Time consumption analysis of the mechanized cut-to-length harvesting system

[Abstract]

Chaotic Behaviour of Active Magnetic Bearing System by Time Series Analysis

Chaotic behaviour is one of the hardest problems that can happen in nonlinear dynamical systems with severe nonlinearities. It makes the system's responses unpredictable. It makes the system's responses to behave similar to noise. In some applications it should be avoided. One of the approaches to detect the chaotic behaviour is nding the Lyapunov exponent through examining the dynamical equation of the system. It needs a model of the system. The goal of this study is the diagnosis of chaotic behaviour by just exploring the data (signal) without using any dynamical model of the system. In this work two methods are tested on the time series data collected from AMB (Active Magnetic Bearing) system sensors. The rst method is used to nd the largest Lyapunov exponent by Rosenstein method. The second method is a 0-1 test for identifying chaotic behaviour. These two methods are used to detect if the data is chaotic. By using Rosenstein method it is needed to nd the minimum embedding dimension. To nd the minimum embedding dimension Cao method is used. Cao method does not give just the minimum embedding dimension, it also gives the order of the nonlinear dynamical equation of the system and also it shows how the system's signals are corrupted with noise. At the end of this research a test called runs test is introduced to show that the data is not excessively noisy.

Converter-flux-based current control of voltage source PWM rectifiers - analysis and implementation

Pulsewidth-modulated (PWM) rectifier technology is increasingly used in industrial applications like variable-speed motor drives, since it offers several desired features such as sinusoidal input currents, controllable power factor, bidirectional power flow and high quality DC output voltage. To achieve these features,however, an effective control system with fast and accurate current and DC voltage responses is required. From various control strategies proposed to meet these control objectives, in most cases the commonly known principle of the synchronous-frame current vector control along with some space-vector PWM scheme have been applied. Recently, however, new control approaches analogous to the well-established direct torque control (DTC) method for electrical machines have also emerged to implement a high-performance PWM rectifier. In this thesis the concepts of classical synchronous-frame current control and DTC-based PWM rectifier control are combined and a new converter-flux-based current control (CFCC) scheme is introduced. To achieve sufficient dynamic performance and to ensure a stable operation, the proposed control system is thoroughly analysed and simple rules for the controller design are suggested. Special attention is paid to the estimationof the converter flux, which is the key element of converter-flux-based control. Discrete-time implementation is also discussed. Line-voltage-sensorless reactive reactive power control methods for the L- and LCL-type line filters are presented. For the L-filter an open-loop control law for the d-axis current referenceis proposed. In the case of the LCL-filter the combined open-loop control and feedback control is proposed. The influence of the erroneous filter parameter estimates on the accuracy of the developed control schemes is also discussed. A newzero vector selection rule for suppressing the zero-sequence current in parallel-connected PWM rectifiers is proposed. With this method a truly standalone and independent control of the converter units is allowed and traditional transformer isolation and synchronised-control-based solutions are avoided. The implementation requires only one additional current sensor. The proposed schemes are evaluated by the simulations and laboratory experiments. A satisfactory performance and good agreement between the theory and practice are demonstrated.

Analysis of outliers in electricity spot prices with exampleof New England and New Zealand markets

Electricity spot prices have always been a demanding data set for time series analysis, mostly because of the non-storability of electricity. This feature, making electric power unlike the other commodities, causes outstanding price spikes. Moreover, the last several years in financial world seem to show that ’spiky’ behaviour of time series is no longer an exception, but rather a regular phenomenon. The purpose of this paper is to seek patterns and relations within electricity price outliers and verify how they affect the overall statistics of the data. For the study techniques like classical Box-Jenkins approach, series DFT smoothing and GARCH models are used. The results obtained for two geographically different price series show that patterns in outliers’ occurrence are not straightforward. Additionally, there seems to be no rule that would predict the appearance of a spike from volatility, while the reverse effect is quite prominent. It is concluded that spikes cannot be predicted based only on the price series; probably some geographical and meteorological variables need to be included in modeling.

Utilization of a Time Domain Simulator in the Technical and Economic Analysis of a Wind Turbine Electric Drive Train

