Online optimization of storage ring nonlinear beam dynamics

We propose to optimize the nonlinear beam dynamics of existing and future storage rings with direct online optimization techniques. This approach may have crucial importance for the implementation of diffraction limited storage rings. In this paper considerations and algorithms for the online optimization approach are discussed. We have applied this approach to experimentally improve the dynamic aperture of the SPEAR3 storage ring with the robust conjugate direction search method and the particle swarm optimization method. The dynamic aperture was improved by more than 5 mm within a short period of time. Experimental setup and results are presented

Accurate angle-of-arrival measurement using particle swarm optimization

As one of the major methods for location positioning, angle-of-arrival (AOA) estimation is a significant technology in radar, sonar, radio astronomy, and mobile communications. AOA measurements can be exploited to locate mobile units, enhance communication efficiency and network capacity, and support location-aided routing, dynamic network management, and many location-based services. In this paper, we propose an algorithm for AOA estimation in colored noise fields and harsh application scenarios. By modeling the unknown noise covariance as a linear combination of known weighting matrices, a maximum likelihood (ML) criterion is established, and a particle swarm optimization (PSO) paradigm is designed to optimize the cost function. Simulation results demonstrate that the paired estimator PSO-ML significantly outperforms other popular techniques and produces superior AOA estimates

Wave-based sensor, actuator and optimizer

Programa doutoral em Sistemas Avançados de Engenharia para a Indústria (AESI)A presente tese explora a utilização de ondas para abordar dois desafios significativos na indústria automóvel. O primeiro desafio consiste no desenvolvimento de um sistema de cancelamento ativo de ruído (ANC) que possa reduzir os ruídos não estacionários no compartimento de passageiros de um veículo. O segundo desafio é criar uma metodologia de conceção ótima para sensores de posição indutivos capazes de medir deslocamentos lineares, rotacionais e angulares. Para abordar o primeiro desafio, foi desenvolvido de um sistema ANC onde wavelets foram combinadas com um banco de filtros adaptativos. O sistema foi implementado em uma FPGA, e testes demonstraram que o sistema pode reduzir o ruído não estacionário em um ambiente acústico aberto e não controlado em 9 dB. O segundo desafio foi abordado através de uma metodologia que combina um algoritmo genético com um método numérico rápido para otimizar um sensor de posição indutivo. O método numérico foi usado para simular o campo eletromagnético associado à geometria do sensor, permitindo a maximização da corrente induzida nas bobinas recetoras e a minimização da não-linearidade no sensor. A minimização da não-linearidade foi conseguida através do desenho (layout) das bobinas que compõem o sensor. Sendo este otimizado no espaço de Fourier através da adição de harmónicos apropriados na geometria. As melhores geometrias otimizadas apresentaram uma não-linearidade inferior a 0,01% e a 0,25% da escala total para os sensores de posição angular e linear, respetivamente, sem calibração por software. O sistema ANC proposto tem o potencial de melhorar o conforto dos ocupantes do veículo, reduzindo o ruído indesejado dentro do compartimento de passageiros. Isso poderia reduzir o uso de materiais de isolamento acústico no veículo, levando a um veículo mais leve e, em última análise, a uma redução no consumo de energia. A metodologia desenvolvida para sensores de posição indutivos contribui para o estado da arte de sensores de posição eficientes e económicos, o que é crucial para os requisitos complexos da indústria automóvel. Essas contribuições têm implicações para o desenho de sistemas automotivos, com requisitos de desempenho e considerações ambientais e económicas.This thesis explores the use of waves to tackle two major engineering challenges in the automotive industry. The first challenge is the development of an Active Noise Cancelling (ANC) system that can effectively reduce non-stationary noise inside a vehicle’s passenger compartment. The second challenge is the optimization of an inductive position sensor design methodology capable of measuring linear, rotational, and angular displacements. To address the first challenge, this work designs an ANC system that employs wavelets combined with a bank of adaptive filters. The system was implemented in an FPGA, and field tests demonstrate its ability to reduce non-stationary noise in an open and uncontrolled acoustic environment by 9 dB. The second challenge was tackled by proposing a new approach that combines a genetic algorithm with a fast and lightweight numerical method to optimize the geometry of an inductive position sensor. The numerical method is used to simulate the sensor’s electromagnetic field, allowing for the maximization of induced current on the receiver coils while minimizing the sensor’s non-linearity. The non-linearity minimization was achieved through its unique sensor’s coils design optimized in the Fourier space by adding the appropriate harmonics to the coils’ geometry. The best optimized geometries exhibited a non-linearity of less than 0.01% and 0.25% of the full scale for the angular and linear position sensors, respectively. Both results were achieved without the need for signal calibration or post-processing manipulation. The proposed ANC system has the potential to enhance the comfort of vehicle occupants by reducing unwanted noise inside the passenger compartment. Moreover, it has the potential to reduce the use of acoustic insulation materials in the vehicle, leading to a lighter vehicle and ultimately reducing energy consumption. The developed methodology for inductive position sensors represents a state-of-the-art contribution to efficient and cost-effective position sensor design, which is crucial for meeting the complex requirements of the automotive industry.I would like to thank the Fundação para a Ciência e Tecnologia (FCT) and Bosch Car Multimedia for funding my PhD (grant PD/BDE/142901/2018)

