Toppeffekter används av elnätsbolag för att dimensionera elnätet, vilket blir allt viktigare för varje år. Fler och fler invånare och företag ökar sin elkonsumtion och förväntar sig en driftsäker och stabil elförsörjning. Det finns två vanliga metoder att beräkna toppeffekter. Första sättet är Velanders formel som är en enkel metod för att uppskatta toppeffekter. Velanders formel behöver bara årsenergi och vetskap om kundkategori med tillhörande Velanderkonstanter för beräkning av uppskattad toppeffekt. Sedan finns den mer komplexa typkurvemetoden som behöver flera olika parametrar, t.ex. graddagtal, dygnsmedeltemperatur, gränssannolikhet och kundkategori. Detta examensarbete undersöker en enkel metod för att ta fram konstanter till Velanders formel för beräkning av toppeffekter. Detta genomfördes med hjälp av regressionsanalys av historiska elanvändningsdata från Mälarenergi Elnät AB:s (MEE) kunder från 12 olika kundkategorier. Detta på grund av att MEE önskade att utveckla en metod för att ta fram konstanter till Velanders formel baserad på historiska elanvändningsdata. Metoden för att ta fram konstanter till Velanders formel går ut på att med hjälp av MATLAB utföra en regressionsanalys på simulerade kundgrupper skapade från timvis historiska elanvändningsdata. En kurva baserad på Velanders formel tas sedan fram som beskriver den övre gränsen till toppeffekterna för de simulerade kundgrupperna. Från kurvan fås sedan de Velanderkonstanter som söks. Resultaten av den undersökta metoden presenteras i form av grafer och tabeller för tre utvalda kundkategorier. Alla kategorier och deras resultat finns som bilagor till rapporten. Valideringen av resultaten och metoden gjordes med hjälp av korsvalidering och jämförelse mot heterogena simulerade kundgrupper. Känslighetsanalysen visar att den undersökta metoden var känslig för flera faktorer såsom kategorisering av kunder, tidsspann för historiska elanvändningsdata, antal simulerade kundgrupper och kundantal. Med tillräcklig dimensionering av dessa faktorer bedömdes metoden vara användbar. Resultaten visade på att de framtagna Velanderkonstanterna gav en god uppskattning av toppeffekter för de kundkategorier som undersökts. Jämförelse av de uppskattade toppeffekterna och de observerade visade på att det fanns en viss differens mellan dem. Detta var dock förväntat eftersom de uppskattade toppeffekterna ska avspegla den övre toppeffektsgränsen. / This degree project has examined a simple method aiming to obtain coefficients for Velanders formula which purpose is to calculate peak loads. This was done by using regression analysis on historical data on consumption of electricity from 12 different customer categories acquired from Mälarenergi Elnät AB (MEE). The reason being that MEE wanted to examine a method which could obtain coefficients for Velanders formula based on hourly historical electricity consumption data. The method for obtaining Velander coefficients uses MATLAB to do regression analysis on simulated customer groups, created from hourly historical electricity consumption data. The Velander coefficients are then obtained from a regression curve based on Velanders formula. Results from the evaluation of the method is presented with the help of plots and tables for three chosen customer categories. Validation of the method was done by cross-validation and comparison against heterogeneous customer groups. Sensitivity analysis showed the examined method to be sensitive to several factors such as categorization of customers, the timespan of historical electricity consumption data, the number of simulated customer groups that were used and how many customers a category contained. By dimensioning these factors carefully, the method examined was assessed to be viable. The results indicated that the obtained Velander coefficients gave a good estimation of the peak loads for the chosen customer categories. Comparison between the estimated and observed peak loads indicated that there was a certain difference between them. This was to be expected since the estimated peak loads are to reflect the upper peak load limit.