The amount of installed wind power has been growing exponentially during the past ten years. As wind turbines have become a significant source of electrical energy, the interactions between the turbines and the electric power network need to be studied more thoroughly than before. Especially, the behavior of the turbines in fault situations is of prime importance; simply disconnecting all wind turbines from the network during a voltage drop is no longer acceptable, since this would contribute to a total network collapse. These requirements have been a contributor to the increased role of simulations in the study and design of the electric drive train of a wind turbine. When planning a wind power investment, the selection of the site and the turbine are crucial for the economic feasibility of the installation. Economic feasibility, on the other hand, is the factor that determines whether or not investment in wind power will continue, contributing to green electricity production and reduction of emissions. In the selection of the installation site and the turbine (siting and site matching), the properties of the electric drive train of the planned turbine have so far been generally not been taken into account. Additionally, although the loss minimization of some of the individual components of the drive train has been studied, the drive train as a whole has received less attention. Furthermore, as a wind turbine will typically operate at a power level lower than the nominal most of the time, efficiency analysis in the nominal operating point is not sufficient. This doctoral dissertation attempts to combine the two aforementioned areas of interest by studying the applicability of time domain simulations in the analysis of the economicfeasibility of a wind turbine. The utilization of a general-purpose time domain simulator, otherwise applied to the study of network interactions and control systems, in the economic analysis of the wind energy conversion system is studied. The main benefits of the simulation-based method over traditional methods based on analytic calculation of losses include the ability to reuse and recombine existing models, the ability to analyze interactions between the components and subsystems in the electric drive train (something which is impossible when considering different subsystems as independent blocks, as is commonly done in theanalytical calculation of efficiencies), the ability to analyze in a rather straightforward manner the effect of selections other than physical components, for example control algorithms, and the ability to verify assumptions of the effects of a particular design change on the efficiency of the whole system. Based on the work, it can be concluded that differences between two configurations can be seen in the economic performance with only minor modifications to the simulation models used in the network interaction and control method study. This eliminates the need ofdeveloping analytic expressions for losses and enables the study of the system as a whole instead of modeling it as series connection of independent blocks with no lossinterdependencies. Three example cases (site matching, component selection, control principle selection) are provided to illustrate the usage of the approach and analyze its performance.

Analysis of Earnings, Stock Prices and Bond Yields: The Fed Model Approach

Prediction of the stock market valuation is a common interest to all market participants. Theoretically sound market valuation can be achieved by discounting future earnings of equities to present. Competing valuation models seek to find variables that affect the equity market valuation in a way that the market valuation can be explained and also variables that could be used to predict market valuation. In this paper we test the contemporaneous relationship between stock prices, forward looking earnings and long-term government bond yields. We test this so-called Fed model in a long- and short-term time series analysis. In order to test the dynamics of the relationship, we use the cointegration framework. The data used in this study spans over four decades of various market conditions between 1964-2007, using data from United States. The empirical results of our analysis do not give support for the Fed model. We are able to show that the long-term government bonds do not play statistically significant role in this relationship. The effect of forward earnings yield on the stock market prices is significant and thus we suggest the use of standard valuation ratios when trying to predict the future paths of equity prices. Also, changes in the long-term government bond yields do not have significant short-term impact on stock prices.

Ecological and Epidemiological Analyses of Multiple Sclerosis Relapse Rate

The underlying cause of many human autoimmune diseases is unknown, but several environmental factors are implicated in triggering the self-destructive immune reactions. Multiple Sclerosis (MS) is a chronic autoimmune disease of the central nervous system, potentially leading to persistent neurological deterioration. The cause of MS is not known, and apart from immunomodulatory treatments there is no cure. In the early phase of the disease, relapsing-remitting MS (RR-MS) is characterized by unpredictable exacerbations of the neurological symptoms called relapses, which can occur at different intervals ranging from 4 weeks to several years. Microbial infections are known to be able to trigger MS relapses, and the patients are instructed to avoid all factors that might increase the risk of infections and to properly use antibiotics as well as to take care of dental hygiene. Among those environmental factors which are known to increase susceptibility to infections, high ambient air inhalable particulate matter levels affect all people within a geographical region. During the period of interest in this thesis, the occurrence of MS relapses could be effectively reduced by injections of interferon, which has immunomodulatory and antiviral properties. In this thesis, ecological and epidemiological analyses were used to study the possible connection between MS relapse occurrence, population level viral infections and air quality factors, as well as the effects of interferon medication. Hospital archive data were collected retrospectively from 1986-2001, a period in time ranging from when interferon medication first became available until just before other disease-modifying MS therapies arrived on the market. The grouped data were studied with logistic regression and intervention analysis, and individual patient data with survival analysis. Interferons proved to be effective in the treatment of MS in this observational study, as the amount of MS exacerbations was lower during interferon use as compared to the time before interferon treatment. A statistically significant temporal relationship between MS relapses and inhalable particular matter (PM₁₀) concentrations was found in this study, which implies that MS patients are affected by the exposure to PM₁₀. Interferon probably protected against the effect of PM₁₀, because a significant increase in the risk of exacerbations was only observed in MS patients without interferon medication following environmental exposure to population level specific viral infections and PM₁₀. Apart from being antiviral, interferon could thus also attenuate the enhancement of immune reactions caused by ambient air PM₁₀. The retrospective approach utilizing carefully constructed hospital records proved to be an economical and reliable source of MS disease information for statistical analyses.

