陷入局部极值,使得粒子群体的进化速度加快,提高了算法的收敛速度和精度。通过实例应用表明,混沌粒子群优化算法可以有效地估算出马斯京根模型参数,优化效果明显优于粒子群优化算法及试错法,因此该算法具有很好的实用性。     

Simulation and Optimization of Multi-Reservoir Operation in Inter-Basin Water Transfer System

Inter-basin water transfer projects are usually considered as one of the most effective facilities to balance the non-uniform temporal and spatial distribution of water resources and water demands by diverting water from surplus to deficient area. However, the operation of these projects are always daunting, especially for projects with multi-donor reservoirs but only one recipient reservoir. In this study, a set of water transfer rule curves are firstly proposed to determine when, where and how much water should be diverted from each donor reservoir. In addition, a simulation-optimization model with the objective to minimize both water shortage risk and vulnerability is established to derive the optimal operation rule curves. Following that, the new transfer rules are applied to provide guidelines for the operation of a water transfer-supply project with two donor reservoirs in central China. The effects of water diversion on each reservoir are evaluated under different scenarios including no diversion, diversion from the donor reservoir with relatively sufficient water, diversion from the donor reservoir with relatively limited water, and diversion from both donor reservoirs. The results show the advantages of improving the performance of whole water diversion system and demonstrate the feasibility of the proposed approach.     

粒子群算法在水电站日优化调度中的应用

针对传统的动态规划方法求解水库优化调度问题存在的“维数灾”问题,给出一种全局随机优化算法[1]——粒子群优化算法并应用于水库日优化调度问题中。相对于动态规划,该算法原理简单,易编程,占用计算机内存少,能以较快的速度收敛到全局最优解,从而为分时电价环境下的水电站日优化调度问题提供了一种有效的解决办法。     

粒子群算法在水电站引水系统优化方面的应用

水电站引水管道直径的取值影响水电站工程的造价和发电效益,是水电站引水系统优化设计的重要内容.以引水管道直径为变量,水电站净收益最大为准则,建立了水电站引水系统优化模型,分别采用标准PSO算法和改进PSO算法进行计算.结果表明,这两种方法均可用于水电站引水系统的优化,改进PSO算法优于标准PSO算法.     

SAPSO算法在梯级水电站日优化调度中的应用

针对传统的动态规划方法求解水库优化调度阋题存在的"维数灾"问题,提出一种全局随机优化算法[1]--SAPSO算法及其在水库日优化调度问题中的应用.相对于动态规划,该算法原理简单,易编程实现,占用计算机内存少,能以较快的速度收敛到全局最优解,从而为分时电价环境下的水电站日优化调度问题提供了一种有效的解决办法.     

改进粒子群优化算法在水电站群优化调度中的应用研究

针对常规粒子群优化算法易早熟，后期收敛慢且易陷入局部最优解的不足，提出一种新的惯性权重系数更新策略—自适应指数惯性权重系数（SEIWC）代替线性递减惯性权重系数（LDIWC），同时，将遗传算法中的染色体交叉、变异思想引入粒子的更新策略，提高粒子的多样性，增强算法的全局搜索能力。使用Rosenbrock函数和Schaffer函数验证了改进粒子群优化算法的有效性。以福建电网闽江流域水电站群优化调度为例，建立基于改进粒子群优化算法的库群长期优化调度模型，计算结果表明，该模型的调度结果显著优于常规粒子群优化算法，与逐步优化算法获得的结果达到相当水平。     

改进粒子群算法在新安江模型参数优选中的应用

粒子群优化算法的原理较易理解，所需参数较少而易于实现，但在具体问题中仍存在如收敛性不稳定等不足。基于收缩因子改进的粒子群算法可以保证算法的收敛性，同时使得速度的限制放松。结合新安江模型参数的特点，将其应用于该模型参数的率定．并编程实现检验分析。实例应用结果表明，该优化算法可得到较为稳定的模型参数．提高模型在洪水预报中的效率。     

Optimization of Hedging Rules for Reservoir Operation During Droughts Based on Particle Swarm Optimization

