The dual network theorem for static flow networks and its application for maximising the throughput flow

The paper discuses a new fundamental result in the theory of flow networks referred to as the ‘dual network theorem forstatic flow networks’. The theorem states that the maximum throughput flow in any static network is equal to the sum ofthe capacities of the edges coming out of the source, minus the total excess flow at all excess nodes, plus the maximumthroughput flow in the dual network. For very few imbalanced nodes in a flow network, determining the throughput flowin the dual network is a task significantly easier than determining the throughput flow in the original network. This createsthe basis of a very efficient algorithm for maximising the throughput flow in a network, by maximising the throughputflow in its dual network.Consequently, a new algorithm for maximising the throughput flow in a network has been proposed. For networks withvery few imbalanced nodes, in the case where only the maximum throughput flow is of interest, the proposed algorithmwill outperform any classical method for determining the maximum throughput flow.In this paper we also raise awareness of a fundamental flaw in classical algorithms for maximising the throughput flow instatic networks with directed edges. Despite the years of intensive research on static flow networks, the classicalalgorithms leave undesirable directed loops of flow in the optimised networks. These directed flow loops are associatedwith wastage of energy and resources and increased levels of congestion in the optimised networks. Consequently, analgorithm is also proposed for discovering and removing directed loops of flow in networks

Utilisation of transformer condition monitoring data

Electricity grids are getting older and demand of electricity is rising. The critical com-ponents in electricity transmission systems should be monitored for assessing the need for maintenance. The electricity grid works more reliable when the condition infor-mation of important components are available continuously and thus larger catastrophic failures are preventable. Transformers are one of the critical components in electricity transmission. It is im-portant that they operate continuously. Transformers are reliable and long life compo-nents but the older the transformer is, the more sensitive it is about to fail. Condition monitoring provides improved data on the condition of transformer. With on-line condi-tion monitoring it is possible to detect developing failures and then a corrective action can be made in time. This study focuses on the utilization of transformer condition monitoring system in tra-ditional grid and in upcoming smart grid. The aim is to find out, where the condition monitoring data is needed in electricity transmission and distribution system manage-ment and how it is possible to carry the information to right place. This thesis introduces first the basics of a power system, the construction of a trans-former, transformer condition monitoring methods and condition monitoring data pro-cess. After that the management of a power system within traditional and smart grid is analyzed. The asset management process of both type power systems is explored through case study of transformer failure situations. In traditional power system the transformer maintenance bases mostly on time scheduled inspections. In smart grid the management is all time aware on the condition information of transformers which al-lows using of better fault prevention strategies.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

The throughput flow constraint theorem and its applications

The paper states and proves an important result related to the theory of flow networks with disturbed flows:“the throughput flow constraint in any network is always equal to the throughput flow constraint in its dual network”. After the failure or congestion of several edges in the network, the throughput flow constraint theorem provides the basis of a very efficient algorithm for determining the edge flows which correspond to the optimal throughput flow from sources to destinations which is the throughput flow achieved with the smallest amount of generation shedding from the sources. In the case where a failure of an edge causes a loss of the entire flow through the edge, the throughput flow constraint theorem permits the calculation of the new maximum throughput flow to be done in time, where m is the number of edges in the network.In this case, the new maximum throughput flow is calculated by inspecting the network only locally, in the vicinity of the failed edge, without inspecting the rest of the network. The superior average running time of the presented algorithm, makes it particularly suitable for decongesting overloaded transmission links of telecommunication networks, in real time.In the paper, it is also shown that the deliberate choking of flows along overloaded edges, leading to a generation of momentary excess and deficit flow, provides a very efficient mechanism for decongesting overloaded branches

Systems Engineering: Availability and Reliability

Current trends in Industry 4.0 are largely related to issues of reliability and availability. As a result of these trends and the complexity of engineering systems, research and development in this area needs to focus on new solutions in the integration of intelligent machines or systems, with an emphasis on changes in production processes aimed at increasing production efficiency or equipment reliability. The emergence of innovative technologies and new business models based on innovation, cooperation networks, and the enhancement of endogenous resources is assumed to be a strong contribution to the development of competitive economies all around the world. Innovation and engineering, focused on sustainability, reliability, and availability of resources, have a key role in this context. The scope of this Special Issue is closely associated to that of the ICIE’2020 conference. This conference and journal’s Special Issue is to present current innovations and engineering achievements of top world scientists and industrial practitioners in the thematic areas related to reliability and risk assessment, innovations in maintenance strategies, production process scheduling, management and maintenance or systems analysis, simulation, design and modelling

Variable selection for wind turbine condition monitoring and fault detection system