CONTROLLERS AND METHODS FOR DIFFERENT ELECTRICAL MEASUREMENTS IN SYNCHRONIZATION OF RENEWABLE ENERGY SOURCES FOR GRID CONNECTIVITY: A REVIEW

In this paper, different controllers used in synchronization of renewable energy sources are studied. A study regarding the use of artificial intelligence in synchronization of grid connected power converters, efficient method for phase angle detection, frequency variation detection and good performance during voltage depression etc  carried out here. Importance of hybrid controllers over conventional controllers is also presented. Possibility of  Z source T type inverter as an alternate solution to DC-DC converter is explored based on existing works

A Novel Adaptive Elite-Based Particle Swarm Optimization Applied to VAR Optimization in Electric Power Systems

Particle swarm optimization (PSO) has been successfully applied to solve many practical engineering problems. However, more efficient strategies are needed to coordinate global and local searches in the solution space when the studied problem is extremely nonlinear and highly dimensional. This work proposes a novel adaptive elite-based PSO approach. The adaptive elite strategies involve the following two tasks: (1) appending the mean search to the original approach and (2) pruning/cloning particles. The mean search, leading to stable convergence, helps the iterative process coordinate between the global and local searches. The mean of the particles and standard deviation of the distances between pairs of particles are utilized to prune distant particles. The best particle is cloned and it replaces the pruned distant particles in the elite strategy. To evaluate the performance and generality of the proposed method, four benchmark functions were tested by traditional PSO, chaotic PSO, differential evolution, and genetic algorithm. Finally, a realistic loss minimization problem in an electric power system is studied to show the robustness of the proposed method

Magnetocardiography in unshielded environment based on optical magnetometry and adaptive noise cancellation

This thesis proposes and demonstrates the concept of a magnetocardiographic system employing an array of optically-pumped quantum magnetometers and an adaptive noise cancellation for heart magnetic field measurement within a magnetically-unshielded environment. Optically-pumped quantum magnetometers are based on the use of the atomic-spin-dependent optical properties of an atomic medium. An Mxconfiguration- based optically-pumped quantum magnetometer employing two sensing cells containing caesium vapour is theoretically described and experimentally developed, and the dependence of its sensitivity and frequency bandwidth upon the light power and the alkali vapour temperature is experimentally demonstrated. Furthermore, the capability of the developed magnetometer of measuring very weak magnetic fields is experimentally demonstrated in a magnetically-unshielded environment. The adaptive noise canceller is based on standard Least-Mean-Squares (LMS) algorithms and on two heuristic optimization techniques, namely, Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). The use of these algorithms is investigated for suppressing the power line generated 50Hz interference and recovering of the weak magnetic heart signals from a much higher electromagnetic environmental noise. Experimental results show that all the algorithms can extract a weak heart signal from a much-stronger magnetic noise, detect the P, QRS, and T heart features and highly suppress the common power line noise component at 50 Hz. Moreover, adaptive noise cancellation based on heuristic algorithms is shown to be more efficient than adaptive noise canceller based on standard or normalised LMS algorithm in heart features detection

Optimal excitation controllers, and location and sizing of energy storage for all-electric ship power system

