Improving the multi-objective evolutionary optimization algorithm for hydropower reservoir operations in the California Oroville–Thermalito complex

This study demonstrates the application of an improved Evolutionary optimization Algorithm (EA), titled Multi-Objective Complex Evolution Global Optimization Method with Principal Component Analysis and Crowding Distance Operator (MOSPD), for the hydropower reservoir operation of the Oroville–Thermalito Complex (OTC) – a crucial head-water resource for the California State Water Project (SWP). In the OTC's water-hydropower joint management study, the nonlinearity of hydropower generation and the reservoir's water elevation–storage relationship are explicitly formulated by polynomial function in order to closely match realistic situations and reduce linearization approximation errors. Comparison among different curve-fitting methods is conducted to understand the impact of the simplification of reservoir topography. In the optimization algorithm development, techniques of crowding distance and principal component analysis are implemented to improve the diversity and convergence of the optimal solutions towards and along the Pareto optimal set in the objective space. A comparative evaluation among the new algorithm MOSPD, the original Multi-Objective Complex Evolution Global Optimization Method (MOCOM), the Multi-Objective Differential Evolution method (MODE), the Multi-Objective Genetic Algorithm (MOGA), the Multi-Objective Simulated Annealing approach (MOSA), and the Multi-Objective Particle Swarm Optimization scheme (MOPSO) is conducted using the benchmark functions. The results show that best the MOSPD algorithm demonstrated the best and most consistent performance when compared with other algorithms on the test problems. The newly developed algorithm (MOSPD) is further applied to the OTC reservoir releasing problem during the snow melting season in 1998 (wet year), 2000 (normal year) and 2001 (dry year), in which the more spreading and converged non-dominated solutions of MOSPD provide decision makers with better operational alternatives for effectively and efficiently managing the OTC reservoirs in response to the different climates, especially drought, which has become more and more severe and frequent in California.     

Study on maximum power point tracking of photovoltaic array in irregular shadow

Traditional maximum power point tracking (MPPT) methods can hardly find global maximum power point (MPP) because output characteristics curve of photovoltaic (PV) array may have multi local maximum power points in irregular shadow, and thus easily fall into the local maximum power point. To address this drawback, Considering that sliding mode variable structure (SMVS) control strategy have such advantages as simple structure, fast response and strong robustness, and P&O method have the advantages of simple principle and convenient implementation, so a new algorithm combining SMVS control method and P&O method is proposed, besides, PI controller is applied to reduce system chattering caused by switching sliding surface. It is applied to MPPT control of PV array in irregular shadow to solve the problem of multi-peak optimization in partial shadow. In order to verity the rationality of the proposed algorithm, the experimental circuit is built, which achieves MPPT control by means of the proposed algorithm and P&O method. The experimental results show that compared with the traditional P&O algorithm, the proposed algorithm can fast track the global MPP, tracking speed increases by 60% and the relative error decreased by 20%. Moreover, the system becomes more stable near the MPP, the fluctuations of output power is greatly reduced, and thus make full use of solar energy.     

兼使水流转向的非对称窄缝挑坎

讨论了兼有水流转向功能的一级非对称直线边壁窄缝的设计方法与水力特性，两边墙转向角之差即为水流转向角，两边墙转角相等时出口水流的水深和佛汝德数也分别相等。按在窄缝出口前形成完整幅射流的条件，讨论了兼有转向功能的非对称曲线边墙窄缝的设计方法与水力特性。比较了六种体型(三种对称、三种非对称)窄缝在八种工况对泄槽底板的冲击压强，实测结果表明，非对称性越强冲击压强越小。     

黄河下游河道萎缩致灾机理探讨

本文基于水力学原理，结合实体模型试验及原型实测资料分析，探讨了黄河下游河道萎缩的概念、致灾效应和致灾机理。分析表明，河道萎缩是造床过程中河道排洪输沙功能衰退的一种演变现象，其致灾效应是使洪水涨率增大，同流量下洪水水位不断抬升。致灾的现象主要表现在畸型河势增加、工程险情增多、直接造成工程损失及滩区经济损失等。试验研究揭示，主河槽宽度缩窄造成洪水水位涨幅增大，河底平均高程抬升和过水断面面积减小造成洪水水位起涨的基准面抬升是河道萎缩致灾的主要原因。河道萎缩致灾效应的大小与萎缩模式有关，其中以“集中淤槽”模式所形成的致灾效应尤为明显。     

A BOUNDARY INTEGRAL EQUATION AND ITS NUMERICAL SOLUTION OF POTENTIAL FLOW AROUND SURFACE-IRREGULA-RITIES

In this paper,using complex functional theory,the authors turn the potentialflow around the surface irregularities in a pressure conduit and semi-infinite platforms intoDirichlet problem.Based on Schwarz formula and by the application of Plemelj's formula,theauthors change the problem into the integration of a Cauchy boundary integral equation in theflow plane through the substitution of variables.Using numerical integration,the authors obtainthe velocity distribution and pressure coefficient along surface irregularities and platforms.Thephysical concept of this method is clear,the convergent speed is rapid and the computative effi-ciency is high.The calculated values agree well with the measured results.It is an effective andsimple method in solving potential flow.     

谈节水灌溉的推广应用

全面推行节水灌溉势在必行，结合江西省上饶地区节水灌溉的推广工作，总结经验，提出了相应的对策和建议。     

水动力学数学模型并行计算技术研究及实现

基于MPI(Message Passing Interface)消息传递实现了黄河二维水动力学数学模型的并行编程。以数据的分布存储作为区域划分的依据,实现了计算量的负载平衡;在全局网格和局部区域之间建立映射关系,并且在临界单元、进出口单元、共用节点等通过规约等进行特殊处理,一方面尽可能的减少通讯量,另一方面也避免消息的阻塞。采用曙光4000A系统的8个CPU进行计算的加速比达0.8,多CPU并行计算极大的提高了计算任务的容量,使得无法在单机上完成的巨量计算成为可能。     

Machine-learning paradigms for selecting ecologically significant input variables

Harmful algal blooms, which are considered a serious environmental problem nowadays, occur in coastal waters in many parts of the world. They cause acute ecological damage and ensuing economic losses, due to fish kills and shellfish poisoning as well as public health threats posed by toxic blooms. Recently, data-driven models including machine-learning (ML) techniques have been employed to mimic dynamics of algal blooms. One of the most important steps in the application of a ML technique is the selection of significant model input variables. In the present paper, we use two extensively used ML techniques, artificial neural networks (ANN) and genetic programming (GP) for selecting the significant input variables. The efficacy of these techniques is first demonstrated on a test problem with known dependence and then they are applied to a real-world case study of water quality data from Tolo Harbour, Hong Kong. These ML techniques overcome some of the limitations of the currently used techniques for input variable selection, a review of which is also presented. The interpretation of the weights of the trained ANN and the GP evolved equations demonstrate their ability to identify the ecologically significant variables precisely. The significant variables suggested by the ML techniques also indicate chlorophyll-a (Chl-a) itself to be the most significant input in predicting the algal blooms, suggesting an auto-regressive nature or persistence in the algal bloom dynamics, which may be related to the long flushing time in the semi-enclosed coastal waters. The study also confirms the previous understanding that the algal blooms in coastal waters of Hong Kong often occur with a life cycle of the order of 1–2 weeks.