Parameter identification in time series models

Time series analysis can be categorized into three different approaches: classical, Box-Jenkins, and State space. Classical approach makes a basement for the analysis and Box-Jenkins approach is an improvement of the classical approach and deals with stationary time series. State space approach allows time variant factors and covers up a broader area of time series analysis. This thesis focuses on parameter identifiablity of different parameter estimation methods such as LSQ, Yule-Walker, MLE which are used in the above time series analysis approaches. Also the Kalman filter method and smoothing techniques are integrated with the state space approach and MLE method to estimate parameters allowing them to change over time. Parameter estimation is carried out by repeating estimation and integrating with MCMC and inspect how well different estimation methods can identify the optimal model parameters. Identification is performed in probabilistic and general senses and compare the results in order to study and represent identifiability more informative way.

Aikasarja-analyysimenetelmien soveltuvuus maakaasun; hinnoittelussa käytettyjen sidonnaisuustekijöiden ennustamisessa: Case Gasum Oy

Tässä tutkielmassatarkastellaan maakaasun hinnoittelussa käytettyjen sidonnaisuustekijöiden hintadynamiikkaa ja niiden vaikutusta maakaasun hinnanmuodostukseen. Pääasiallisena tavoitteena on arvioida eri aikasarjamenetelmien soveltuvuutta sidonnaisuustekijöiden ennustamisessa. Tämä toteutettiin analysoimalla eri mallien ja menetelmien ominaisuuksia sekä yhteen sovittamalla nämä eri energiamuotojen hinnanmuodostuksen erityispiirteisiin. Tutkielmassa käytetty lähdeaineisto on saatu Gasum Oy:n tietokannasta. Maakaasun hinnoittelussa käytetään kolmea sidonnaisuustekijää seuraavilla painoarvoilla: raskaspolttoöljy 50%, indeksi E40 30% ja kivihiili 20%. Kivihiilen ja raskaan polttoöljyn hinta-aineisto koostuu verottomista dollarimääräisistä kuukausittaisista keskiarvoista periodilta 1.1.1997 - 31.10.2004. Kotimarkkinoiden perushintaindeksin alaindeksin E40 indeksi-aineisto, joka kuvaa energian tuottajahinnan kehitystä Suomessa ja koostuu tilastokeskuksen julkaisemista kuukausittaisista arvoista periodilta 1.1.2000 - 31.10.2004. Tutkimuksessa tarkasteltujen mallien ennustuskyky osoittautui heikoksi. Kuitenkin tuloksien perusteella voidaan todeta, että lyhyellä aikavälillä EWMA-malli antoi harhattomimman ennusteen. Muut testatuista malleista eivät kyenneet antamaan riittävän luotettavia ja tarkkoja ennusteita. Perinteinen aikasarja-analyysi kykeni tunnistamaan aikasarjojen kausivaihtelut sekä trendit. Lisäksi liukuvan keskiarvon menetelmä osoittautui jossain määrin käyttökelpoiseksi aikasarjojen lyhyen aikavälin trendien identifioinnissa.

Mechanistic population Models in Biology: Model Derivation and Application

In general, models of ecological systems can be broadly categorized as ’top-down’ or ’bottom-up’ models, based on the hierarchical level that the model processes are formulated on. The structure of a top-down, also known as phenomenological, population model can be interpreted in terms of population characteristics, but it typically lacks an interpretation on a more basic level. In contrast, bottom-up, also known as mechanistic, population models are derived from assumptions and processes on a more basic level, which allows interpretation of the model parameters in terms of individual behavior. Both approaches, phenomenological and mechanistic modelling, can have their advantages and disadvantages in different situations. However, mechanistically derived models might be better at capturing the properties of the system at hand, and thus give more accurate predictions. In particular, when models are used for evolutionary studies, mechanistic models are more appropriate, since natural selection takes place on the individual level, and in mechanistic models the direct connection between model parameters and individual properties has already been established. The purpose of this thesis is twofold. Firstly, a systematical way to derive mechanistic discrete-time population models is presented. The derivation is based on combining explicitly modelled, continuous processes on the individual level within a reproductive period with a discrete-time maturation process between reproductive periods. Secondly, as an example of how evolutionary studies can be carried out in mechanistic models, the evolution of the timing of reproduction is investigated. Thus, these two lines of research, derivation of mechanistic population models and evolutionary studies, are complementary to each other.

