Μία από τις σημαντικότερες Ανανεώσιμες Πηγές Ενέργειας θεωρείται η ηλιακή ενέργεια. Η μετατροπή της σε ηλεκτρική ενέργεια επιτυγχάνεται με τα φωτοβολταϊκά συστήματα. Η ολοένα και μεγαλύτερη διάδοσή τους κάνει επιτακτική την ανάγκη για την εις βάθος μελέτη τους και τη βελτίωση της απόδοσής τους. Στην παρούσα διπλωματική εργασία εξετάζεται η αρχή λειτουργίας και η τεχνολογία των συστημάτων αυτών, ενώ γίνεται μοντελοποίηση και βέλτιστη προσαρμογή διαφόρων περιπτώσεων φορτίων συνδεδεμένων σε ένα αυτόνομο σύστημα. Παράλληλα, εξετάζονται οι παράμετροι που επηρεάζουν τη μέγιστη μεταφορά ισχύος από τη φωτοβολταϊκή συστοιχία στο φορτίο, ενώ δίνεται βάρος στην κατανόηση της επίδρασης της προσπίπτουσας ακτινοβολίας και της θερμοκρασίας / One of the major Renewable Energies Sources is solar energy. Its conversion to electricity is achieved by photovoltaic systems. Their increasing dissemination makes both in-depth study and improvement of their performance necessary. This thesis examines the working principle and technology of these systems, while modeling and optimal adjustment of different cases of loads connected to an off-grid system are researched. In addition, the parameters affecting the maximum transfer of power from the photovoltaic array to the load are analyzed and the impact of the incident radiation and temperature is explained.

The purpose of this study was to investigate the effect of various loads on the force-time characteristics associated with peak power during the hang high pull (HHP). Fourteen athletic men (age: 21.6 ± 1.3 years; height: 179.3 ± 5.6 cm; body mass: 81.5 ± 8.7 kg; 1 repetition maximum [1RM] hang power clean [HPC]: 104.9 ± 15.1 kg) performed sets of the HHP at 30, 45, 65, and 80% of their 1RM HPC. Peak force, peak velocity, peak power, force at peak power, and velocity at peak power were compared between loads. Statistical differences in peak force (p 0.001), peak velocity (p < 0.001), peak power (p 0.015), force at peak power (p < 0.001), and velocity at peak power (p < 0.001) existed, with the greatest values for each variable occurring at 80, 30, 45, 80, and 30% 1RM HPC, respectively. Effect sizes between loads indicated that larger differences in velocity at peak power existed as compared with those displayed by force at peak power. It seems that differences in velocity may contribute to a greater extent to differences in peak power production as compared with force during the HHP. Further investigation of both force and velocity at peak power during weightlifting variations is necessary to provide insight on the contributing factors of power production. Specific load ranges should be prescribed to optimally train the variables associated with power development during the HHP.

This study examined the impact of load on lower body performance variables during the hang power clean. Fourteen men performed the hang power clean at loads of 30%, 45%, 65%, and 80% 1RM. Peak force, velocity, power, force at peak power, velocity at peak power, and rate of force development were compared at each load. The greatest peak force occurred at 80% 1RM. Peak force at 30% 1RM was statistically lower than peak force at 45% (p = 0.022), 65% (p = 0.010), and 80% 1RM (p = 0.018). Force at peak power at 65% and 80% 1RM was statistically greater than force at peak power at 30% (p < 0.01) and 45% 1RM (p < 0.01). The greatest rate of force development occurred at 30% 1RM, but was not statistically different from the rate of force development at 45%, 65%, and 80% 1RM. The rate of force development at 65% 1RM was statistically greater than the rate of force development at 80% 1RM (p = 0.035). No other statistical differences existed in any variable existed. Changes in load affected the peak force, force at peak power, and rate of force development, but not the peak velocity, power, or velocity at peak power.

This dissertation addresses the problem of optimally scheduling the hoists of a twin rock winder system in a demand side management context. The objective is to schedule the hoists at minimum energy cost taking into account various physical and operational constraints and production requirements as well as unplanned system delays. The problem is solved by first developing a static linear programming model of the rock winder system. The model is built on a discrete dynamic winder model and consists of physical and operational winder system constraints and an energy cost based objective function. Secondly a model predictive control based scheduling algorithm is applied to the model to provide closed-loop feedback control. The scheduling algorithm first solves the linear programming problem before applying an adapted branch and bound integer solution methodology to obtain a near optimal integer schedule solution. The scheduling algorithm also compensates for situations resulting in infeasible linear programming solutions. The simulation results show the model predictive control based scheduling algorithm to be able to successfully generate hoist schedules that result in steady state solutions in all scenarios studied, including where delays are enforced. The energy cost objective function is proven to be very effective in ensuring minimal hoisting during expensive peak periods and maximum hoisting during low energy cost off-peak periods. The algorithm also ensures that the hoist target is achieved while controlling all system states within or around their boundaries for a sustainable and continuous hoist schedule. Copyright / Dissertation (MEng)--University of Pretoria, 2010. / Electrical, Electronic and Computer Engineering / Unrestricted

