Simulation of sea-state sequences

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The present PhD study, in its first part, uses artificial neural networks (ANNs), an optimization technique called simulated annealing, and statistics to simulate the significant wave height (Hs) and mean zero-up-crossing period ( ) of 3-hourly sea-states of a location in the North East Pacific using a proposed distribution called hepta-parameter spline distribution for the conditional distribution of Hs or given some inputs. Two different seven- network sets of ANNs for the simulation and prediction of Hs and were trained using 20-year observed Hs’s and ’s. The preceding Hs’s and ’s were the most important inputs given to the networks, but the starting day of the simulated period was also necessary. However, the code replaced the day with the corresponding time and the season. The networks were trained by a simulated annealing algorithm and the outputs of the two sets of networks were used for calculating the parameters of the probability density function (pdf) of the proposed hepta-parameter distribution. After the calculation of the seven parameters of the pdf from the network outputs, the Hs and of the future sea-state is predicted by generating random numbers from the corresponding pdf. In another part of the thesis, vertical piles have been studied with the goal of identifying the range of sea-states suitable for the safe pile driving operation. Pile configuration including the non-linear foundation and the gap between the pile and the pile sleeve shims were modeled using the finite elements analysis facilities within ABAQUS. Dynamic analyses of the system for a sea-state characterized by Hs and and modeled as a combination of several wave components were performed. A table of safe and unsafe sea-states was generated by repeating the analysis for various sea-states. If the prediction for a particular sea-state is repeated N times of which n times prove to be safe, then it could be said that the predicted sea-state is safe with the probability of 100(n/N). The last part of the thesis deals with the Hs return values. The return value is a widely used measure of wave extremes having an important role in determining the design wave used in the design of maritime structures. In this part, Hs return value was calculated demonstrating another application of the above simulation of future 3-hourly Hs’s. The maxima method for calculating return values was applied in such a way that avoids the conventional need for unrealistic assumptions. The significant wave height return value has also been calculated using the convolution concept from a model presented by Anderson et al. (2001)

Adaptive shaping of laser beams for high-harmonic generation applications

This thesis explores the use of adaptive optics to create tailored laser profiles to drive the process of high-order harmonic generation (HHG).A deformable mirror controlled by a genetic, simulated-annealing algorithm (SA), and a genetic-annealing hybrid algorithm (HA) have been used to create super-Gaussian intensity profiles of orders ranging from P = 1 to P = 2 using a low-powered He-Ne laser. Between these three algorithms it was found that there is a compromise between the algorithm performance and reliability, and the algorithm complexity.Simulated super-Gaussian beam-shaping with a phase-only SLM has been performed with a SA and HA algorithm and compared to a known π-shift method. The HA has shown an improvement in super-Gaussian quality for high orders, P ≈ 2.6.Simulations of HHG driven by super-Gaussian driver fields have been made using both the simple dipole model and the strong field approximation. It has been shown that HHG beam divergence decreases with increased order P . The fringe visibility has also been calculated as a measure of coherence

