Comparative Application of Model Predictive Control and Particle Swarm Optimization in Optimum Operation of a Large-Scale Water Transfer System

Water Resources Management - This research evaluates the application and performance of two methods of Model Predictive Control (MPC) and Particle Swarm Optimization (PSO) in real time control and...     

A Particle Swarm Optimization Assessment for the Determination of Non-Darcian Flow Parameters in a Confined Aquifer

Being one of the preliminary in-situ testing methods, aquifer pumping tests would provide significant insights which form a basis for the aquifer characterization. The use of Darcian based flow models to describe the groundwater flow would be ineffective for the aquifer pumping tests under certain circumstances. Non-Darcian flow models could therefore construct more accurate portrayal of physical reality for the assessment of aquifer testing. The interpretation of flow parameters obtained from non-Darcian flows via classical curve matching methods seems extremely difficult to acquire a unique match since the well-defined type curves have not been developed. In this study, an evolutionary optimization based algorithm, called as Particle Swarm Optimization (PSO), was established to determine the flow parameters namely power index, storativity and the turbulent factor which serves as an apparent hydraulic conductivity. The proposed PSO based parameter estimation scheme was implemented for a number of numerical test cases and the estimation performance was evaluated by comparing with available population based algorithms. The results reveal that the PSO based estimation approach is successfully able to identify the flow parameters in an accurate and fast manner. A number of sensitivity analyses were also conducted to draw the limitations of the introduced PSO based technique. The positive findings from this study pointed the potential capability of using PSO as a viable algorithm to process the complex relations in the flow.

     

基于PSO与DE混合算法的水资源优化配置

基于公平性原则构建水资源优化配置模型,针对模型的特点,将模型的可行解进行粒子化处理。利用基于粒子群(PSO)和差分进化(DE)的混合算法(PSODE)对模型进行求解。该算法通过双种群间的信息共享机制,大大降低了求解陷入局部最优的风险。此外,还采用了一种粒子变异机制进一步提高PSODE算法的性能,并通过漳河流域四大灌区水资源配置实例表明PSODE算法比PSO和DE算法收敛速度更快、准确度更高。     

Development of a Fuzzy Multi-Objective Heuristic Model for Optimum Water Allocation

Water Resources Management - A challenging issue in optimal allocating water resources is uncertainty in parameters of a model. In this paper, a fuzzy multi-objective model was proposed to maximize...     

A Model for Solving the Optimal Water Allocation Problem in River Basins with Network Flow Programming When Introducing Non-Linearities

The allocation of water resources between different users is a traditional problem in many river basins. The objective is to obtain the optimal resource distribution and the associated circulating flows through the system. Network flow programming is a common technique for solving this problem. This optimisation procedure has been used many times for developing applications for concrete water systems, as well as for developing complete decision support systems. As long as many aspects of a river basin are not purely linear, the study of non-linearities will also be of great importance in water resources systems optimisation. This paper presents a generalised model for solving the optimal allocation of water resources in schemes where the objectives are minimising the demand deficits, complying with the required flows in the river and storing water in reservoirs. Evaporation from reservoirs and returns from demands are considered, and an iterative methodology is followed to solve these two non-network constraints. The model was applied to the Duero River basin (Spain). Three different network flow algorithms (Out-of-Kilter, RELAX-IV and NETFLO) were used to solve the allocation problem. Certain convergence issues were detected during the iterative process. There is a need to relate the data from the studied systems with the convergence criterion to be able to find the convergence criterion which yields the best results possible without requiring a long calculation time.     

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。     

Correction to: Development of a Fuzzy Multi-Objective Heuristic Model for Optimum Water Allocation

Water Resources Management - The original version of this article unfortunately contains mistakes introduced during the publishing process. The mistakes and corrections are described in the...     

基于混合微粒群算法的堆石坝土石方调配计算

基于微粒群算法和模拟退火算法,构成混合微粒群算法.建立混合微粒群算法数学模型,用于堆石坝土石方调配计算.在河口村水库面板堆石坝土石方调配计算的应用中,计算成果为施工组织设计提供了较为详尽、可靠的数据支持.与其他算法相比,利用混合微粒群算法解决土石方调配问题方便、可行.     

Optimization of Nebhana Reservoir Water Allocation by Stochastic Dynamic Programming

This study is devoted to the identification of an optimal rule that would permit to improve the water resources management of dam in arid condition. The Nebhana dam is considered in this study as a representative of a set dams situated in such condition. The water storage is used for irrigation purpose. The identification of an optimal rule is based on two opposite objectives: the satisfaction of the irrigation water demand and the safeguard of a minimal water storage in the dam. By considering different weights for these objectives, the stochastic dynamic programming technique was lead to various optimal rules for the water resources management of the Nebhana dam. This technique takes into account the variability of the volume of water inflow to the dam on the basis of their occurrence probability; the water losses by means of forecasting models and the water resources goals using weight coefficients. The identified optimal rule would permit to estimate the necessary water release volume for irrigation by considering the water storage and the decision period.     

