Simulation of the urban climate variations in connection with the transformations of the city of Nantes since the 17th century

The paper seeks to quantify the effect of urban politics on the microclimate of the city of Nantes (France), in particular those initiated by the sanitarians in the mid-19th century to find a remedy for the insalubrity that had been developing with urban densification since the late 17th century. Intensive historical research was first carried out in order to define and date the major transformations undergone by the city, its structure (densification, then widening of the streets, filling of water courses), the lifestyle habits (heating) or the building practices (appearance of stone and paving, higher buildings, insulation). This led to the definition of 5 characteristic states of the city, in 1680, 1756, 1835, 1880 and 1945.     

Issues in the assimilation of the sources of a trace gas by inverse modelling

The problem of reconstructing the (otherwise unknown) source and sink field of a tracer in a fluid is studied by developing and testing a simple tracer transport model of a single-level global atmosphere and a dynamic data assimilation system. The source/sink field (taken to be constant over a 10-day assimilation window) and initial tracer field are analysed together by assimilating imperfect tracer observations over the window.Experiments show that useful information about the source/sink field may be determined from relatively few observations when the initial tracer field is known very accurately a-priori, even when a-priori source/sink information is biased (the source/sink a-priori is set to zero). In this case each observation provides information about the source/sink field at positions upstream and the assimilation of many observations together can reasonably determine the location and strength of a test source.     

Tuning fuzzy PD and PI controllers using reinforcement learning

In this paper, we propose a new auto-tuning fuzzy PD and PI controllers using reinforcement Q-learning (QL) algorithm for SISO (single-input single-output) and TITO (two-input two-output) systems. We first, investigate the design parameters and settings of a typical class of Fuzzy PD (FPD) and Fuzzy PI (FPI) controllers: zero-order Takagi–Sugeno controllers with equidistant triangular membership functions for inputs, equidistant singleton membership functions for output, Larsen’s implication method, and average sum defuzzification method. Secondly, the analytical structures of these typical fuzzy PD and PI controllers are compared to their classical counterpart PD and PI controllers. Finally, the effectiveness of the proposed method is proven through simulation examples.     

Desorption kinetics of trichloroethylene and perchloroethylene from aged dense nonaqueous phase liquid-contaminated soil

Batch experiments were conducted to investigate the desorption kinetics of a contaminated soil collected from a field site at Hill Air Force Base (AFB), Utah. The site has been contaminated with many dense nonaqueous phase liquids (DNAPLs), primarily trichloroethylene (TCE) and perchloroethylene (PCE), for more than 60 years. Similar desorptive behaviour of both TCE and PCE was observed: a stage of fast release of the contaminant at the start followed by a very slow stage. Moreover, it is suggested that not all the contaminant in the solid phase may be released to the aqueous phase, apparently due to the age factor. Local variations were observed in the effective diffusivity and other sorption-related parameters across the site due to aquifer heterogeneity. Experimental results were then simulated using a numerical code based on the intraparticle diffusion model. Good agreement was found between both results.     

基于模糊神经网络直接转矩控制系统设计

针对传统的直接转矩控制中存在转矩、定子磁链波动大的问题,提出了一种结合空间矢量调制的模糊神经网络直接转矩控制方法。它运用模糊神经网络得到期望的任意相位空间电压矢量,再结合空间矢量调制直接对逆变器的开关状态进行控制,以达到减小转矩、定子磁链波动的目的。最后以TMS320F240为核心搭建了一个直接转矩控制系统,通过采集实验数据,分析该直接转矩控制系统与传统的直接转矩控制系统相比磁链和转矩波动明显减少,并具良好的响应性。     

Volt/Var control in a distribution system by a fuzzy optimization approach

This paper presents a fuzzy optimization approach for solving the Volt/Var control problem in a distribution system with uncertainties. Wind turbines are being considered in the study distribution system. The main purpose is to find an optimum combination of tap position for the main transformer under load tap changer (ULTC) and on/off status for switched capacitors in a day to minimize the voltage deviation on the secondary bus of the main transformer, reactive power flow through the main transformer and real power loss on feeders. When performing the Volt/Var control problem in conventional methods, the hourly load and wind speed must be forecasted to prevent errors. However, actually there are always errors in these forecasted values. A characteristic feature of the proposed fuzzy optimization approach is that the forecast hourly load and wind speed errors can be taken into account using fuzzy sets. Fuzzy set notations in the load demand, wind speed, voltage deviation on the secondary bus, reactive power flow through the main transformer and total real power loss on feeders are developed to obtain the optimal dispatching schedule under an uncertain environment. To demonstrate the effectiveness of the proposed method, the Volt/Var control problem is performed in a distribution system within the service area of Yunlin District Office of Taiwan Power Company (TPC). The results show that a proper dispatching schedule for ULTC position and capacitor switching operation can be reached using the proposed method.     

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.     

Control of a DC/DC converter by fuzzy controller for a solar pumping system

The exploitation of the solar energy is very significant for the very sunny countries. Moreover the dryness phenomenon in these country is imposes more and more the use of pumping plants. The storage of the water in insulated basins from the wells has a double advantage. On the one hand, it is a technical storage solution of the solar energy collected by the photovoltaic panels. On the other hand, it is a hygienic way out to supply water for the rural population.In our work, we propose a technique for the identification of the maximum power point (MPP) based on fuzzy logic. This method is used to generate the cyclic ratio to operate the switcher within the maximum power of a photovoltaic array (PVA).For simulation purpose we made a complete modeling of the entire system. The system carried out consists of a photovoltaic array supplying, through a DC converter, a direct current (DC) engine coupled to a centrifugal pump. Our experimental bench consists of two principal units. A DC converter module composed of IGBT power transistors. And a processing module connected to a PC serial port, handling the input signals delivered by photovoltaic generator and controlling the power unit.The obtained experimental results confirm the simulation result which is very satisfactory and show the utility of the fuzzy controller for the optimization of the system.     

Optimal power flow by a fuzzy based hybrid particle swarm optimization approach

This paper presents a fuzzy based hybrid particle swarm optimization (PSO) approach for solving the optimal power flow (OPF) problem with uncertainties. Wind energy systems are being considered in the study power systems. OPF is an optimization problem which minimizes the total thermal unit fuel cost, total emission, and total real power loss while satisfying physical and technical constraints on the network. When performing the OPF problem in conventional methods, the load demand and wind speed must be forecasted to prevent errors. However, actually there are always errors in these forecasted values. A characteristic feature of the proposed fuzzy based hybrid PSO method is that the forecast load demand and wind speed errors can be taken into account using fuzzy sets. Fuzzy set notations in the load demand, wind speed, total fuel cost, total emission, and total real power loss are developed to obtain the optimal setting under an uncertain environment. To demonstrate the effectiveness of the proposed method, the OPF problem is performed on the IEEE 30- and 118-Bus test systems.