The use of artificial intelligence techniques for power analysis

This thesis reports the research carried out into the use of Artificial Intelligence techniques for Power System Analysis. A number of aspects of Power System analysis and its management are investigated and the application of Artificial Intelligence techniques is researched. The use of software tools for checking the application of power system protection systems particularly for complex circuit arrangements was investigated. It is shown that the software provides a more accurate and efficient way of carrying out these investigations. The National Grid Company's (plc, UK) use of software tools for checking the application of protection systems is described, particularly for complex circuit arrangements such as multi-terminal circuits and composite overhead line and cable circuits. Also described, is how investigations have been made into an actual system fault that resulted in a failure of protection to operate. Techniques using digital fault records to replay a fault into a static model of protection are used in the example. The need for dynamic modelling of protection is also discussed. Work done on automating the analysis of digital fault records using computational techniques is described. An explanation is given on how a rule-based system has been developed to classify fault types and analyse the response of protection during a power system fault or disturbance in order to determine correct or incorrect operation. The development of expert systems for on-line application in Energy Control Centres (ECC), is reported. The development of expert systems is a continuous process as new knowledge is gained in the field of artificial intelligence and new expert system development tools are built. Efforts are being made for on-line application of expert systems in ECC as preventive control under normal/alert conditions and as a corrective control during a disturbance. This will enable a more secure power system operation. Considerable scope exists in the development of expert systems and their application to power system operation and control. An overview of the many different types of Neural Network has been carried out explaining terminology and methodology along with a number of techniques used for their implementation. Although the mathematical concepts are not new, many of them were recorded more than fifty years ago, the introduction of fast computers has enabled many of these concepts to be used for today's complex problems. The use of Genetic Algorithm based Artificial Neural Networks is demonstrated for Electrical Load Forecasting and the use of Self Organising Maps is explored for classifying Power System digital fault records. The background of the optimisation process carried out in this thesis is given and an introduction to the method applied, in particular Evolutionary Programming and Genetic Algorithms. Possible solutions to optimisation problems were introduced to be either local or global minimum solutions with the latter being the desirable result. The evolutionary computation that has potential to produce a global solution to a problem due to the searching mechanisms that are inherent to the procedures is discussed. Various mechanisms may be introduced to the genetic algorithm routine which may eliminate the problems of premature convergence, thus enhancing the methods' chances of producing the best solution. The other, more traditional methods of optimisation described include Lagrange multipliers, Dynamic Programming, Local Search and Simulated annealing. Only the Dynamic Programming method guarantees a global optimum solution to an optimisation problem, however for complex problems, the method could take a vast amount of time to locate a solution due to the potential for combinatorial explosion since every possible solution is considered. The Lagrange multiplier method and the local search method are useful for quick location of a global minimum and are therefore useful when the topography of the optimisation problem is uni-modal. However in a complex multi-modal problem, a global solution is less likely. The simulated annealing method has been more popular for solving complex multi-modal problems since it includes techniques for the search to avoid being trapped in local minimum solutions. Artificial Neural Network and Genetic Algorithm have been used to design a neural network for short-term load forecasting. The forecasting model has been used to produce a forecast of the load in the 24 hours of the forecast day concerned, using data provided by an Italian power company. The results obtained are promising. In this particular case, the comparison between the results from the Genetic Algorithm - Artificial Neural Network and Back Propagation - Neural Network shows that the Genetic Algorithm - Artificial Neural Network does not provide a faster solution than the Back Propagation - Neural Network. The application of Evolutionary Programming to fault section estimation is investigated and a comparison made with a Genetic Algorithm approach. To enhance service reliability and to reduce power outage, rapid restoration of power system is required. As a first step of restoration, the fault section should be accurately estimated quickly. The Fault Section Estimation (FSE) identifies fault components in a power system by using information on the operation of protection relays and circuit breakers. However this task is difficult especially for cases where the relay or circuit breaker fails to operate and for multiple faults. An Evolutionary Programming (EP) approach has been developed for solving the FSE problem including malfunctions of protection relays and/or circuit breakers and multiple fault cases. A comparison is made with the Genetic Algorithm (GA) approach at the same time. Two different population sizes are tested for each case. In general, EP showed faster computational speed than GA with an average factor of 13 times more. The final results were almost the same. The convergence speed (the required number of generations to get an optimum result) is a very important factor in real time applications. Test results show that EP is better than GA. However, as both EP and GA are evolutionary algorithms, their efficiencies are largely dependent on the complexity of the problem that might differ from case to case. The use of Artificial Neural Networks to classify digital fault records is investigated showing theat Self Organising Maps could be useful for classifying records if integrated into other systems. Digital fault records are a very useful source of information to the protection engineer to assist with the investigation of a suspected unwanted operation or failure to operate of a protection scheme. After a widespread power system disturbance, due to a storm for example, a large number of fault records can be produced. A method of automatically classifying fault records would be very helpful in reducing the amount of time spent in manual analysis, thus assisting the engineer to focus on records that need in depth analysis. Fault classification using rule base methods have already been developed. The completed work is preliminary in nature and an overview of an extension to this work, involving the extraction of frequency components from the digital fault record data and using these as input to a SOM network, is described

Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations

In recent years, a great variety of nature- and bio-inspired algorithms has been reported in the literature. This algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm. Using these taxonomies we review more than three hundred publications dealing with nature-inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper. From our analysis we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-third of the reviewed bio-inspired solvers are versions of classical algorithms. Grounded on the conclusions of our critical analysis, we give several recommendations and points of improvement for better methodological practices in this active and growing research field.Comment: 76 pages, 6 figure

Neural network based simulation of sea-state sequences

The present PhD study, in its first part, uses artificial neural networks (ANNs), an optimization technique called simulated annealing, and statistics to simulate the significant wave height (Hs) and mean zero-up-crossing period ( ) of 3-hourly sea-states of a location in the North East Pacific using a proposed distribution called hepta-parameter spline distribution for the conditional distribution of Hs or given some inputs. Two different seven- network sets of ANNs for the simulation and prediction of Hs and were trained using 20-year observed Hs’s and ’s. The preceding Hs’s and ’s were the most important inputs given to the networks, but the starting day of the simulated period was also necessary. However, the code replaced the day with the corresponding time and the season. The networks were trained by a simulated annealing algorithm and the outputs of the two sets of networks were used for calculating the parameters of the probability density function (pdf) of the proposed hepta-parameter distribution. After the calculation of the seven parameters of the pdf from the network outputs, the Hs and of the future sea-state is predicted by generating random numbers from the corresponding pdf. In another part of the thesis, vertical piles have been studied with the goal of identifying the range of sea-states suitable for the safe pile driving operation. Pile configuration including the non-linear foundation and the gap between the pile and the pile sleeve shims were modeled using the finite elements analysis facilities within ABAQUS. Dynamic analyses of the system for a sea-state characterized by Hs and and modeled as a combination of several wave components were performed. A table of safe and unsafe sea-states was generated by repeating the analysis for various sea-states. If the prediction for a particular sea-state is repeated N times of which n times prove to be safe, then it could be said that the predicted sea-state is safe with the probability of 100(n/N)%. The last part of the thesis deals with the Hs return values. The return value is a widely used measure of wave extremes having an important role in determining the design wave used in the design of maritime structures. In this part, Hs return value was calculated demonstrating another application of the above simulation of future 3-hourly Hs’s. The maxima method for calculating return values was applied in such a way that avoids the conventional need for unrealistic assumptions. The significant wave height return value has also been calculated using the convolution concept from a model presented by Anderson et al. (2001).EThOS - Electronic Theses Online ServiceGBUnited Kingdo