A Stochastic Non-linear Programming Model for a Multi-period Water Resource Allocation with Multiple Objectives

A rapid increase in demand and severe droughts in recent years has increased the pressure on water supplies throughout most parts of Australia. This has resulted in the need for tools to allocate limited water across users in different regions, and explore scenarios so as to achieve economic, social and environmental benefits. A major challenge in water resource allocation is dealing with the uncertainty in the system, particularly with respect to reservoir inflow. Stochastic non-linear programming is applied to water resource allocation to accommodate this uncertainty across the time periods of the planning horizon. A large range of solutions is produced representing the distributions of uncertainty in reservoir inflow. These solutions are used in a Monte Carlo simulation to estimate the trade-off in amounts of water allocated versus risk of not achieving minimal reservoir levels. The methodology is applied to a case study in South East Queensland in Australia, a region which is currently facing a severe water shortage over the next 3 years. A new water supply initiative that the Queensland State Government is considering to overcome the water crisis is assessed using the methodology.     

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。     

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。     

基于量子行为粒子群优化算法的河道糙率反演

糙率是河道水动力模型的重要参数,在模型中敏感性高,但其在实际工作中难以准确测定。量子行为粒子群算法(QPSO)是粒子群算法的发展,相对于粒子群算法,在全局收敛和收敛率上有很大提高。将量子行为粒子群优化算法与一维河道水动力模型耦合,建立河道糙率反演模型,并在淮河干流蚌埠到花园咀河段进行了模拟,取得了较好的效果。与其他糙率反演算法相比,该算法具有理论简单、参数少、易于编程实现、通用性强等优点。     

一二维耦合河网水沙模型研究

借鉴河网水流的三级解法,将二维河段概化为河网内部河段,通过河网节点流量和输沙量的平衡,建立一二维耦合河网水沙模型。模型采用全隐式方法建立二维河段以首末断面的水位和含沙量为中间变量的矩阵追赶关系,进而建立整个一二维河网的节点水位及含沙量的矩阵方程组。对方程组的求解,可实现一二维水沙模型的耦合求解。通过对长江下游大通至镇江概化河网的验证计算,表明模型具有很好的实用价值。     

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。     

基于粒子群算法的邯郸市水资源优化配置系统研究

随着经济的发展,邯郸市缺水矛盾日益严重,合理配置有限的水资源显得尤为重要.运用多目标规划理论建立一个多水源联合调度的水资源优化配置模型.该模型以经济、社会和环境的最大综合效益为目标,用粒子群算法求解,得到了邯郸市规划年(2020年)3种不同保证率下的水资源优化配置方案,为邯郸市的水资源规划和管理提供了依据.优化结果表明...     

基于粒子群算法的邯郸市水资源优化配置系统研究

随着经济的发展,邯郸市缺水矛盾日益严重,合理配置有限的水资源显得尤为重要。运用多目标规划理论建立一个多水源联合调度的水资源优化配置模型。该模型以经济、社会和环境的最大综合效益为目标,用粒子群算法求解,得到了邯郸市规划年（2020年）3种不同保证率下的水资源优化配置方案,为邯郸市的水资源规划和管理提供了依据。优化结果表明,粒子群算法在邯郸市水资源优化配置中是切实可行的。     

Parameters Estimation for the New Four-Parameter Nonlinear Muskingum Model Using the Particle Swarm Optimization

The Muskingum model is a popular method for flood routing in river engineering. This model has several parameters, which should be estimated. Most of the techniques have applied to estimate these parameters to reduce the distance between observed flow and estimated flows. In this paper, for the first time, the parameters of a novel form of the nonlinear Muskingum model are estimated by the Particle Swarm Optimization (PSO) algorithm. The new Muskingum model, which have four parameters, is applied for three benchmark examples and one real case in Iran. The sum of the squared (SSQ) or absolute (SAD) deviations between the observed and estimated outflows was considered as objective functions. The results showed that although the new Muskingum model became more complex but this model by using PSO technique can improve the fit to observed flow especially in multiple-peak hydrographs.     

基于改进粒子群算法的大坝监控加权统计模型

用于大坝安全监控的加权统计模型主要依据工程经验确定各因子的权重,这种求解方式易导致部分因子信息的缺失。根据大坝安全监测数据,应用粒子群算法可优化确定加权统计模型中各参数的最优解,但对于高维度优化问题,该算法存在收敛速度慢、易陷入局部最小等不足。针对这些不足,考虑粒子种群平均位置信息的影响,提出一种新的改进粒子群算法,利用单体与种群平均位置的距离信息确定两者之间的学习因子。土石坝工程实例分析结果表明:改进粒子群算法加强了种群跳出局部最小的能力,所得加权统计模型的权重符合工程实际情况。尤其在大坝运行初期,监测资料较少的情况下,基于改进粒子群算法的大坝监控模型具有较高的预测精度和预报能力,可为大坝监控领域提供一种新的数据分析方法。     

基于粒子群算法的梯级水电站水能优化计算

基于粒子群算法,考虑梯级水电站之间水能、水量、出力之间的约束和联系,以及影响梯级电站水能计算的发电效率、水头、发电流量等因素,对某梯级水电站水能进行了优化计算。结果表明:非统一调节单级优化时的发电量比设计年均发电量大35.62亿kW·h,统一调节梯级优化时的发电量比设计年均发电量大97.84亿kW·h;离开了上游调节能力强的水库调节,下游水电站的水能损失较大。