In this study, weight optimization of two dimensional steel frames is carried out in which the flexibility of beam-to-column connections and the soil-structure interaction are considered. In the analysis and design of steel frames, beam-tocolumn connections are assumed to be either fully rigid or perfectly pinned. However, the real behavior of beam-to-column connections is actually between these extremes. Namely, even the simple connections used in practice possess some stiffness falling between these two cases mentioned above. Moreover, it is found that there exists a nonlinear relationship between the moment and beam-to-column rotation when a moment is applied to a flexible connection. These partially restrained connections influence the drift (P- effect) of whole structure as well as the moment distribution in beams and columns. Use of a direct nonlinear inelastic analysis is one way to account for all these effects in frame design. To be able to implement such analysis, beam-to-column connections should be assumed and modeled as semi-rigid connections. In the present study, beam-to-column connections are modeled as &ldquo / end plate without column stiffeners&rdquo / and &ldquo / top and seat angle with web angles&rdquo / . Soil-structure interaction is also included in the analysis. Frames are assumed to be resting on nonlinear soil, which is represented by a set of axial elements. Particle swarm optimization method is used to develop the optimum design algorithm. The Particle Swarm method is a numerical optimization technique that simulates the social behavior of birds, fishes and bugs. In nature fish school, birds flock and bugs swarm not only for reproduction but for other reasons such as finding food and escaping predators. Similar to birds seek to find food, the optimum design process seeks to find the optimum solution. In the particle swarm optimization each particle in the swarm represents a candidate solution of the optimum design problem. The design algorithm presented selects sections for the members of steel frame from the complete list of sections given in LRFD- AISC (Load and Resistance Factor Design, American Institute of Steel Construction). Besides, the design constraints are implemented from the specifications of the same code which covers serviceability and strength limitations. The optimum design algorithm developed is used to design number of rigid and semi-rigid steel frames.

Power industry restructuring has created many opportunities for customers to reduce their electricity bills. In order to facilitate the retail choice in a competitive power market, the knowledge of hourly load shape by customer class is necessary. Requiring a meter as a prerequisite for lower voltage customers to choose a power supplier is not considered practical at the present time. In order to be used by Energy Service Provider (ESP) to assign customers to specific load profiles with certainty factors, a technique which bases on load research and customers¡¦ monthly energy usage data for a preliminary screening of customer load profiles is required. Distribution systems supply electricity to different mixtures of customers, due to lack of field measurements, load point data used in distribution network studies have various degrees of uncertainties. In order to take the expected uncertainties in the demand into account, many previous methods have used fuzzy load models in their studies. However, the issue of deriving these models has not been discussed. To address this issue, an approach for building these fuzzy load models is needed. Load aggregation allows customers to purchase electricity at a lower price. In some contracts, load factor is considered as one critical aspect of aggregation. To facilitate a better load aggregation in distribution networks, feeder reconfiguration could be used to improve the load factor in a distribution subsystem. To solve the aforementioned problems, two data mining techniques, namely, the fuzzy c-means (FCM) method and an Artificial Neural Network (ANN) based pattern recognition technique, are proposed for load profiling and customer class assignment. A variant to the previous load profiling technique, customer hourly load distributions obtained from load research can be converted to fuzzy membership functions based on a possibility¡Vprobability consistency principle. With the customer class fuzzy load profiles, customer monthly power consumption and feeder load measurements, hourly loads of each distribution transformer on the feeder can be estimated and used in distribution network analysis. After feeder models are established, feeder reconfiguration based on binary particle swarm optimization (BPSO) technique is used to improve feeder load factors. Test results based on several simple sample networks have shown that the proposed feeder reconfiguration method could improve customers¡¦ position for a good bargain in electricity service.

