A module-level DC optimization proof of concept architecture is proposed to increase the efficiency of photovoltaic (PV) strings by minimizing the negative effects of shading caused by intermittent cloud cover while reducing cloud induced fast frequency fluctuations. The decentralized inverter approach combines the benefits of string and micro-inverter technology. This device can be affixed to pre-existing or new systems and operates in compliance with IEEE 1547 and California rule 21 standards by operating in maximum power point tracking (MPPT) or curtailment mode whenever necessary. The modular level device encapsulates three individual processes: an optimization engine to determine minimum power requirements, a fuzzy logic controller (FLC) to eliminate the effect of passing cloud cover, and a voltage regulation stage to monitor and appropriately adjust the output voltage of the device. Ramp rate reduction was accomplished using adaptive fuzzy logic control with a heuristic rule base inference engine. The modular design can be affixed to grid connected or islanded systems allowing for operation in regulated and variable load conditions. Matlab/Simulink 2019a was used to design and simulate the proof of concept model to verify the resiliency to partial shading, reduction of ramp rates during passing cloud coverage, and optimal output voltage for each panel while maintaining a constant DC link voltage of 120 V. This proof of concept has been successfully validated therefore further testing will be performed for various irradiance conditions.

No description available.

The objective of the thesis is to introduce a new fuzzy logic control application, develop the associated mathematical theory and prove the concept and its advantages through comparative simulation with existing, classical, methods. A stable fuzzy logic controller for the master-slave current sharing loop of a paralleled DC-DC system is presented that exhibits a considerably improved large signal performance over the presently employed, small signal designed compensators, both in terms of system response and control effort. Because of high system complexity, the present small signal designs are unable to give a good response for large load changes and line transients. Fuzzy logic, by dealing naturally with nonlinearities, offers a superior controller type, for this type of applications. The design uses a PID expert to derive the fuzzy inference rules, and simulation results show a good parameter insensitive transient response over a wide range load-step responses, e.g., from 25% to 75% of the nominal load. Current sharing control is formulated as a tracking problem and stability is ensured through adaptation or supervisory control on a Lyapunov trajectory. The technique benefits also from the heuristic approach to the problem that overcomes the complexity in modeling such systems and, hence, offers a practical engineering tool, amenable to both analog and digital implementations. / Ph. D.

A very economical way of reducing the operational energy consumed by large commercial buildings such as an airport terminal is the automatic control of its active energy systems. Such control can adjust the indoor environment systems setpoints to satisfy comfort during occupancy or when unoccupied, initiate energy conservation setpoints and if necessary, shut down part of the building systems. Adjusting energy control setpoints manually in large commercial buildings can be a nightmare for facility managers. Incidentally for such buildings, occupancy based control strategies are not achieved through the use of conventional controllers alone. This research, therefore, investigated the potential of using a high-level control system in airport terminal building. The study presents the evolution of a novel fuzzy rule-based supervisory controller, which intelligently establishes comfort setpoints based on flow of passenger through the airport as well as variable external environmental conditions. The inputs to the supervisory controller include: the time schedule of the arriving and departing passenger planes; the expected number of passengers; zone daylight illuminance levels; and external temperature. The outputs from the supervisory controller are the low-level controllers internal setpoint profile for thermal comfort, visual comfort and indoor air quality. Specifically, this thesis makes contribution to knowledge in the following ways: It utilised artificial intelligence to develop a novel fuzzy rule-based, energy-saving supervisory controller that is able to establish acceptable indoor environmental quality for airport terminals based on occupancy schedules and ambient conditions. It presents a unique methodology of designing a supervisory controller using expert knowledge of an airport s indoor environment systems through MATLAB/Simulink platform with the controller s performance evaluated in both MATLAB and EnergyPlus simulation engine. Using energy conservation strategies (setbacks and switch-offs), the pro-posed supervisory control system was shown to be capable of reducing the energy consumed in the Manchester Airport terminal building by up to 40-50% in winter and by 21-27% in summer. It demonstrates that if a 45 minutes passenger processing time is aimed for instead of the 60 minutes standard time suggested by ICAO, energy consumption is significantly reduced (with less carbon emission) in winter particularly. The potential of the fuzzy rule-based supervisory controller to optimise comfort with minimal energy based on variation in occupancy and external conditions was demonstrated through this research. The systematic approach adopted, including the use of artificial intelligence to design supervisory controllers, can be extended to other large buildings which have variable but predictable occupancy patterns.

