Optimization algorithms for steady state analysis of self excited induction generator

The current publication is directed to evaluate the steady state performance of three-phase self-excited induction generator (SEIG) utilizing particle swarm optimization (PSO), grey wolf optimization (GWO), wale optimization algorithm (WOA), genetic algorithm (GA), and three MATLAB optimization functions (fminimax, fmincon, fminunc). The behavior of the output voltage and frequency under a vast range of variation in the load, rotational speed and excitation capacitance is examined for each optimizer. A comparison made shows that the most accurate results are obtained with GA followed by GWO. Consequently, GA optimizer can be categorized as the best choice to analyze the generator under various conditions

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Copyright © 2014 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in Environmental Modelling and Software. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Environmental Modelling and Software Vol. 62 (2014), DOI: 10.1016/j.envsoft.2014.09.013The development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g. model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Abstract not availableH.R. Maier, Z. Kapelan, Kasprzyk, J. Kollat, L.S. Matott, M.C. Cunha, G.C. Dandy, M.S. Gibbs, E. Keedwell, A. Marchi, A. Ostfeld, D. Savic, D.P. Solomatine, J.A. Vrugt, A.C. Zecchin, B.S. Minsker, E.J. Barbour, G. Kuczera, F. Pasha, A. Castelletti, M. Giuliani, P.M. Ree

Spatially optimised sustainable urban development

PhD ThesisTackling urbanisation and climate change requires more sustainable and resilient cities, which in turn will require planners to develop a portfolio of measures to manage climate risks such as flooding, meet energy and greenhouse gas reduction targets, and prioritise development on brownfield sites to preserve greenspace. However, the policies, strategies and measures put in place to meet such objectives can frequently conflict with each other or deliver unintended consequences, hampering long-term sustainability. For example, the densification of cities in order to reduce transport energy use can increase urban heat island effects and surface water flooding from extreme rainfall events. In order to make coherent decisions in the presence of such complex multi-dimensional spatial conflicts, urban planners require sophisticated planning tools to identify and manage potential trade-offs between the spatial strategies necessary to deliver sustainability. To achieve this aim, this research has developed a multi-objective spatial optimisation framework for the spatial planning of new residential development within cities. The implemented framework develops spatial strategies of required new residential development that minimize conflicts between multiple sustainability objectives as a result of planning policy and climate change related hazards. Five key sustainability objectives have been investigated, namely; (i) minimizing risk from heat waves, (ii) minimizing the risk from flood events, (iii) minimizing travel costs in order to reduce transport emissions, (iv) minimizing urban sprawl and (v) preventing development on existing greenspace. A review identified two optimisation algorithms as suitable for this task. Simulated Annealing (SA) is a traditional optimisation algorithm that uses a probabilistic approach to seek out a global optima by iteratively assessing a wide range of spatial configurations against the objectives under consideration. Gradual ‘cooling’, or reducing the probability of jumping to a different region of the objective space, helps the SA to converge on globally optimal spatial patterns. Genetic Algorithms (GA) evolve successive generations of solutions, by both recombining attributes and randomly mutating previous generations of solutions, to search for and converge towards superior spatial strategies. The framework works towards, and outputs, a series of Pareto-optimal spatial plans that outperform all other plans in at least one objective. This approach allows for a range of best trade-off plans for planners to choose from. ii Both SA and GA were evaluated for an initial case study in Middlesbrough, in the North East of England, and were able to identify strategies which significantly improve upon the local authority’s development plan. For example, the GA approach is able to identify a spatial strategy that reduces the travel to work distance between new development and the central business district by 77.5% whilst nullifying the flood risk to the new development. A comparison of the two optimisation approaches for the Middlesbrough case study revealed that the GA is the more effective approach. The GA is more able to escape local optima and on average outperforms the SA by 56% in in the Pareto fronts discovered whilst discovering double the number of multi-objective Pareto-optimal spatial plans. On the basis of the initial Middlesbrough case study the GA approach was applied to the significantly larger, and more computationally complex, problem of optimising spatial development plans for London in the UK – a total area of 1,572km2. The framework identified optimal strategies in less than 400 generations. The analysis showed, for example, strategies that provide the lowest heat risk (compared to the feasible spatial plans found) can be achieved whilst also using 85% brownfield land to locate new development. The framework was further extended to investigate the impact of different development and density regulations. This enabled the identification of optimised strategies, albeit at lower building density, that completely prevent any increase in urban sprawl whilst also improving the heat risk objective by 60% against a business as usual development strategy. Conversely by restricting development to brownfield the ability of the spatial plan to optimise future heat risk is reduced by 55.6% against the business as usual development strategy. The results of both case studies demonstrate the potential of spatial optimisation to provide planners with optimal spatial plans in the presence of conflicting sustainability objectives. The resulting diagnostic information provides an analytical appreciation of the sensitivity between conflicts and therefore the overall robustness of a plan to uncertainty. With the inclusion of further objectives, and qualitative information unsuitable for this type of analysis, spatial optimization can constitute a powerful decision support tool to help planners to identify spatial development strategies that satisfy multiple sustainability objectives and provide an evidence base for better decision making