Micro Aerial Vehicles (MAVs) are battery operated, remote controlled miniature flying vehicles. MAVs are required in military missions, traffic management, hostage situation surveillance, sensing, spying, scientific, rescue, police and mapping applications. The essential characteristics required for MAVs are: light weight, maneuverability, ease of launch in variety of conditions, ability to operate in very hostile environments, stealth capabilities and small size. There are three main classes of MAVs : fixed, rotary and flapping wing MAV’s. There are some MAVs which are combinations of these main classes. Each class has its own advantage and disadvantage. Different scenarios may call for different types of MAV. Amongst the various classes, flapping wing class of MAVs offer the required potential for miniaturisation and maneuverability, necessitating the need to understand flapping wing flight. In the case of flapping winged flight, the thrust required for the vehicle flight is obtained due to the flapping of the wing. Hence for efficient flapping flight, optimising the flap motion is necessary. In this thesis work, an algorithm for motion optimisation of plunging airfoils is developed in a parallel framework. An evolutionary optimisation algorithm, PSO (Particle Swarm Optimisation), is coupled with an unsteady flow solver to develop a generic motion optimisation tool for plunging airfoils. All the unsteady flow computations in this work are done with the HIFUN1 code, developed in–house in the Computational Aerodynamics Laboratory, IISc. This code is a cell centered finite volume compressible flow solver. The motion optimisation algorithm involves starting with a population of motion curves from which an optimal curve is evolved. Parametric representation of curves using NURBS is used for efficient handling of the motion paths. In the present case, the motion paths of a plunging NACA 0012 airfoil is optimised to give maximum flight efficiency for both inviscid and laminar cases. Also, the present analysis considers all practically achievable plunge paths, si- nusoidal and non–sinusoidal, with varying plunge amplitudes and slopes. The results show promise, and indicate that the algorithm can be extended to more realistic three dimension motion optimisation studies.

There is a growing interest in investigating the high order contingency events that may result in large blackouts, which have been a great concern for power grid secure operation. The actual number of high order contingency is too huge for operators and planner to apply a brute-force enumerative analysis. This thesis presents a heuristic searching method based on particle swarm optimization (PSO) and tabu search to select severe high order contingencies. The original PSO algorithm gives an intelligent strategy to search the feasible solution space, but tends to find the best solution only. The proposed method combines the original PSO with tabu search such that a number of top candidates will be identified. This fits the need of high order contingency screening, which can be eventually the input to many other more complicate security analyses. Reordering of branches of test system based on severity of N-1 contingencies is applied as a pre-processing to increase the convergence properties and efficiency of the algorithm. With this reordering approach, many critical high order contingencies are located in a small area in the whole searching space. Therefore, the proposed algorithm tends to concentrate in searching this area such that the number of critical branch combinations searched will increase. Therefore, the speedup ratio is found to increase significantly. The proposed algorithm is tested for N-2 and N-3 contingencies using two test systems modified from the IEEE 118-bus and 30-bus systems. Variation of inertia weight, learning factors, and number of particles is tested and the range of values more suitable for this specific algorithm is suggested. Although illustrated and tested with N-2 and N-3 contingency analysis, the proposed algorithm can be extended to even higher order contingencies but visualization will be difficult because of the increase in the problem dimensions corresponding to the order of contingencies.