This paper presents a methodology to achieve the identification of optimal hedging rules for operating reservoir systems, seeking to mitigate the drought impacts. The heuristic Particle Swarm Optimization (PSO) method is adopted as the optimization solver. This procedure establishes a two-phase method that combines PSO with the simulation of the water system, representing a system of reservoirs that are jointly operated to satisfy a set of demands with different priorities. The hedging rules are based on monthly storage levels that trigger restrictions on the demands. As model parameters, monthly rule activation thresholds and rationing factors were used for each type of demand. The optimization procedure minimizes an objective function that penalizes large deficits and assigns different weights to different demand types. Since the whole problem is quite complex, its dimensionality is reduced through: i) a set of candidate monthly activation thresholds are selected a priori associated to given risk conditions; and ii) the rationing factors are defined for every demand of each threshold throughout all months. In addition, an effort is made to avoid the trap in local optimums, whilst several other comments considering the application of the PSO method in the examined applications are provided. The procedure has been successfully applied to four water resource systems in Spain. From the application it can be seen that the deficits of the water supply demand are nearly removed, thanks to the larger weight given to the deficits of this demand type. The irrigation deficits are also reduced, since we lead to a sequence of smaller shortages than only one potential catastrophic shortage.     

粒子群算法及其在暴雨强度公式参数优化中的应用

暴雨强度公式中含有多个参数,用传统的方法难以直接优化,或拟合误差较大.通过把粒子群优化算法(PSO)应用到暴雨强度公式参数优化中,经实例分析表明该算法与遗传算法相比具有较好应用效果.     

梯级泵站输水系统预测控制方法研究

南水北调来水调入密云水库调蓄工程前半段是由泵站、渠道、节制闸等构成的复杂梯级泵站输水系统。为了应对该输水系统经常出现流量不平衡可能带来的水位超出极值约束的问题,建立一种新的预测控制方法,基于恒定流模型建立各渠段的站前水位—流量—蓄量关系,通过水量平衡和插值计算对蓄量和站前水位的时间变化规律进行预测,进而提出控制策略。该预测方法经实际运行数据检验,精度较高、方法简单实用,可用于密云水库调蓄工程梯级泵站输水系统实际运行控制。     

Short Term Optimal Operation of Water Supply Reservoir under Flood Control Stress using Model Predictive Control

Reservoir operations require enhanced operating procedures for water systems under stress attributed to growing water demand and consequences of changing hydro-climatic conditions. This study focuses on the management of the Yuvacik Dam Reservoir for water supply and flood mitigation in the Marmara Region of Turkey. We present an improved operating technique for fulfilling the conflicting water supply and flood mitigation objectives. This is accomplished by incorporating the long term water supply objectives into a Guide Curve (GC) whereas the extreme floods are attenuated by means of short-term optimization based on Model Predictive Control (MPC). The reference case implements operating rules with a constant GC at maximum forebay elevation targeting the fulfillment of the water supply objective. We compare the reference with a new time-dependent GC, derived using an Implicit Stochastic Optimization (ISO) approach. This new curve shows nearly the same performance regarding the water supply objectives, but significantly reduces the flooding risk downstream of the dam. Possible flood events observed at the end of the wet season, when the reservoir is at the maximum level to enable water supply for the dry season, can be eliminated by the application of an additional short-term optimization by MPC. The robustness of the approach is demonstrated via hindcasting experiments.     

改进粒子群算法求解水火电系统短期负荷分配问题

粒子群优化（PSO）算法是一种基于群体智能的启发式搜索方法，应用领域很广。文中将PSO算法用于求解水火电系统短期负荷的经济分配，属于高维、强约束工程问题。分析了算法参数设置对解的影响，发现算法的局部开发能力和粒子的多样性是影响解的优劣的关键因素;提出多子群辅助的PSO算法，兼顾了对解空间的全局搜索和局部开发。实际算例证明，改进的算法是有效的。     

基于改进粒子群优化算法的新安江模型参数优选

新安江模型是一种实用有效的水文模型,在洪水预报以及水资源评估和管理中得到了广泛的应用。为此,结合新安江模型参数的特点,提出了基于改进粒子群优化算法的新安江模型参数优选方法,并将该模型应用到日径流预报中。实例表明,该方法能快速地完成参数寻优,并能较好地寻找出参数的全局最优解。     

机组在线运行优化系统及实时目标工况的确定

介绍了机组在线运行优化系统的体系结构、数据流程和功能模块，提出了机组运行目标工况的定义和实现方案。采用基于趋势提取的检测方法对机组历史运行工况数据库进行稳态判定，将运行不可控因素作为约束条件运用K-均值法将机组稳定运行工况聚类到不同的工况簇中，以机组供电煤耗作为评价基准对各工况簇中的工况进行寻优，将各工况簇中的运行最优工况组合起来作为训练样本，建立起机组运行目标工况的神经网络模型，在进行实例验证后对模型进行分析讨论。实际应用表明，模型能够及时跟踪机组的运行特性变化，实时确定机组目标工况，对于提高机组经济运行水平具有现实意义。     

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.