A new optimization method: Dolphin echolocation

Nature has provided inspiration for most of the man-made technologies. Scientists believe that dolphins are the second to humans in smartness and intelligence. Echolocation is the biological sonar used by dolphins and several kinds of other animals for navigation and hunting in various environments. This ability of dolphins is mimicked in this paper to develop a new optimization method. There are different meta-heuristic optimization methods, but in most of these algorithms parameter tuning takes a considerable time of the user, persuading the scientists to develop ideas to improve these methods. Studies have shown that meta-heuristic algorithms have certain governing rules and knowing these rules helps to get better results. Dolphin echolocation takes advantages of these rules and outperforms many existing optimization methods, while it has few parameters to be set. The new approach leads to excellent results with low computational efforts.     

Towards optimal allocation of computer resources: Trade-offs between uncertainty quantification,discretization and model reduction

The computational complexity of numerical models can be broken down into contributions ranging from spatial, temporal and stochastic resolution, e.g., spatial grid resolution, time step size and number of repeated simulations dedicated to quantify uncertainty. Controlling these resolutions allows keeping the computational cost at a tractable level whilst still aiming at accurate and robust predictions. The objective of this work is to introduce a framework that optimally allocates the available computational resources in order to achieve highest accuracy associated with a given prediction goal. Our analysis is based on the idea to jointly consider the discretization errors and computational costs of all individual model dimensions (physical space, time, parameter space). This yields a cost-to-error surface which serves to aid modelers in finding an optimal allocation of the computational resources (ORA). As a pragmatic way to proceed, we propose running small cost-efficient pre-investigations in order to estimate the joint cost-to-error surface, then fit underlying complexity and error models, decide upon a computational design for the full simulation, and finally to perform the designed simulation at near-optimal costs-to-accuracy ratio. We illustrate our approach with three examples from subsurface hydrogeology and show that the computational costs can be substantially reduced when allocating computational resources wisely and in a situation-specific and task-specific manner. We conclude that the ORA depends on a multitude of parameters, assumptions and problem-specific features and, hence, ORA needs to be determined carefully prior to each investigation.     

模块化多电平变换器优化调度调制策略研究

由于MMC电源的模块电容电压存在波动,一般认为它较为适用于定频运行。首先,定义了MMC电源的2种工作模式,即高效放电的工作模式和高效充电的工作模式;然后在此基础上提出了MMC电源的优化调度调制方法,该方法能够实现MMC变频变压运行时的低电容电压波动和高效控制;最后仿真结果验证了所提方法的有效性。     

CO2腐蚀数据库的设计和实现

在Microsoft Win XP中文版操作系统支持下，应用Visual FoxPro6.0中文版数据库管理系统设计和实现了一个全中文的综合性腐蚀数据库.库内包括影响CO2腐蚀的各种影响因素的腐蚀数据.数据库功能包括数据编辑、检索浏览、库维护以及打印等功能.使用Visual FoxPro6.0自带的编程语言编制数据库管理系统和用户界面，软件支持环境简单，使用操作简便.      

Seeker optimization algorithm for global optimization: A case study on optimal modelling of proton exchange membrane fuel cell (PEMFC)

In order to optimize the proton exchange membrane fuel cell (PEMFC) model parameters, a novel approach based on seeker optimization algorithm (SOA) is proposed. The SOA is based on the concept of simulating human searching behaviors, where the choice of search direction is based on the empirical gradient by evaluating the response to the position changes and the decision of step length is based on uncertainty reasoning by using a simple Fuzzy rule. In this study, after evaluated on benchmark function optimization, the SOA is applied to optimal modelling of the PEMFC by using a fuel cell test system in Fuel Cell Application Centre (FAC) at the Temasek Polytechnic, and compared with several state-of-the-art versions of differential evolution (DE) and particle swarm optimization (PSO) algorithms. The simulation results show that the proposed approach is superior to other compared algorithms, and the PEMFC model with optimized parameters by SOA fitted experimental data well. Hence, SOA is an effective and reliable technique for optimizing the parameters of PEMFC model, and can be helpful for system analysis, optimization design and real-time control of the PEMFCs.     

An innovative technique to evaluate network equivalent for voltage stability assessment in a widespread sub-grid system

The equivalent two-bus network models currently available are obtained by lumping all the series impedances and shunt admittances of transmission lines within a series equivalent impedance, to assess voltage stability of multi-bus power system. This paper reports the development of an equivalent pi-network model using a new methodology considering series and shunt parts of line loss separately obtained from the operational parameters of optimal power flow solution of the original multi-bus power network, which can be applied to assess the overall voltage stability status of the system accurately by developing the concept of a generalized global voltage stability margin (GVSM). Simulation results for a typical longitudinal power supply (LPS) system and a robust practical (Indian Eastern Grid) system establish that the pi-equivalent model obtained by the proposed method is highly promising for assessing voltage stability of any power system at any operating point in global scenario in a better way as compared to available series equivalent model. Continuation power flow (CPF) method has also been adopted here to verify the potential of the proposed method for voltage stability assessment. In the proposed equivalent network the generators have been modeled more accurately considering optimal operating criteria.     

Optimal power system operation using parallel processing system and PSO algorithm

In recent studies, PSO algorithm is applied to solve OPF problem. However, population based optimization method requires higher computing time to find optimal point. This shortcoming is overcome by a straightforward parallelization of PSO algorithm. The developed parallel PSO algorithm is implemented on a PC-cluster system with 8 Intel Pentium IV 2 GHz processors. The proposed approach has been tested on the test systems. The results showed that computing time of parallelized PSO algorithm can be reduced by parallel processing without losing the quality of solution.     

DG integrated multistage distribution system expansion planning

In this paper, a framework is presented to solve the problem of multistage distribution system expansion planning in which installation and/or reinforcement of substations, feeders and distributed generation units are taken into consideration as possible solutions for system capacity expansion. The proposed formulation considers investment, operation, and outage costs of the system. The expansion methodology is based on pseudo-dynamic procedure. A combined genetic algorithm (GA) and optimal power flow (OPF) is developed as an optimization tool to solve the problem. The performance of the proposed approach is assessed and illustrated by numerical studies on a typical distribution system.