Spatial optimisation for resilient infrastructure services

Ph. D. Thesis.Infrastructure networks provide crucial services to the functioning of human settlements. Extreme weather events, especially flooding, can lead to disruption or complete loss of these crucial infrastructure services, which can have significant impacts on people’s health and wellbeing, as well as being costly to repair. Urban areas concentrate infrastructure and people, and are consequently particularly sensitive to disruptions due to natural (and human-made) disasters. Flooding alone constituted 47% of all weather-related disasters between 1995 and 2015, causing enormous loss of lives and economic damages. Climate change is projected to further exacerbate the impacts that natural disasters have on cities. Choices about where to site infrastructure have a significant impact on the impacts of extreme weather events. For example, investments in flood risk management have typically focussed on prioritising interventions to protect people, houses and businesses. Protection of infrastructure services has either been a bonus benefit of flood defence protection of property, or been implemented by individual infrastructure operators. Spatial planning is a key process to influence the distribution of people and activities over broad spatial scales. However, decision-making processes to locate infrastructure services does not typically consider resilience issues at broad spatial scales which can lead to inefficient use of resources. Moreover, spatial planning typically requires consideration of multiple, sometimes competing, objectives with solutions that are not readily tractable. Balancing multiple trade-offs in spatial planning with multiple variables at high spatial resolution is computationally demanding. This research has developed a new framework for multi-objective Pareto-optimal location-allocation problems solving. The RAO (Resource Allocation Optimisation) framework developed here is a heuristic approach that makes use of a Genetic Algorithm (GA) to produce Pareto-optimal spatial plans that balance a typical tradeoff in spatial planning: the maximisation of accessibility of a given infrastructure service vs the minimisation of the costs of providing that service. The method is applied to two case studies: (i) Storage of temporary flood defences, and (ii) Location of healthcare facilities. The RAO is first applied to a flood risk management case study in the Humber Estuary, UK, to optimise the strategic allocation of storing space for emergency resources (like temporary flood barriers, portable generators, pumps etc.) by maximising the accessibility of warehouses (i.e. minimising travel times from storing locations to deployment sites) and minimising costs. The evaluation of costs involves both capital and operational costs such as the length of temporary defences needed, storage site locations, number of lorries and personnel to enable their deployment, and maintenance costs. A baseline is tested against a number of scenarios, including a flood disrupting road network and thereby deployment operations, as well as variable infrastructure and land use costs, different transportation and deployment strategies and changing the priority of protecting different critical infrastructures. Key findings show investment in strategically located warehouses decreases deployment time across the whole region by several hours, while prioritising the protection of the infrastructure assets serving larger shares of population can cut costs by 30%. Moreover, the analysis of the ensemble of all scenarios provides crucial insights for spatial planners. For example, storage sites in Hull or Hedon, and in the areas of Withernsea and Drax are robust choices under all scenarios. Meanwhile, the Humber Bridge is shown to play a crucial role in enabling regional coverage of temporary barriers. The second case study shows how emergency response strategies can be enhanced by optimal allocation of healthcare facilities at a regional scale. The RAO framework allocates healthcare facilities in Northland (New Zealand) balancing the trade-off between maximisation of accessibility (i.e. minimisation of travel times between households and GP clinics) and minimisation of costs (i.e. number of clinics and doctors). Results show how c.80% of Northland’s population lives within a 20 minutes drive from the closest GP, but this can be increased to 90% with strategic investment and relocation of doctors and clinics. By accounting for flood and landslide risk, the RAO is used to identify strategies that improve accessibility to healthcare services by up to 5% even during extreme events (when compared to the current business as usual service accessibility). Application to these two problems demonstrates that the RAO framework can identify optimal strategies to deploy finite resources to maximise the resilience of infrastructure services. Moreover, it provides an analytical appreciation of the sensitivity between planning tradeoffs and therefore the overall robustness of a strategy to uncertainty. The method is consequently of benefit to local authorities, infrastructure operators and agencies responsible for disaster management. Following successful application to regional scale case studies, it is recommended that future work scale the analysis to consider resource allocation to protect infrastructure at a national scaleEngineering and Physical Sciences Research Counci

Comparative Evaluation of Generalized River/Reservoir System Models

This report reviews user-oriented generalized reservoir/river system models. The terms reservoir/river system, reservoir system, reservoir operation, or river basin management "model" or "modeling system" are used synonymously to refer to computer modeling systems that simulate the storage, flow, and diversion of water in a system of reservoirs and river reaches. Generalized means that a computer modeling system is designed for application to a range of concerns dealing with river basin systems of various configurations and locations, rather than being site-specific customized to a particular system. User-oriented implies the modeling system is designed for use by professional practitioners (model-users) other than the original model developers and is thoroughly tested and well documented. User-oriented generalized modeling systems should be convenient to obtain, understand, and use and should work correctly, completely, and efficiently. Modeling applications often involve a system of several simulation models, utility software products, and databases used in combination. A reservoir/river system model is itself a modeling system, which often serves as a component of a larger modeling system that may include watershed hydrology and river hydraulics models, water quality models, databases and various software tools for managing time series, spatial, and other types of data. Reservoir/river system models are based on volume-balance accounting procedures for tracking the movement of water through a system of reservoirs and river reaches. The model computes reservoir storage contents, evaporation, water supply withdrawals, hydroelectric energy generation, and river flows for specified system operating rules and input sequences of stream inflows and net evaporation rates. The hydrologic period-of-analysis and computational time step may vary greatly depending on the application. Storage and flow hydrograph ordinates for a flood event occurring over a few days may be determined at intervals of an hour or less. Water supply capabilities may be modeled with a monthly time step and several decade long period-of-analysis capturing the full range of fluctuating wet and dry periods including extended drought. Stream inflows are usually generated outside of the reservoir/river system model and provided as input to the model. However, reservoir/river system models may also include capabilities for modeling watershed precipitation-runoff processes to generate inflows to the river/reservoir system. Some reservoir/river system models simulate water quality constituents along with water quantities. Some models include features for economic evaluation of system performance based on cost and benefit functions expressed as a function of flow and storage

Full Proceedings, 2018

Full conference proceedings for the 2018 International Building Physics Association Conference hosted at Syracuse University