407 research outputs found

    A framework for traffic flow survivability in wireless networks prone to multiple failures and attacks

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    Transmitting packets over a wireless network has always been challenging due to failures that have always occurred as a result of many types of wireless connectivity issues. These failures have caused significant outages, and the delayed discovery and diagnostic testing of these failures have exacerbated their impact on servicing, economic damage, and social elements such as technological trust. There has been research on wireless network failures, but little on multiple failures such as node-node, node-link, and link–link failures. The problem of capacity efficiency and fast recovery from multiple failures has also not received attention. This research develops a capacity efficient evolutionary swarm survivability framework, which encompasses enhanced genetic algorithm (EGA) and ant colony system (ACS) survivability models to swiftly resolve node-node, node-link, and link-link failures for improved service quality. The capacity efficient models were tested on such failures at different locations on both small and large wireless networks. The proposed models were able to generate optimal alternative paths, the bandwidth required for fast rerouting, minimized transmission delay, and ensured the rerouting path fitness and good transmission time for rerouting voice, video and multimedia messages. Increasing multiple link failures reveal that as failure increases, the bandwidth used for rerouting and transmission time also increases. This implies that, failure increases bandwidth usage which leads to transmission delay, which in turn slows down message rerouting. The suggested framework performs better than the popular Dijkstra algorithm, proactive, adaptive and reactive models, in terms of throughput, packet delivery ratio (PDR), speed of transmission, transmission delay and running time. According to the simulation results, the capacity efficient ACS has a PDR of 0.89, the Dijkstra model has a PDR of 0.86, the reactive model has a PDR of 0.83, the proactive model has a PDR of 0.83, and the adaptive model has a PDR of 0.81. Another performance evaluation was performed to compare the proposed model's running time to that of other evaluated routing models. The capacity efficient ACS model has a running time of 169.89ms on average, while the adaptive model has a running time of 1837ms and Dijkstra has a running time of 280.62ms. With these results, capacity efficient ACS outperforms other evaluated routing algorithms in terms of PDR and running time. According to the mean throughput determined to evaluate the performance of the following routing algorithms: capacity efficient EGA has a mean throughput of 621.6, Dijkstra has a mean throughput of 619.3, proactive (DSDV) has a mean throughput of 555.9, and reactive (AODV) has a mean throughput of 501.0. Since Dijkstra is more similar to proposed models in terms of performance, capacity efficient EGA was compared to Dijkstra as follows: Dijkstra has a running time of 3.8908ms and EGA has a running time of 3.6968ms. In terms of running time and mean throughput, the capacity efficient EGA also outperforms the other evaluated routing algorithms. The generated alternative paths from these investigations demonstrate that the proposed framework works well in preventing the problem of data loss in transit and ameliorating congestion issue resulting from multiple failures and server overload which manifests when the process hangs. The optimal solution paths will in turn improve business activities through quality data communications for wireless service providers.School of ComputingPh. D. (Computer Science

    Evolutionary Computation 2020

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    Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

    Weed/Plant Classification Using Evolutionary Optimised Ensemble Based On Local Binary Patterns

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    This thesis presents a novel pixel-level weed classification through rotation-invariant uniform local binary pattern (LBP) features for precision weed control. Based on two-level optimisation structure; First, Genetic Algorithm (GA) optimisation to select the best rotation-invariant uniform LBP configurations; Second, Covariance Matrix Adaptation Evolution Strategy (CMA-ES) in the Neural Network (NN) ensemble to select the best combinations of voting weights of the predicted outcome for each classifier. The model obtained 87.9% accuracy in CWFID public benchmark

    Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems

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    The electrical power system is undergoing a revolution enabled by advances in telecommunications, computer hardware and software, measurement, metering systems, IoT, and power electronics. Furthermore, the increasing integration of intermittent renewable energy sources, energy storage devices, and electric vehicles and the drive for energy efficiency have pushed power systems to modernise and adopt new technologies. The resulting smart grid is characterised, in part, by a bi-directional flow of energy and information. The evolution of the power grid, as well as its interconnection with energy storage systems and renewable energy sources, has created new opportunities for optimising not only their techno-economic aspects at the planning stages but also their control and operation. However, new challenges emerge in the optimization of these systems due to their complexity and nonlinear dynamic behaviour as well as the uncertainties involved.This volume is a selection of 20 papers carefully made by the editors from the MDPI topic “Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems”, which was closed in April 2022. The selected papers address the above challenges and exemplify the significant benefits that optimisation and nonlinear control techniques can bring to modern power and energy systems