Wireless resources are scarce, shared and time-varying making resource allocation mechanisms, e.g., scheduling, a key and challenging element of wireless system design. In designing good schedulers, we consider three types of performance metrics: system capacity, quality of service (QoS) seen by users, and the energy expenditures (battery lifetimes) incurred by mobile terminals. In this dissertation we investigate the impact of scheduling policies on these performance metrics, their interactions, and/or tradeoffs, and we specifically focus on flow-level performance under stochastic traffic loads. In the first part of the dissertation we evaluate interactions among flow-level performance metrics when integrating QoS and best effort flows in a wireless system using opportunistic scheduling. We introduce a simple flow-level model capturing the salient features of bandwidth sharing for an opportunistic scheduler which ensures a mean throughput to each QoS stream on every time slot. We show that the integration of QoS and best effort flows results in a loss of opportunism, which in turn results in a reduction of the stability region, degradation in system capacity, and increased file transfer delay. In the second part of the dissertation we study several ways in which mobile terminals can backoff on their uplink transmit power (thus slow down their transmissions) in order to extend battery lifetimes. This is particularly effective when a wireless system is underloaded, so the degradation in the users' perceived performance can be negligible. The challenge, however, is developing a mechanism that achieves a good tradeoff among transmit power, idling/circuit power, and the performance customers will see. We consider systems with flow-level dynamics supporting either real-time or best effort (e.g., file transfers) sessions. We show that significant energy savings can be achieved by leveraging dynamic spare capacity. We then extend our study to the case where mobile terminals have multiple transmit antennas. In the third part of the dissertation we develop a framework for user association in infrastructure-based wireless networks, specifically focused on adaptively balancing flow loads given spatially inhomogeneous traffic distributions. Our work encompasses several possible user association objective functions resulting in rate-optimal, throughput-optimal, delay-optimal, and load-equalizing policy, which we collectively denote [alpha]-optimal user association. We prove that the optimal load vector that minimizes this function is the fixed point of a certain mapping. Based on this mapping we propose an iterative distributed user association policy and prove that it converges to the globally optimal decision in steady state. In addition we address admission control policies for the case where the system cannot be stabilized. / text

Este trabalho apresenta uma metodologia para a alocação de fontes de reativos em sistemas de energia elétrica. A metodologia é baseada no multiplicador de Lagrange, o qual indica a sensibilidade entre a função objetivo perdas de potência ativa na transmissão, e as restrições injeções de potência reativa. Os multiplicadores de Lagrange são obtidos impondo-se as condições de estacionaridade à função Lagrangeana Aumentada, a qual é associada ao problema de Fluxo de Carga Ótimo. A metodologia é comparada com o método Simplex e com o sistema original. Testes foram realizados com os sistemas AEP14 e AEP30 que mostraram a eficiência da metodologia. / This work presents a methodology for the allocation of reactive sources in electrical power systems. The methodology is based on Lagrange\'s multiplier that points out the sensibility between the objective function - transmission active power loss and the constraints reactive power injections. The Lagrange\'s multipliers are obtained by imposing the conditions of stationarity at Augmented Lagrangeana function, that is connected to the Optimal Load Flow problem. The methodology is compared with the Simplex method and the original system. Tests were canied out with AEP14 and AEP30 systems, that showed the efficiency of the methodology.

Este trabalho apresenta uma metodologia para a alocação de fontes de reativos em sistemas de energia elétrica. A metodologia é baseada no multiplicador de Lagrange, o qual indica a sensibilidade entre a função objetivo perdas de potência ativa na transmissão, e as restrições injeções de potência reativa. Os multiplicadores de Lagrange são obtidos impondo-se as condições de estacionaridade à função Lagrangeana Aumentada, a qual é associada ao problema de Fluxo de Carga Ótimo. A metodologia é comparada com o método Simplex e com o sistema original. Testes foram realizados com os sistemas AEP14 e AEP30 que mostraram a eficiência da metodologia. / This work presents a methodology for the allocation of reactive sources in electrical power systems. The methodology is based on Lagrange\'s multiplier that points out the sensibility between the objective function - transmission active power loss and the constraints reactive power injections. The Lagrange\'s multipliers are obtained by imposing the conditions of stationarity at Augmented Lagrangeana function, that is connected to the Optimal Load Flow problem. The methodology is compared with the Simplex method and the original system. Tests were canied out with AEP14 and AEP30 systems, that showed the efficiency of the methodology.

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