Solar Reector Design

The design of solar panels is investigated. Different aspects of this problem are presented. A formula averaging the solar energy received on a given location is derived rst. The energy received by the collecting solar panel is then calculated using a specially designed algorithm. The geometry of the device collecting the energy may then be optimised using different algorithms. The results show that for a given depth, devices of smaller width are more energy efficient than those of wider dimensions. This leads to a more economically efficient design

Adaptive control of a solar furnace for material testing

IFAC Adaptive Systems in Control and Signal Processing. Glasgow. Scotland. UK. 26/08/1998This paper presents an adaptive control system for controlling the temperature of a solar furnace, which is a high solar concentrating facility made up of heliostats tracking the sun and reflecting solar radiation onto a static parabolic concentrating system at the focal spot of which a high percentage of the solar energy collected by the collector system is concentrated in a small area. A large attenuator (shutter) placed between the collector system and the concentrator serves to control the amount of solar energy used for heating the samples placed at the focal spot. The paper shows the results obtained in the application of adaptive PI controllers to a solar furnace, incorporating feedforward action, anti-windup and slew rate constraint handling mechanisms

Topology design and performance analysis of an integrated communication network

A research study on the topology design and performance analysis for the Space Station Information System (SSIS) network is conducted. It is begun with a survey of existing research efforts in network topology design. Then a new approach for topology design is presented. It uses an efficient algorithm to generate candidate network designs (consisting of subsets of the set of all network components) in increasing order of their total costs, and checks each design to see if it forms an acceptable network. This technique gives the true cost-optimal network, and is particularly useful when the network has many constraints and not too many components. The algorithm for generating subsets is described in detail, and various aspects of the overall design procedure are discussed. Two more efficient versions of this algorithm (applicable in specific situations) are also given. Next, two important aspects of network performance analysis: network reliability and message delays are discussed. A new model is introduced to study the reliability of a network with dependent failures. For message delays, a collection of formulas from existing research results is given to compute or estimate the delays of messages in a communication network without making the independence assumption. The design algorithm coded in PASCAL is included as an appendix

Detection of Non-Technical Losses in Smart Distribution Networks: a Review

With the advent of smart grids, distribution utilities have initiated a large deployment of smart meters on the premises of the consumers. The enormous amount of data obtained from the consumers and communicated to the utility give new perspectives and possibilities for various analytics-based applications. In this paper the current smart metering-based energy-theft detection schemes are reviewed and discussed according to two main distinctive categories: A) system statebased, and B) arti cial intelligence-based.Comisión Europea FP7-PEOPLE-2013-IT

ROLAND : a tool for the realistic optimisation of local access network design

Bibliography: p. 141-147.Investment in the local access network represents between 50% and 70% of capital investment of a telecommunications company. This thesis investigates algorithms that can be used to design economical access networks and presents ROLAND: a tool that incorporates several of these algorithms into an interactive environment. The software allows a network designer to explore different approaches to solving the problem, before adopting a particular one. The family of problems that are tackled by the algorithms included in ROLAND involve determining the most economical way of installing concentrators in an access network and connecting demand nodes such as distribution points to these concentrators. The Centre-of-Mass (COM) Algorithm identifies clusters of demand in the network and suggests good locations for concentrators to be installed. The problem of determining which concentrators in a set of potential sites to install is known as the concentrator location problem (CPL) and is an instance of the classical capacitated plant location problem. Linear programming techniques such as branch-and-bound can be used to find an optimal solution to this problem, but soon becomes infeasible as the network size increases. Some form of heuristic approach is needed, and ROLAND includes two such heuristics, namely the Add and Drop Heuristic. Determining the layout of multi-drop lines, which allow a number of demand nodes to share the same connection to a concentrator, is analogous to finding minimal spanning trees in a graph. Greedy approaches such as Kruskal's algorithm are not ideal however, and heuristics such as Esau-William's algorithm achieve better results. Kruskal's algorithm and Kershenbaum's Unified Algorithm (which encapsulates a number of heuristics) have been implemented and come bundled with ROLAND. ROLAND also includes an optimal terminal assignment algorithm for associating distribution points to concentrators. A description of ROLAND's architecture and GUI are provided. The graphical elements are kept separate from the algorithm implementations, and an interface class provides common data structures and routines for use by new algorithm implementations. A test data generator, able to create random or localized data, is also included. A new hybrid concentrator location algorithm, known as the Cluster-Add Heuristic is presented. The implementation of this algorithm is included in ROLAND, and demonstrates the ease with which new solution methods can be integrated into the tool's framework. Experimentation with the concentrator location algorithms is conducted to show the Cluster-Add Heuristic's relative performance