    Performance Optimisation of Standalone and Grid Connected Microgrid Clusters

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    Remote areas usually supplied by isolated electricity systems known as microgrids which can operate in standalone and grid-connected mode. This research focus on reliable operation of microgrids with minimal fuel consumption and maximal renewables penetration, ensuring least voltage and frequency deviations. These problems can be solved by an optimisation-based technique. The objective function is formulated and solved with a Genetic Algorithm approach and performance of the proposal is evaluated by exhaustive numerical analyses in Matlab

    Graph-Transfromational Swarms : A Graph-Transformational Approach to Swarm Computation

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    Computer systems are becoming increasingly distributed and interconnected. Various emerging notions, such as smart grids, system of systems, industry 4.0 or cyber-physical systems have gained more and more importance during the last few years. All of them propose to solve engineering problems by using several autonomous components that act in parallel and are interconnected, foremost using Internet technologies. These emerging concepts look very promising, but also exhibit various technical challenges. For instance, how is it possible to develop decentralized control mechanisms that produce a desired emerging behavior to solve a given task or how to model such solutions in order to analyze their behavior in terms of complexity and correctness? These are two major questions that this thesis attempts to answer. Indeed, it provides graph-transformational swarms as a novel concept that combines the ideas and principles of swarms and swarm computing and the formal methods of graph transformation to model distributed systems. Graph-transformational swarms captures the advantages of swarms and swarm computing and of graph transformation

    Optimisation of hedging-integrated rule curves for reservoir operation

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    Reservoir managers use operational rule curves as guides for managing and operating reservoir systems. However, this approach saves no water for impending droughts, resulting in large shortages during such droughts. This problem can be tempered by integrating hedging with the rule curves to curtail the water releases during normal periods of operation and use the saved water to limit the amount and impact of water shortages during droughts. However, determining the timing and amount of hedging is a challenge. This thesis presents the application of genetic algorithms (GA) for the optimisation of hedging-integrated reservoir rule curves. However, due to the challenge of establishing the boundary of feasible region in standard GA (SGA), a new development of the GA i.e. the dynamic GA (DGA), is proposed. Both the new development and its hedging policies were tested through extensive simulations of the Ubonratana reservoir (Thailand). The first observation was that the new DGA was faster and more efficient than the SGA in arriving at an optimal solution. Additionally, the derived hedging policies produced significant changes in reservoir performance when compared to no-hedging policies. The performance indices analysed were reliability (time and volume), resilience, vulnerability and sustainability; the results showed that the vulnerability (i.e. average single periods shortage) in particular was significantly reduced with the optimised hedging rules as compared to using the no-hedging rule curves. This study also developed a monthly inflow forecasting model using artificial neural networks (ANN) to aid reservoir operational decision-making. Extensive testing of the model showed that it was able to provide inflow forecasts with reasonable accuracy. The simulated effect on reservoir performance of forecasted inflows vis-à-vis other assumed reservoir inflow knowledge situations showed that the ANN forecasts were superior, further reinforcing the importance of good inflow information for reservoir operation. The ability of hedging to harness the inherent buffering capacity of existing water resources systems for tempering water shortage (or vulnerability) without the need for expensive new-builds is a major outcome of this study. Although applied to Ubonratana, the study has utility for other regions of the world, where e.g. climate and other environmental changes are stressing the water availability situation