Mitigation of displacement and acceleration responses of a three story benchmark structure excited by seismic motions is pursued in this study. Multiple 20-kN magnetorheological (MR) dampers are installed in the three-story benchmark structure and managed by a global fuzzy logic controller to provide smart damping forces to the benchmark structure. Two configurations of MR damper locations are considered to display multiple-input, single-output and multiple-input, multiple-output control capabilities. Characterization tests of each MR damper are performed in a laboratory to enable the formulation of fuzzy inference models. Prediction of MR damper forces by the fuzzy models shows sufficient agreement with experimental results. A controlled-elitist multi-objective genetic algorithm is utilized to optimize a set of fuzzy logic controllers with concurrent consideration to four structural response metrics. The genetic algorithm is able to identify optimal passive cases for MR damper operation, and then further improve their performance by intelligently modulating the command voltage for concurrent reductions of displacement and acceleration responses. An optimal controller is identified and validated through numerical simulation and fullscale experimentation. Numerical and experimental results show that performance of the controller algorithm is superior to optimal passive cases in 43% of investigated studies. Furthermore, the state-space model of the benchmark structure that is used in numerical simulations has been improved by a modified version of the same genetic algorithm used in development of fuzzy logic controllers. Experimental validation shows that the state-space model optimized by the genetic algorithm provides accurate prediction of response of the benchmark structure to base excitation.

A typical wastewater effluent of a chemical process can contain several strong acids/bases, weak acids/bases as well as their salts. They must be neutralized before being discharged to the environment in order to protect aquatic life and human welfare. However, neutralization process is highly non&ndash / linear and has time&ndash / varying characteristics. Therefore, the control of pH is a challenging problem where advanced control strategies are often considered. In this study, the aim is to design a pH control system that will be capable of controlling the pH-value of a plant waste-water effluent stream having unknown acids with unknown concentrations using an on&ndash / line identification procedure. A Model Predictive Controller, MPC, and a Fuzzy Logic Controller, FLC, are designed and used in a laboratory scale pH neutralization system. The characteristic of the upstream flow is obtained by a small identification reactor which has ten times faster dynamics and which is working parallel to actual neutralization tank. In the control strategy, steady&ndash / state titration curve of the process stream is obtained using the data collected in terms of pH value from the response of the identification reactor to a pulse input in base flow rate and using the simulated response of the identification reactor for the same input. After obtaining the steady&ndash / state titration curve, it is used in the design of a Proportional&ndash / Integral, PI, and of an Adaptive Model Predictive Controller, AMPC. On the other hand, identification reactor is not used in the FLC scheme. The performances of the designed controllers are tested mainly for disturbance rejection, set&ndash / point tracking and robustness issues theoretically and experimentally. The superiority of the FLC is verified.

Master of Science / Department of Electrical and Computer Engineering / Shelli Starrett / Variability in power systems is increasing due to pushing the system to limits for economic purposes, the inclusion of new energy sources like wind turbines and photovoltaic, and the introduction of new types of loads such as electric vehicle chargers. In this new environment, system monitoring and control must keep pace to insure system stability and reliability on a wide area scale. Phasor measurement unit technology implementation is growing and can be used to provide input signals to new types of control. Fuzzy logic based power system stabilizer (PSS) controllers have also been shown effective in various studies. This thesis considers several choices of input signals, composed assuming phasor measurement availability, for fuzzy logic-based controllers. The purpose of the controller is to damp power systems’ low frequency oscillations. Nonlinear transient simulation results for a 4-machine two-area system and 50 machine system are used to compare the effects of input choice and controller type on damping of system oscillations. Reactive power in the system affects voltage, which in turn affects system damping and dynamic stability. System stability and damping can be enhanced by deploying SVC controllers properly. Different types of power system variables play critical role to damp power swings using SVC controller. A fuzzy logic based static var compensator (SVC) was used near a generator to damp these electromechanical oscillations using different PMU-acquired inputs. The goal was again improve dynamic stability and damping performance of the system at local and global level. Nonlinear simulations were run to compare the damping performance of different inputs on the 50 machine system.

No description available.