A Review of the Family of Artificial Fish Swarm Algorithms: Recent Advances and Applications

The Artificial Fish Swarm Algorithm (AFSA) is inspired by the ecological behaviors of fish schooling in nature, viz., the preying, swarming, following and random behaviors. Owing to a number of salient properties, which include flexibility, fast convergence, and insensitivity to the initial parameter settings, the family of AFSA has emerged as an effective Swarm Intelligence (SI) methodology that has been widely applied to solve real-world optimization problems. Since its introduction in 2002, many improved and hybrid AFSA models have been developed to tackle continuous, binary, and combinatorial optimization problems. This paper aims to present a concise review of the family of AFSA, encompassing the original ASFA and its improvements, continuous, binary, discrete, and hybrid models, as well as the associated applications. A comprehensive survey on the AFSA from its introduction to 2012 can be found in [1]. As such, we focus on a total of {\color{blue}123} articles published in high-quality journals since 2013. We also discuss possible AFSA enhancements and highlight future research directions for the family of AFSA-based models.Comment: 37 pages, 3 figure

Metaheuristics algorithms to identify nonlinear Hammerstein model: A decade survey

Metaheuristics have been acknowledged as an effective solution for many difficult issues related to optimization. The metaheuristics, especially swarm’s intelligence and evolutionary computing algorithms, have gained popularity within a short time over the past two decades. Various metaheuristics algorithms are being introduced on an annual basis and applications that are more new are gradually being discovered. This paper presents a survey for the years 2011-2021 on multiple metaheuristics algorithms, particularly swarm and evolutionary algorithms, to identify a nonlinear block-oriented model called the Hammerstein model, mainly because such model has garnered much interest amidst researchers to identify nonlinear systems. Besides introducing a complete survey on the various population-based algorithms to identify the Hammerstein model, this paper also investigated some empirically verified actual process plants results. As such, this article serves as a guideline on the fundamentals of identifying nonlinear block-oriented models for new practitioners, apart from presenting a comprehensive summary of cutting-edge trends within the context of this topic area

Feature Selection Inspired Classifier Ensemble Reduction

Classifier ensembles constitute one of the main research directions in machine learning and data mining. The use of multiple classifiers generally allows better predictive performance than that achievable with a single model. Several approaches exist in the literature that provide means to construct and aggregate such ensembles. However, these ensemble systems contain redundant members that, if removed, may further increase group diversity and produce better results. Smaller ensembles also relax the memory and storage requirements, reducing system's run-time overhead while improving overall efficiency. This paper extends the ideas developed for feature selection problems to support classifier ensemble reduction, by transforming ensemble predictions into training samples, and treating classifiers as features. Also, the global heuristic harmony search is used to select a reduced subset of such artificial features, while attempting to maximize the feature subset evaluation. The resulting technique is systematically evaluated using high dimensional and large sized benchmark datasets, showing a superior classification performance against both original, unreduced ensembles, and randomly formed subsets. ? 2013 IEEE

Artificial Intelligence Techniques for Flood Risk Management in Urban Environments

Flooding is an important concern for the UK, as evidenced by the many extreme flooding events in the last decade. Improved flood risk intervention strategies are therefore highly desirable. The application of hydroinformatics tools, and optimisation algorithms in particular, which could provide guidance towards improved intervention strategies, is hindered by the necessity of performing flood modelling in the process of evaluating solutions. Flood modelling is a computationally demanding task; reducing its impact upon the optimisation process would therefore be a significant achievement and of considerable benefit to this research area. In this thesis sophisticated multi-objective optimisation algorithms have been utilised in combination with cutting-edge flood-risk assessment models to identify least-cost and most-benefit flood risk interventions that can be made on a drainage network. Software analysis and optimisation has improved the flood risk model performance. Additionally, artificial neural networks used as feature detectors have been employed as part of a novel development of an optimisation algorithm. This has alleviated the computational time-demands caused by using extremely complex models. The results from testing indicate that the developed algorithm with feature detectors outperforms (given limited computational resources available) a base multi-objective genetic algorithm. It does so in terms of both dominated hypervolume and a modified convergence metric, at each iteration. This indicates both that a shorter run of the algorithm produces a more optimal result than a similar length run of a chosen base algorithm, and also that a full run to complete convergence takes fewer iterations (and therefore less time) with the new algorithm.EPSRCSTREAM-ID