    Improved methodologies for security of electricity supply of future power system

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    The security of electricity supply has always been important, but it has recently become one of the critical issues for the planning and operation of modern electricity networks. There are several reasons for that, including increased demands and deregulation of electricity markets, resulting in much lower infrastructural investments, which both pushed existing networks to operate closer to their security limits. The increasing penetration levels of variable and inherently non-dispatchable renewable energy resource, as well as the implementation of demand-responsive controls and technologies on the demand side, together with the application of real-time thermal ratings for system components, have introduced an unprecedented level of uncertainties into the system operation. These uncertainties present genuinely new challenges for the maintenance of high system security levels. The first contribution of this thesis is the development of advanced computational tools to strengthen the decision-making capabilities of system operators and ensure secure and economic operation under high uncertainty levels. It initially evaluates the hosting capacities for wind-based generation in a distribution network subject to operational security limits. In order to analyse the impacts of variations and uncertainties in the wind-based generation, loads and dynamic thermal ratings of network components, both deterministic and probabilistic approaches are applied for hosting capacity assessment at each bus, denoted as “locational hosting capacity”, which is of interest to distributed generation (DG) developers. Afterwards, the locational hosting capacities are used to determine the hosting capacity of the whole network, denoted as “network hosting capacity”, which is of primary interest to system operators. As the available hosting capacities change after the connection of any DG units, a sensitivity analysis is implemented to calculate the variations of the remaining hosting capacity for any number of DG units connected at arbitrary network buses. The second contribution of this thesis is a novel optimisation model for the active management of networks with a high amount of wind-based generation and utilisation of dynamic thermal ratings, which employs both probabilistic analysis and interval/affine arithmetic for a comprehensive evaluation of related uncertainties. Affine arithmetic is applied to deal with interval information, where the obtained interval solutions cover the full range of possible optimal solutions, with all realisations of uncertain variables. However, the interval solutions overlook the probabilistic characteristics of uncertainties, e.g. a likely very low probabilities around the edges of intervals. In order to consider realistic probability distribution information and to reduce overestimation errors, the affine arithmetic approach is combined with probabilistic (Monte Carlo) based analysis, to identify the suitable ranges of uncertainties for optimal balancing of risks and costs. Finally, this thesis proposes a general multi-stage framework for efficient management of post-contingency congestions and constraint violations. This part of the work uses developed thermal models of overhead lines and transformers to calculate the maximum lead time for system operators to resolve constraint violations caused by post-fault contingency events. The maximum lead time is integrated into the framework as the additional constraint, to support the selection of the most effective corrective actions. The framework has three stages, in which the optimal settings for volt-var controls, generation re-dispatch and load shedding are determined sequentially, considering their response times. The proposed framework is capable of mitigating severe constraint violations while preventing overheating and overloading conditions during the congestion management process. In addition, the proposed framework also considers the costs of congestion management actions so that the effective corrective actions can be selected and evaluated both technically and economically

    Transmissionmanagementforcongestedpowersystem:Areviewof concepts,technicalchallengesanddevelopmentofanewmethodology

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    Transmissionnetworkshavesomeconstraintsthatshouldbeaddressedinordertoensuresufficient\ud control tomaintainthesecuritylevelofapowersystemwhilemaximisingmarketefficiency.Themost\ud obviousdrawbackoftransmissionconstraintsisacongestionproblemthatbecomesanobstacleto\ud perfect competitionamongthemarketparticipantssinceitcaninfluence spotmarketpricing.Asthe\ud power flow violatestransmissionconstraints,redispatchinggeneratingunitsisrequiredandthiswill\ud cause thepriceateverynodetovary.Thismanuscriptpresentsconcepts,technicalchallengesand\ud methodology forinvestigatinganalternativesolutiontotheredispatchmechanismandthenformulates\ud LMP schemeusinganoptimisationtechniquethatmaywellcontrolcongestionasthemainissue.The\ud LMP schemearevariedandimprovedtotakeintoaccounttheenergyprice,congestionrevenue,costof\ud losses, aswellasthetransmissionusagetariffbyutilisingshiftfactor-basedoptimalpower flow\ud (SF-OPF), whichisderivedfromthewell-knownDCoptimalpower flow(DC-OPF)mode

    Experimental investigation and modelling of the heating value and elemental composition of biomass through artificial intelligence

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    Abstract: Knowledge advancement in artificial intelligence and blockchain technologies provides new potential predictive reliability for biomass energy value chain. However, for the prediction approach against experimental methodology, the prediction accuracy is expected to be high in order to develop a high fidelity and robust software which can serve as a tool in the decision making process. The global standards related to classification methods and energetic properties of biomass are still evolving given different observation and results which have been reported in the literature. Apart from these, there is a need for a holistic understanding of the effect of particle sizes and geospatial factors on the physicochemical properties of biomass to increase the uptake of bioenergy. Therefore, this research carried out an experimental investigation of some selected bioresources and also develops high-fidelity models built on artificial intelligence capability to accurately classify the biomass feedstocks, predict the main elemental composition (Carbon, Hydrogen, and Oxygen) on dry basis and the Heating value in (MJ/kg) of biomass...Ph.D. (Mechanical Engineering Science
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