The challenges in automating the design of planar mechanisms are tremendous especially in areas related to computational representation, kinematic analysis and synthesis of planar mechanisms. The challenge in computational representation relates to the development of a comprehensive methodology to completely define and manipulate the topologies of planar mechanisms while in kinematic analysis, the challenge is primarily in the development of generalized analysis routines to analyze different mechanism topologies. Combining the aforementioned challenges along with appropriate optimization algorithms to synthesize planar mechanisms for different user-defined applications presents the final challenge in the automated design of planar mechanisms. The methods presented in the literature demonstrate synthesis of standard four-bar and six-bar mechanisms with revolute and prismatic joints. But a detailed review of these methods point to the fact that they are not scalable when the topologies and the parameters of n-bar mechanisms are required to be simultaneously synthesized. Through this research, a comprehensive and scalable methodology for synthesizing different mechanism topologies and their parameters simultaneously is presented that overcomes the limitations in different challenge areas in the following ways. In representation, a graph-grammar based scheme for planar mechanisms is developed to completely describe the topology of a mechanism. Grammar rules are developed in conjunction with this representation scheme to generate different mechanism topologies in a tree-search process. In analysis, a generic kinematic analysis routine is developed to automatically analyze one-degree of freedom mechanisms consisting of revolute and prismatic joints. Two implementations of kinematic analysis have been included. The first implementation involves the use of graphical methods for position and velocity analyses and the equation method for acceleration analysis for mechanisms with a four-bar loop. The second implementation involves the use of an optimization-based method that has been developed to handle position kinematics of indeterminate mechanisms while the velocity and acceleration analyses of such mechanisms are carried out by formulating appropriate linear equations. The representation and analysis schemes are integrated to parametrically synthesize different mechanism topologies using a hybrid implementation of Particle Swarm Optimization and Nelder-Mead simplex algorithm. The hybrid implementation is able to produce better results for the problems found in the literature using a four-bar mechanism with revolute joints as well as through other higher order mechanisms from the design space. The implementation has also been tested on three new challenge problems with satisfactory results subject to computational constraints. The difficulties in the search have been studied that indicates the reasons for the lack of solution repeatability. This dissertation concludes with a discussion of the results and future directions. / text

Detecting abnormal operating conditions, which will lead to faults developing later, has important economic implications for industries trying to meet their performance and production goals. It is unacceptable to wait for failures that have potential safety, environmental and financial consequences. Moving from a “reactive” strategy to a “proactive” strategy can improve critical equipment reliability and availability while constraining maintenance costs, reducing production deferrals, decreasing the need for spare parts. Once the fault initiates, predicting its progression and deterioration can enable timely interventions without risk to personnel safety or to equipment integrity. This work presents an online-integrated condition monitoring and prognostics framework that addresses the above issues holistically. The proposed framework aligns fully with ISO 17359:2011 and derives from the I-P and P-F curve. Depending upon the running state of machine with respect to its I-P and P-F curve an algorithm will do one of the following: (1) Predict the ideal behaviour and any departure from the normal operating envelope using a combination of Evolving Clustering Method (ECM), a normalised fuzzy weighted distance and tracking signal method. (2) Identify the cause of the departure through an automated diagnostics system using a modified version of ECM for classification. (3) Predict the short-term progression of fault using a modified version of the Dynamic Evolving Neuro-Fuzzy Inference System (DENFIS), called here MDENFIS and a tracking signal method. (4) Predict the long term progression of fault (Prognostics) using a combination of Autoregressive Integrated Moving Average (ARIMA)- Empirical Mode Decomposition (EMD) for predicting the future input values and MDENFIS for predicting the long term progression of fault (output). The proposed model was tested and compared against other models in the literature using benchmarks and field data. This work demonstrates four noticeable improvements over previous methods: (1) Enhanced testing prediction accuracy, (2) comparable processing time if not better, (3) the ability to detect sudden changes in the process and finally (4) the ability to identify and isolate the problem source with high accuracy.