In recent years, response analysis of complex structures under impact loads has attracted a great deal of attention. For example, a collision or an accident that produces impact loads that exceed the design load can cause severe damage on the structural components. Although the AASHTO specification is used for impact-resistant bridge design, it has many limitations. The AASHTO specification does not incorporate complex and uncertain factors. Thus, a well-designed structure that can survive a collision under specific conditions in one region may be severely damaged if it were impacted by a different vessel, or if it were located elsewhere with different in-situ conditions. With these limitations in mind, we propose different solutions that use smart control technology to mitigate impact hazard on structures. However, it is challenging to develop an accurate mathematical model of the integrated structure-smart control systems. The reason is due to the complicated nonlinear behavior of the integrated nonlinear systems and uncertainties of high impact forces. In this context, novel algorithms are developed for identification, control and monitoring of nonlinear responses of smart structures under high impact forces. To evaluate the proposed approaches, a smart aluminum and two smart reinforced concrete beam structures were designed, manufactured, and tested in the High Impact Engineering Laboratory of Civil and Environmental Engineering at WPI. High-speed impact force and structural responses such as strain, deflection and acceleration were measured in the experimental tests. It has been demonstrated from the analytical and experimental study that: 1) the proposed system identification model predicts nonlinear behavior of smart structures under a variety of high impact forces, 2) the developed structural health monitoring algorithm is effective in identifying damage in time-varying nonlinear dynamic systems under ambient excitations, and 3) the proposed controller is effective in mitigating high impact responses of the smart structures.

Depuis sa création, la projection thermique ne cesse d’étendre son champ d’application en raison de ses potentialités à projeter des matériaux bien différents (métallique, céramique, plastique,...) sous des formes bien différentes aussi (poudre, fil, suspension, solution,...). Plusieurs types de procédés ont été développés afin de satisfaire les applications industrielles, par exemple, le procédé HVOF (High Velocity Oxygen Fuel), le procédé APS (Atmospheric Plasma Spraying), le procédé VLPPS (Very Low Pressure Plasma Spray). Parmi ces procédés, le procédé APS est aujourd’hui bien implanté dans l’industrie et en laboratoire réussissant à élaborer des revêtements de bonne qualité à coût intéressant. Néanmoins, cette technologie pâtit des incidences des instabilités du procédé sur la qualité du produit obtenu et souffre d’un manque de compréhension des relations entre les paramètres opératoires et les caractéristiques des particules en vol.Pour rappel, pendant la projection APS, les phénomènes d’instabilité du pied d’arc, d’érosion des électrodes, d’instabilité des paramètres opératoires ne peuvent pas être complètement éliminés. Et, il est encore aujourd’hui difficile de mesurer et de bien contrôler ces paramètres.Compte tenu des progrès réalisés sur les moyens de diagnostic qui peuvent être utilisés en milieu hostile (comme dans le cas de la projection APS), un contrôle efficace de ce procédé en boucle fermée peut être maintenant envisagé et requiert le développement d’un système expert qui se compose des réseaux de neurones artificiels et de logique floue. Les réseaux de neurones artificiels sont développés dans plusieurs domaines d’application et aussi maintenant au cas de la projection thermique. La logique floue quant à elle est une extension de la logique booléenne basée sur la théorie mathématique des ensembles flous. Nous nous sommes intéressés dans ce travail à bâtir le modèle de contrôle en ligne du procédé de projection basé sur des éléments d’Intelligence Artificielle et à construire un émulateur qui reproduise aussi fidèlement que possible le comportement dynamique du procédé. / Since its creation, the thermal spraying continuously expands its application scope because of its potential to project very different materials (metal, ceramic, plastic ...) as well as different forms (powder, wire, suspension, solution ...). Several types of methods have been developed to meet industrial applications, for example, the process HVOF (High Velocity Oxygen Fuel), the process APS (Atmospheric Plasma Spraying), the process VLPPS (Very Low Pressure Plasma Spray). Among these methods, the APS process is now well established in the industry and laboratory for successfully developing coatings with good quality but low cost. However, this technology suffers from the instability effect of the process on the obtained product quality and endures a lack of understanding of the relationship between the operating parameters and the characteristics of in-flight particles.As a reminder, during the projection APS, the arc foot instability phenomena, the electrode erosion, the instability of the operating parameters cannot be completely eliminated. Further, it is still difficult to measure and control these parameters well. With the developing technology of diagnostic tools that can be used in a hostile environment (as in the case of APS process), an effective control of APS process in closed-loop can be considered and requires the development of an expert system consisting of artificial neural networks and fuzzy logic controlling. The artificial neural networks have been developed in several application fields and now also to plasma spraying process. Fuzzy logic controlling is an extension of Boolean logic based on the mathematical theory of fuzzy sets.We are interested in this work to build an on-line control model for the APS process based on the elements of artificial intelligence and to build an emulator that replicates as closely as possible the dynamic behavior of the process. Further, the artificial neural networks will be combined with the emulator for constituting a big system who can monitor the process and also can automatically carry out modification action. The system then will be tested off-line, the time response will be discussed.