The goal of this work was to calculate the mass in the bucket of a underground loaderfrom pressure data in the cylinders. Three different approaches to using a Kalman filter toestimate the loaded mass were tried and evaluated in MATLAB simulations. Of these, twogave promising results when tried on real data and a combination of the two methods issuggested as solution to the problem. The filters required a model for the mechanics of themachine which was also derived.

Distributed event processing systems suffer from poor scalability and inefficient resource usage caused by load distributions typical in real-world applications. The results of these shortcomings are availability issues, poor system performance, and high operating costs. This thesis proposes three remedies to solve these limitations in content-based publish/subscribe, which is a practical realization of an event processing system. First, we present a load balancing algorithm that relocates subscribers to distribute load and avoid overloads. Second, we propose publisher relocation algorithms that reduces both the load imposed onto brokers and delivery delay experienced by subscribers. Third, we present ``green" resource allocation algorithms that allocate as few brokers as possible while maximizing their resource usage efficiency by reconfiguring the publishers, subscribers, and the broker topology. We implemented and evaluated all of our approaches on an open source content-based publish/subscribe system called PADRES and evaluated them on SciNet, PlanetLab, a cluster testbed, and in simulations to prove the effectiveness of our solutions. Our evaluation findings are summarized as follows. One, the proposed load balancing algorithm is effective in distributing and balancing load originating from a single server to all available servers in the network. Two, our publisher relocation algorithm reduces the average input load of the system by up to 68%, average broker message rate by up to 85%, and average delivery delay by up to 68%. Three, our resource allocation algorithm reduces the average broker message rate even further by up to 92% and the number of allocated brokers by up to 91%.

Distributed event processing systems suffer from poor scalability and inefficient resource usage caused by load distributions typical in real-world applications. The results of these shortcomings are availability issues, poor system performance, and high operating costs. This thesis proposes three remedies to solve these limitations in content-based publish/subscribe, which is a practical realization of an event processing system. First, we present a load balancing algorithm that relocates subscribers to distribute load and avoid overloads. Second, we propose publisher relocation algorithms that reduces both the load imposed onto brokers and delivery delay experienced by subscribers. Third, we present ``green" resource allocation algorithms that allocate as few brokers as possible while maximizing their resource usage efficiency by reconfiguring the publishers, subscribers, and the broker topology. We implemented and evaluated all of our approaches on an open source content-based publish/subscribe system called PADRES and evaluated them on SciNet, PlanetLab, a cluster testbed, and in simulations to prove the effectiveness of our solutions. Our evaluation findings are summarized as follows. One, the proposed load balancing algorithm is effective in distributing and balancing load originating from a single server to all available servers in the network. Two, our publisher relocation algorithm reduces the average input load of the system by up to 68%, average broker message rate by up to 85%, and average delivery delay by up to 68%. Three, our resource allocation algorithm reduces the average broker message rate even further by up to 92% and the number of allocated brokers by up to 91%.

Engineers have long been inspired by nature's flyers. Such animals navigate complex environments gracefully and efficiently by using a variety of evolutionary adaptations for high-performance flight. Biologists have discovered a variety of sensory adaptations that provide flow state feedback and allow flying animals to feel their way through flight. A specialized skeletal wing structure and plethora of robust, adaptable sensory systems together allow nature's flyers to adapt to myriad flight conditions and regimes. In this work, motivated by biology and the successes of bio-inspired, engineered aerial vehicles, linear quadratic control of a flexible, morphing wing design is investigated, helping to pave the way for truly autonomous, mission-adaptive craft. The proposed control algorithm is demonstrated to morph a wing into desired positions. Furthermore, motivated specifically by the sensory adaptations organisms possess, this work transitions to an investigation of aircraft wing load identification using structural response as measured by distributed sensors. A novel, recursive estimation algorithm is utilized to recursively solve the inverse problem of load identification, providing both wing structural and aerodynamic states for use in a feedback control, mission-adaptive framework. The recursive load identification algorithm is demonstrated to provide accurate load estimate in both simulation and experiment. / Graduation date: 2012