With the fast growth in wind energy, the performance and reliability of the wind power generation system has become a major issue in order to achieve cost-effective generation. Integration of condition monitoring system (CMS) in the wind turbine has been considered as the most viable solution, which enhances maintenance scheduling and achieving a more reliable system. However, for an effective CMS, large number of sensors and high sampling frequency are required, resulting in a large amount of data to be generated. This has become a burden for the CMS and the fault detection system. This thesis focuses on the development of variable selection algorithm, such that the dimensionality of the monitoring data can be reduced, while useful information in relation to the later fault diagnosis and prognosis is preserved. The research started with a background and review of the current status of CMS in wind energy. Then, simulation of the wind turbine systems is carried out in order to generate useful monitoring data, including both healthy and faulty conditions. Variable selection algorithms based on multivariate principal component analysis are proposed at the system level. The proposed method is then further extended by introducing additional criterion during the selection process, where the retained variables are targeted to a specific fault. Further analyses of the retained variables are carried out, and it has shown that fault features are present in the dataset with reduced dimensionality. Two detection algorithms are then proposed utilising the datasets obtained from the selection algorithm. The algorithms allow accurate detection, identification and severity estimation of anomalies from simulation data and supervisory control and data acquisition data from an operational wind farm. Finally an experimental wind turbine test rig is designed and constructed. Experimental monitoring data under healthy and faulty conditions is obtained to further validate the proposed detection algorithms

Multi-objective optimisation methods applied to complex engineering systems

This research proposes, implements and analyses a novel framework for multiobjective optimisation through evolutionary computing aimed at, but not restricted to, real-world problems in the engineering design domain. Evolutionary algorithms have been used to tackle a variety of non-linear multiobjective optimisation problems successfully, but their success is governed by key parameters which have been shown to be sensitive to the nature of the particular problem, incorporating concerns such as the number of objectives and variables, and the size and topology of the search space, making it hard to determine the best settings in advance. This work describes a real-encoded multi-objective optimising evolutionary algorithm framework, incorporating a genetic algorithm, that uses self-adaptive mutation and crossover in an attempt to avoid such problems, and which has been benchmarked against both standard optimisation test problems in the literature and a real-world airfoil optimisation case. For this last case, the minimisation of drag and maximisation of lift coefficients of a well documented standard airfoil, the framework is integrated with a freeform deformation tool to manage the changes to the section geometry, and XFoil, a tool which evaluates the airfoil in terms of its aerodynamic efficiency. The performance of the framework on this problem is compared with those of two other heuristic MOO algorithms known to perform well, the Multi-Objective Tabu Search (MOTS) and NSGA-II, showing that this framework achieves better or at least no worse convergence. The framework of this research is then considered as a candidate for smart (electricity) grid optimisation. Power networks can be improved in both technical and economical terms by the inclusion of distributed generation which may include renewable energy sources. The essential problem in national power networks is that of power flow and in particular, optimal power flow calculations of alternating (or possibly, direct) current. The aims of this work are to propose and investigate a method to assist in the determination of the composition of optimal or high-performing power networks in terms of the type, number and location of the distributed generators, and to analyse the multi-dimensional results of the evolutionary computation component in order to reveal relationships between the network design vector elements and to identify possible further methods of improving models in future work. The results indicate that the method used is a feasible one for the achievement of these goals, and also for determining optimal flow capacities of transmission lines connecting the bus bars in the network

Best Environmental Management Practice in the Telecommunications and ICT Services sector: Learning from front runners

The steady growth over the past decades of the Telecommunications and ICT Services sector, and its uninterrupted progress with the constant provision of renewed and ever-faster services as well as new applications, has transformed many aspects of our society and lives but has also spurred the development of ever more power- and resource-hungry systems, contributing to the sector’s ever-growing environmental footprint. On the basis of an in-depth analysis of the actions implemented by environmental front runners and of existing EU and industry initiatives addressing the environmental performance of the sector, this report describes a set of best practices with high potential for larger uptake. These are called Best Environmental Management Practices (BEMPs). The BEMPs, identified in close cooperation with a technical working group comprising experts from the sector, cover improvement of environmental performance across all significant environmental aspects (energy consumption, resource consumption, etc.) at the different life cycle stages (planning and design, installation, operation, end-of-life management, etc.) and for different ICT assets (software, data centres, etc.). Besides actions aimed at reducing the environmental impact of Telecommunications and ICT Services operations (with a special focus on data centres and telecommunications networks), the report also identifies best practices in the ICT sector that contribute towards reducing the environmental impact of other sectors of the economy ("greening by ICT" measures). The report gives a wide range of information (environmental benefits, economics, indicators, benchmarks, references, etc.) for each of the proposed best practices in order to be a source of inspiration and guidance for any company in the sector wishing to improve its environmental performance. In addition, it will be the technical basis for a Sectoral Reference Document on Best Environmental Management Practice for the Telecommunications and ICT Services sector, to be produced by the European Commission according to Article 46 of Regulation (EC) No 1221/2009 (EMAS Regulation).JRC.B.5-Circular Economy and Industrial Leadershi