Η παρούσα διατριβή ασχολείται με τη μελέτη και την ανάπτυξη μοντέλων ταξινόμησης τα οποία βασίζονται στα Πιθανοτικά Νευρωνικά Δίκτυα (ΠΝΔ). Τα προτεινόμενα μοντέλα αναπτύχθηκαν ενσωματώνοντας στατιστικές μεθόδους αλλά και μεθόδους από διάφορα πεδία της Υπολογιστικής Νοημοσύνης (ΥΝ). Συγκεκριμένα, χρησιμοποιήθηκαν οι Διαφοροεξελικτικοί αλγόριθμοι βελτιστοποίησης και η Βελτιστοποίηση με Σμήνος Σωματιδίων (ΒΣΣ) για την αναζήτηση βέλτιστων τιμών των παραμέτρων των ΠΝΔ. Επιπλέον, ενσωματώθηκε η τεχνική bagging για την ανάπτυξη συστάδας μοντέλων ταξινόμησης. Μια άλλη προσέγγιση ήταν η ανάπτυξη ενός Μπεϋζιανού μοντέλου για την εκτίμηση των παραμέτρων του ΠΝΔ χρησιμοποιώντας τον δειγματολήπτη Gibbs. Επίσης, ενσωματώθηκε μια Ασαφή Συνάρτηση Συμμετοχής για την καλύτερη στάθμιση των τεχνητών νευρώνων του ΠΝΔ καθώς και ένα νέο σχήμα διάσπασης του συνόλου εκπαίδευσης σε προβλήματα ταξινόμησης πολλαπλών κλάσεων όταν ο ταξινομητής μπορεί να επιτύχει ταξινόμηση δύο κλάσεων.Τα προτεινόμενα μοντέλα ταξινόμησης εφαρμόστηκαν σε μια σειρά από πραγματικά προβλήματα από διάφορες επιστημονικές περιοχές με ενθαρρυντικά αποτελέσματα. / The present thesis is dealing with the study and the development of classification models that are based on Probabilistic Neural Networks (PNN). The proposed models were developed by the incorporation of statistical methods as well as methods from several fields of Computational Intelligence (CI) into PNNs. In particular, the Differential Evolutionary optimization algorithms and Particle Swarm Optimization algorithms are employed for the search of promising values of PNNs’ parameters. Moreover, the bagging technique was incorporated for the development of an ensemble of classification models. Another approach was the construction of a Bayesian model for the estimation of PNN’s parameters utilizing the Gibbs sampler. Furthermore, a Fuzzy Membership Function was incorporated to achieve an improved weighting of PNN’s neurons. A new decomposition scheme is proposed for multi-class classification problems when a two-class classifier is employed. The proposed classification models were applied to a series of real-world problems from several scientific areas with encouraging results.

Carbon fiber-reinforced silicon carbide matrix (C/SiC) composite is a ceramic matrixcomposite (CMC) that has considerable promise for use in high-temperature structuralapplications. In this thesis, systematic numerical studies including the prediction of elasticand thermal properties, analysis and optimization of stresses and simulation ofhigh-temperature oxidations are presented for the investigation of C/SiC composites.A strain energy method is firstly proposed for the prediction of the effective elastic constantsand coefficients of thermal expansion (CTEs) of 3D orthotropic composite materials. Thismethod derives the effective elastic tensors and CTEs by analyzing the relationship betweenthe strain energy of the microstructure and that of the homogenized equivalent model underspecific thermo-elastic boundary conditions. Different kinds of composites are tested tovalidate the model.Geometrical configurations of the representative volume cell (RVC) of 2-D woven and 3-Dbraided C/SiC composites are analyzed in details. The finite element models of 2-D wovenand 3-D braided C/SiC composites are then established and combined with the stain energymethod to evaluate the effective elastic constants and CTEs of these composites. Numericalresults obtained by the proposed model are then compared with the results measuredexperimentally.A global/local analysis strategy is developed for the determination of the detailed stresses inthe 2-D woven C/SiC composite structures. On the basis of the finite element analysis, theprocedure is carried out sequentially from the homogenized composite structure of themacro-scale (global model) to the parameterized detailed fiber tow model of the micro-scale(local model). The bridge between two scales is realized by mapping the global analysisresult as the boundary conditions of the local tow model. The stress results by global/localmethod are finally compared to those by conventional finite element analyses.Optimal design for minimizing thermal residual stress (TRS) in 1-D unidirectional C/SiCcomposites is studied. The finite element models of RVC of 1-D unidirectional C/SiCIIcomposites with multi-layer interfaces are generated and finite element analysis is realized todetermine the TRS distributions. An optimization scheme which combines a modifiedParticle Swarm Optimization (PSO) algorithm and the finite element analysis is used toreduce the TRS in the C/SiC composites by controlling the multi-layer interfaces thicknesses.A numerical model is finally developed to study the microstructure oxidation process and thedegradation of elastic properties of 2-D woven C/SiC composites exposed to air oxidizingenvironments at intermediate temperature (T<900°C). The oxidized RVC microstructure ismodeled based on the oxidation kinetics analysis. The strain energy method is then combinedwith the finite element model of oxidized RVC to predict the elastic properties of composites.The environmental parameters, i.e., temperature and pressure are studied to show theirinfluences upon the oxidation behavior of C/SiC composites.