The Navy\u27s future all-electric ship power system is based on the integrated power system (IPS) architecture consisting of power generation, propulsion systems, hydrodynamics, and DC zonal electric distribution system (DC-ZEDS). To improve the power quality, optimal excitation systems, and optimal location and sizing of energy storage modules (ESMs) are studied. In this dissertation, clonal selection algorithm (CSA) based controller design is firstly introduced. CSA based controller design shows better exploitation ability with relatively long search time when compared to a particle swarm optimization (PSO) based design. Furthermore, \u27optimal\u27 small population PSO (SPPSO) based excitation controller is introduced. Parameter sensitivity analysis shows that the parameters of SPPSO for regeneration can be fined tuned to achieve fast optimal controller design, and thus exploiting SPPSO features for problem of particles get trapped in local minima and long search time. Furthermore, artificial immune system based concepts are used to develop adaptive and coordinated excitation controllers for generators on ship IPS. The computational approaches for excitation controller designs have been implemented on digital signal processors interfaced to an actual laboratory synchronous machine, and to multimachine electric ship power systems simulated on a real-time digital simulator. Finally, an approach to evaluate ESM location and sizing is proposed using three metrics: quality of service, survivability and cost. Multiple objective particle swarm optimization (MOPSO) is used to optimize these metrics and provide Pareto fronts for optimal ESM location and sizing --Abstract, page iv

Maximum Power Point Tracking Algorithm for Advanced Photovoltaic Systems

Photovoltaic (PV) systems are the major nonconventional sources for power generation for present power strategy. The power of PV system has rapid increase because of its unpolluted, less noise and limited maintenance. But whole PV system has two main disadvantages drawbacks, that is, the power generation of it is quite low and the output power is nonlinear, which is influenced by climatic conditions, namely environmental temperature and the solar irradiation. The natural limiting factor is that PV potential in respect of temperature and irradiation has nonlinear output behavior. An automated power tracking method, for example, maximum power point tracking (MPPT), is necessarily applied to improve the power generation of PV systems. The MPPT methods undergo serious challenges when the PV system is under partial shade condition because PV shows several peaks in power. Hence, the exploration method might easily be misguided and might trapped to the local maxima. Therefore, a reasonable exploratory method must be constructed, which has to determine the global maxima for PV of shaded partially. The traditional approaches namely constant voltage tracking (CVT), perturb and observe (P&O), hill climbing (HC), Incremental Conductance (INC), and fractional open circuit voltage (FOCV) methods, indeed some of their improved types, are quite incompetent in tracking the global MPP (GMPP). Traditional techniques and soft computing-based bio-inspired and nature-inspired algorithms applied to MPPT were reviewed to explore the possibility for research while optimizing the PV system with global maximum output power under partially shading conditions. This paper is aimed to review, compare, and analyze almost all the techniques that implemented so far. Further this paper provides adequate details about algorithms that focuses to derive improved MPPT under non-uniform irradiation. Each algorithm got merits and demerits of its own with respect to the converging speed, computing time, complexity of coding, hardware suitability, stability and so on

Particle Swarm Optimization for Interference Mitigation of Wireless Body Area Network: A Systematic Review

Wireless body area networks (WBAN) has now become an important technology in supporting services in the health sector and several other fields. Various surveys and research have been carried out massively on the use of swarm intelligent (SI) algorithms in various fields in the last ten years, but the use of SI in wireless body area networks (WBAN) in the last five years has not seen any significant progress. The aim of this research is to clarify and convince as well as to propose a answer to this problem, we have identified opportunities and topic trends using the particle swarm optimization (PSO) procedure as one of the swarm intelligence for optimizing wireless body area network interference mitigation performance. In this research, we analyzes primary studies collected using predefined exploration strings on online databases with the help of Publish or Perish and by the preferred reporting items for systematic reviews and meta-analysis (PRISMA) way. Articles were carefully selected for further analysis. It was found that very few researchers included optimization methods for swarm intelligence, especially PSO, in mitigating wireless body area network interference, whether for intra, inter, or cross-WBAN interference. This paper contributes to identifying the gap in using PSO for WBAN interference and also offers opportunities for using PSO both standalone and hybrid with other methods to further research on mitigating WBAN interference