Ostotapahtuman ennustemallin kehittäminen ajoneuvomarkkinoinnin tueksi

Perinteisesti ajoneuvojen markkinointikampanjoissa kohderyhmät muodostetaan yksinkertaisella kriteeristöllä koskien henkilön tai hänen ajoneuvonsa ominaisuuksia. Ennustavan analytiikan avulla voidaan tuottaa kohderyhmänmuodostukseen teknisesti kompleksisia mutta kuitenkin helppokäyttöisiä menetelmiä. Tässä työssä on sovellettu luokittelu- ja regressiomenetelmiä uuden auton ostajien joukkoon. Tämän työn menetelmiksi on rajattu tukivektorikone sekä Coxin regressiomalli. Coxin regression avulla on tutkittu elinaika-analyysien soveltuvuutta ostotapahtuman tapahtumahetken mallintamiseen. Luokittelu tukivektorikonetta käyttäen onnistuu tehtävässään noin 72% tapauksissa. Tukivektoriregressiolla mallinnetun hankintahetken virheen keskiarvo on noin neljä kuukautta. Työn tulosten perusteella myös elinaika-analyysin käyttö ostotapahtuman tapahtumahetken mallintamiseen on menetelmänä käyttökelpoinen.

Luottolaitosten oman pääoman määrän muutos ja Basel II

Luottolaitosten pääoman valvonta on tärkeä osa talouden tasapainon säilyttämisessä. Tässä tutkielmassa tutkitaan miten luottolaitosten pääoman sääntelyn Basel II:n säännönmuutokset vuonna 2007 ovat vaikuttaneet luottolaitosten oman pääoman määrään. Muuttuneiden säännösten vaikutuksia selvitetään tutkimalla eurooppalaisten luottolaitosten omavaraisuusasteita ajalla 1999 – 2009 yleistetyllä lineaarisella regressiolla ja autoregressiivisellä aikasarjamallinnuksella. Talouden suhdanteiden ja vuonna 2007 alkaneen finanssikriisin vaikutukset pääoman määrään huomioidaan bruttokansantuotteen kasvun avulla analyysissa. Tuloksena todetaan, että oman pääoman määrä luottolaitoksissa on vähentynyt merkitsevästi Basel II:n voimaan astumisen jälkeen, mutta muutos on luultavasti aiheutunut talouden laskusuhdanteesta.

Diskreettiaikainen liukuvan moodin säätö kiinteäoksidipolttokennon DC-tasonnostossa

Huoli ympäristön tilasta ja fossiilisten polttoaineiden hinnan nousu ovat vauhdittaneet tutkimusta uusien energialähteiden löytämiseksi. Polttokennot ovat yksi lupaavimmista tekniikoista etenkin hajautetun energiantuotannon, varavoimalaitosten sekä liikennevälineiden alueella. Polttokenno on tehonlähteenä kuitenkin hyvin epäideaalinen, ja se asettaa tehoelektroniikalle lukuisia erityisvaatimuksia. Polttokennon kytkeminen sähköverkkoon on tavallisesti toteutettu käyttämällä galvaanisesti erottavaa DC/DC hakkuria sekä vaihtosuuntaajaa sarjassa. Polttokennon kulumisen estämiseksi tehoelektroniikalta vaaditaan tarkkaa polttokennon lähtövirran hallintaa. Perinteisesti virran hallinta on toteutettu säätämällä hakkurin tulovirtaa PI (Proportional and Integral) tai PID (Proportional, Integral and Derivative) -säätimellä. Hakkurin epälineaarisuudesta johtuen tällainen ratkaisu ei välttämättä toimi kaukana linearisointipisteestä. Lisäksi perinteiset säätimet ovat herkkiä mallinnusvirheille. Tässä diplomityössä on esitetty polttokennon jännitettä nostavan hakkurin tilayhtälökeskiarvoistusmenetelmään perustuva malli, sekä malliin perustuva diskreettiaikainen integroiva liukuvan moodin säätö. Esitetty säätö on luonteeltaan epälineaarinen ja se soveltuu epälineaaristen ja heikosti tunnettujen järjestelmien säätämiseen.

Teollisuusprosessien signaalien automaattinen analysointi

Diplomityössä kehitettiin menetelmiä teollisuusprosessien signaalien automaattiseen havainnointiin ja luotiin työkalu tulosten esittämiseen. Työn tarkoituksena on nopeuttaa ja helpottaa prosessin ongelmien ratkaisua luokittelemalla signaalit matemaattisten menetelmien avulla. Koska prosessin mittaussignaalit ovat pääasiassa stokastisia, eli niitä ei voida etukäteen ennustaa, käsitellään signaaleita tilastomatemaattisin keinoin. Työstä rajattiin mittaushistorian käyttö, joten värähtelyiden tunnistus toteutettiin taajuusanalyysin avulla. Korrelaation avulla löydetään samankaltaiset signaalit. Testeissä todettiin, että työssä kehitetyt havainnoinnit toimivat eri näytteenottotaajuuksilla ja työkalun suoritusnopeus todettiin hyväksi. Lopuksi esiteltiin todellinen teollisuusprosessin ongelma ja siihen mahdollisia ratkaisuja.