The power industry has undergone significant restructuring throughout the world since the 1990s. In particular, its traditional, vertically monopolistic structures have been reformed into competitive markets in pursuit of increased efficiency in electricity production and utilization. However, along with market deregulation, power systems presently face severe challenges. One is power system stability, a problem that has attracted widespread concern because of severe blackouts experienced in the USA, the UK, Italy, and other countries. Another is that electricity market operation warrants more effective planning, management, and direction techniques due to the ever expanding large-scale interconnection of power grids. Moreover, many exterior constraints, such as environmental protection influences and associated government regulations, now need to be taken into consideration. All these have made existing challenges even more complex. One consequence is that more advanced power system data analysis methods are required in the deregulated, market-oriented environment. At the same time, the computational power of modern computers and the application of databases have facilitated the effective employment of new data analysis techniques. In this thesis, the reported research is directed at developing computational intelligence based techniques to solve several power system problems that emerge in deregulated electricity markets. Four major contributions are included in the thesis: a newly proposed quantum-inspired particle swarm optimization and self-adaptive learning scheme for radial basis function neural networks; online wavelet denoising techniques; electricity regional reference price forecasting methods in the electricity market; and power system security assessment approaches for deregulated markets, including fault analysis, voltage profile prediction under contingencies, and machine learning based load shedding scheme for voltage stability enhancement. Evolutionary algorithms (EAs) inspired by biological evolution mechanisms have had great success in power system stability analysis and operation planning. Here, a new quantum-inspired particle swarm optimization (QPSO) is proposed. Its inspiration stems from quantum computation theory, whose mechanism is totally different from those of original EAs. The benchmark data sets and economic load dispatch research results show that the QPSO improves on other versions of evolutionary algorithms in terms of both speed and accuracy. Compared to the original PSO, it greatly enhances the searching ability and efficiently manages system constraints. Then, fuzzy C-means (FCM) and QPSO are applied to train radial basis function (RBF) neural networks with the capacity to auto-configure the network structures and obtain the model parameters. The benchmark data sets test results suggest that the proposed training algorithms ensure good performance on data clustering, also improve training and generalization capabilities of RBF neural networks. Wavelet analysis has been widely used in signal estimation, classification, and compression. Denoising with traditional wavelet transforms always exhibits visual artefacts because of translation-variant. Furthermore, in most cases, wavelet denoising of real-time signals is actualized via offline processing which limits the efficacy of such real-time applications. In the present context, an online wavelet denoising method using a moving window technique is proposed. Problems that may occur in real-time wavelet denoising, such as border distortion and pseudo-Gibbs phenomena, are effectively solved by using window extension and window circle spinning methods. This provides an effective data pre-processing technique for the online application of other data analysis approaches. In a competitive electricity market, price forecasting is one of the essential functions required of a generation company and the system operator. It provides critical information for building up effective risk management plans by market participants, especially those companies that generate and retail electrical power. Here, an RBF neural network is adopted as a predictor of the electricity market regional reference price in the Australian national electricity market (NEM). Furthermore, the wavelet denoising technique is adopted to pre-process the historical price data. The promising network prediction performance with respect to price data demonstrates the efficiency of the proposed method, with real-time wavelet denoising making feasible the online application of the proposed price prediction method. Along with market deregulation, power system security assessment has attracted great concern from both academic and industry analysts, especially after several devastating blackouts in the USA, the UK, and Russia. This thesis goes on to propose an efficient composite method for cascading failure prevention comprising three major stages. Firstly, a hybrid method based on principal component analysis (PCA) and specific statistic measures is used to detect system faults. Secondly, the RBF neural network is then used for power network bus voltage profile prediction. Tests are carried out by means of the “N-1” and “N-1-1” methods applied in the New England power system through PSS/E dynamic simulations. Results show that system faults can be reliably detected and voltage profiles can be correctly predicted. In contrast to traditional methods involving phase calculation, this technique uses raw data from time domains and is computationally inexpensive in terms of both memory and speed for practical applications. This establishes a connection between power system fault analysis and cascading analysis. Finally, a multi-stage model predictive control (MPC) based load shedding scheme for ensuring power system voltage stability is proposed. It has been demonstrated that optimal action in the process of load shedding for voltage stability during emergencies can be achieved as a consequence. Based on above discussions, a framework for analysing power system voltage stability and ensuring its enhancement is proposed, with such a framework able to be used as an effective means of cascading failure analysis. In summary, the research reported in this thesis provides a composite framework for power system data analysis in a market environment. It covers advanced techniques of computational intelligence and machine learning, also proposes effective solutions for both the market operation and the system stability related problems facing today’s power industry.