Geometrical Optimisation of Receivers for Concentrating Solar Thermal Systems

In concentrated solar thermal technologies, the receiver converts concentrated solar radiation into high-temperature heat. Solar receivers are commonly simulated with a stochastic integration method: Monte-Carlo ray-tracing. The optimisation of the geometry of receivers is challenging when using existing optimisation methods for two reasons: each receiver evaluation using Monte-Carlo ray-tracing requires significant computational effort and the outcome of a simulation involves uncertainty. A series of novel optimisation techniques are proposed to enable gradient-free, stochastic and multi-objective optimisation adapted to such problems. These techniques address the computational load difficulty and the challenge of conducting stochastic optimisation based on uncertain evaluations by introducing the concepts of “Progressive Monte-Carlo Evaluation (PMCE)”, “Intermediate Ray Emission Source (IRES)” and adaptive view-factor calculation. A new “Multi-Objective and Evolutionary PMCE Optimisation (MOEPMCE-O)” method is then built around PMCE to enable multi-objective geometrical optimisation of receivers. PMCE is shown to be able to reduce the computational time of a random search optimisation by more than 90% and is used in the geometrical design of a new receiver for the Australian National University SG4 dish concentrator that achieved 97.1% (±2.2%) of thermal efficiency during on-sun testing. MOE-PMCE-O is applied to a multi-objective tower receiver problem where liquid sodium is used as the receiver heat-carrier in a surround configuration heliostat field. A series of useful geometrical concepts emerge from the results, with geometrical features able to maintain high efficiency while keeping acceptable incident peak flux values with a moderate receiver total mass. Finally, a more fundamental look at the impact of the interaction of concentrating optics on the exergy of radiation available at the receiver location highlights the major role played by concentrator surface slope error in lowering the exergy in concentrated solar thermal systems and quantifies the exergy loss associated with non-ideal match between flux and surface temperature in receivers

Hierarchical Network Design Using Simulated Annealing

The hierarchical network problem is the problem of nding the least cost net-work, with nodes divided into groups, edges connecting nodes in each groups and groups ordered in a hierarchy. The idea of hierarchical networks comes from telecommunication networks where hierarchies exist. Hierarchical net-works are described and a mathematical model is proposed for a two level version of the hierarchical network problem. The problem is to determine which edges should connect nodes, and how demand is routed in the net-work. The problem is solved heuristically using simulated annealing which as a sub-algorithm uses a construction algorithm to determine edges and route the demand. Performance for dierent versions of the algorithm are reported in terms of runtime and quality of the solutions. The algorithm is able to nd solutions of reasonable quality in approximately 1 hour for networks with 100 nodes

Sensor Placement Algorithms for Process Efficiency Maximization

Even though the senor placement problem has been studied for process plants, it has been done for minimizing the number of sensors, minimizing the cost of the sensor network, maximizing the reliability, or minimizing the estimation errors. In the existing literature, no work has been reported on the development of a sensor network design (SND) algorithm for maximizing efficiency of the process. The SND problem for maximizing efficiency requires consideration of the closed-loop system, which is unlike the open-loop systems that have been considered in previous works. In addition, work on the SND problem for a large fossil energy plant such as an integrated gasification combined cycle (IGCC) power plant with CO2 capture is rare.;The objective of this research is to develop a SND algorithm for maximizing the plant performance using criteria such as efficiency in the case of an estimator-based control system. The developed algorithm will be particularly useful for sensor placement in IGCC plants at the grassroots level where the number, type, and location of sensors are yet to be identified. In addition, the same algorithm can be further enhanced for use in retrofits, where the objectives could be to upgrade (addition of more sensors) and relocate existing sensors to different locations. The algorithms are developed by considering the presence of an optimal Kalman Filter (KF) that is used to estimate the unmeasured and noisy measurements given the process model and a set of measured variables. The designed algorithms are able to determine the location and type of the sensors under constraints on budget and estimation accuracy. In this work, three SND algorithms are developed: (a) steady-state SND algorithm, (b) dynamic model-based SND algorithm, and (c) nonlinear model-based SND algorithm. These algorithms are implemented in an acid gas removal (AGR) unit as part of an IGCC power plant with CO2 capture. The AGR process involves extensive heat and mass integration and therefore, is very suitable for the study of the proposed algorithm in the presence of complex interactions between process variables

A hierarchical solution approach for a multicommodity distribution problem under a special cost structure

Cataloged from PDF version of article.Motivated by the spare parts distribution system of a major automotive manufacturer in Turkey, we consider a multicommodity distribution problem from a central depot to a number of geographically dispersed demand points. The distribution of the items is carried out by a set of identical vehicles. The demand of each demand point can be satisfied by several vehicles and a single vehicle is allowed to serve multiple demand points. For a given vehicle, the cost structure is dictated by the farthest demand point from the depot among all demand points served by that vehicle. The objective is to satisfy the demand of each demand point with the minimum total distribution cost. We present a novel integer linear programming formulation of the problem as a variant of the network design problem. The resulting optimization problem becomes computationally infeasible for real-life problems due to the large number of integer variables. In an attempt to circumvent this disadvantage of using the direct formulation especially for larger problems, we propose a Hierarchical Approach that is aimed at solving the problem in two stages using partial demand aggregation followed by a disaggregation scheme. We study the properties of the solution returned by the Hierarchical Approach. We perform computational studies on a data set adapted from a major automotive manufacturer in Turkey. Our results reveal that the Hierarchical Approach significantly outperforms the direct formulation approach in terms of both the running time and the quality of the resulting solution especially on large instances. © 2012 Elsevier Ltd. All rights reserved