A Otimização por Enxame de Partículas (PSO, Particle Swarm Optimization) é uma técnica de otimização que vem sendo utilizada na solução de diversos problemas, em diferentes áreas do conhecimento. Porém, a maioria das implementações é realizada de modo sequencial. O processo de otimização necessita de um grande número de avaliações da função objetivo, principalmente em problemas complexos que envolvam uma grande quantidade de partículas e dimensões. Consequentemente, o algoritmo pode se tornar ineficiente em termos do desempenho obtido, tempo de resposta e até na qualidade do resultado esperado. Para superar tais dificuldades, pode-se utilizar a computação de alto desempenho e paralelizar o algoritmo, de acordo com as características da arquitetura, visando o aumento de desempenho, a minimização do tempo de resposta e melhoria da qualidade do resultado final. Nesta dissertação, o algoritmo PSO é paralelizado utilizando três estratégias que abordarão diferentes granularidades do problema, assim como dividir o trabalho de otimização entre vários subenxames cooperativos. Um dos algoritmos paralelos desenvolvidos, chamado PPSO, é implementado diretamente em hardware, utilizando uma FPGA. Todas as estratégias propostas, PPSO (Parallel PSO), PDPSO (Parallel Dimension PSO) e CPPSO (Cooperative Parallel PSO), são implementadas visando às arquiteturas paralelas baseadas em multiprocessadores, multicomputadores e GPU. Os diferentes testes realizados mostram que, nos problemas com um maior número de partículas e dimensões e utilizando uma estratégia com granularidade mais fina (PDPSO e CPPSO), a GPU obteve os melhores resultados. Enquanto, utilizando uma estratégia com uma granularidade mais grossa (PPSO), a implementação em multicomputador obteve os melhores resultados. / Particle Swarm Optimization (PSO) is an optimization technique that is used to solve many problems in different applications. However, most implementations are sequential. The optimization process requires a large number of evaluations of the objective function, especially in complex problems, involving a large amount of particles and dimensions. As a result, the algorithm may become inefficient in terms of performance, execution time and even the quality of the expected result. To overcome these difficulties,high performance computing and parallel algorithms can be used, taking into account to the characteristics of the architecture. This should increase performance, minimize response time and may even improve the quality of the final result. In this dissertation, the PSO algorithm is parallelized using three different strategies that consider different granularities of the problem, and the division of the optimization work among several cooperative sub-swarms. One of the developed parallel algorithms, namely PPSO, is implemented directly in hardware, using an FPGA. All the proposed strategies, namely PPSO ( Parallel PSO), PDPSO (Parallel Dimension PSO) and CPPSO (Cooperative Parallel PSO), are implemented in a multiprocessor, multicomputer and GPU based parallel architectures. The different performed assessments show that the GPU achieved the best results for problems with high number of particles and dimensions when a strategy with finer granularity is used, namely PDPSO and CPPSO. In contrast with this, when using a strategy with a coarser granularity, namely PPSO, the multi-computer based implementation achieved the best results.