Browsing by Author "Koivisto, Matti"
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Item Design of a local area network for the DX 200 switching system(1990) Koivisto, Matti; Rahko, Kauko; Sähkötekniikan osasto; Teknillinen korkeakoulu; Helsinki University of Technology; Rahko, KaukoItem Finding Value in Big Data - Statistical Analysis of Large Data Sets with Applications in Electric Power Systems(Aalto University, 2015) Koivisto, Matti; Lehtonen, Matti, Prof., Aalto University, Department of Electrical Engineering, Finland; Mellin, Ilkka, University Teacher Emeritus, Aalto University, Department of Mathematics and Systems Analysis, Finland; Sähkötekniikan ja automaation laitos; Department of Electrical Engineering and Automation; Power Systems and High Voltage Engineering; Sähkötekniikan korkeakoulu; School of Electrical Engineering; Lehtonen, Matti, Prof., Aalto University, Department of Electrical Engineering, FinlandA growing volume of data is becoming available in the field of electric power systems. The hourly automatic meter reading (AMR) electricity consumption data available from small customers, such as households and small businesses, is a significant new data source. For example, geographic data, wind speed data and phasor measurement unit data add to both the quantity and the significant variety in the available data. This thesis presents how these large data sets can be utilized in power system studies using statistical methodology. A visualization and clustering of a large AMR data set is presented, and consumption models are then estimated for the discovered clusters, i.e., consumer groups. Statistical modelling is applied to wind speed and wind generation data from multiple locations, with the emphasis on understanding the effect of the geographical distribution of wind power. In addition, combined statistical modelling of stochastic distributed generation (e.g., wind and solar power) and electricity consumption is presented, which allows the effects of stochastic generation to be analysed at the distribution system level. Interesting system operation conditions (e.g., power flows, consumption, wind generation) affecting the expected damping of the 0.35 Hz inter-area oscillation in the Nordic power system are identified, and their use in the short term prediction of damping is demonstrated using statistical methods. Several different geographically varying risk factors affecting the expected fault rates in power distribution systems are also identified, and the use of the estimated fault rates in automatic network planning is presented. It is argued that the statistical analysis of electricity consumption and generation can also be used in automatic network planning. Although the volume and variety of data are important in enabling data analyses, the value that can be extracted from the data using appropriate data analysis methods is fundamentally the most important aspect. In this thesis, multiple data visualization techniques are presented for finding patterns in the large data sets. The discovered patterns are then modelled using statistical data models. The need to model the probability distributions of the relevant random variables in detail is emphasized. This is especially important in wind power modelling, and was achieved using Monte Carlo simulation.Item From Control to Competition - Changes in Regulator's a Strategic Position in Telecommunications Markets(2002) Koivisto, Matti; Jormakka, Jorma; Sähkö- ja tietoliikennetekniikan osasto; Teknillinen korkeakoulu; Helsinki University of Technology; Kantola, RaimoItem Laajamittaisen tuulivoimatuotannon tilastollinen analyysi(2014-03-31) Ekström, Jussi; Koivisto, Matti; Matilainen, Jussi; Sähkötekniikan korkeakoulu; Lehtonen, MattiTuulituotannon määrä kasvaa jatkuvasti monissa maissa, ja täten lisääntyneen tuulituotannon vaikutukset sähköjärjestelmään tulevat yhä merkittävämmiksi. Tässä diplomityössä kehitettiin kaksi aikasarjamallia, VAR-malli aikariippuvalla leikkaustermillä muunnetulle datalle ja ARC-malli muunnetulle datalle. Malleja voidaan käyttää Monte Carlo -simulaatioissa sellaisten tilanteiden todennäköisyyksien määrittämiseen, missä esiintyy erittäin suuria tai matalia tuulennopeuksia samanaikaisesti monissa eri kohteissa. Mallien käyttökelpoisuutta arvioidaan kahdenlaisissa tilanteissa, sellaisissa joissa mallinnetaan olemassa olevia kohteita joista on mittausdataa, ja sellaisissa, joissa mallinnetaan uusia kohteita joista ei ole lainkaan mittausdataa. Esitetyt mallit todennetaan vertaamalla niiden antamia simulaatiotuloksia 21:een mittauskohteeseen Suomesta. Lisäksi esitellään esimerkkitilanteita mallien eri sovellusmahdollisuuksista.Item Mobile information system adoption and use: beliefs and attitudes in mobile context(Teknillinen korkeakoulu, 2009) Koivisto, Matti; Tietoliikenne- ja tietoverkkotekniikan laboratorioDuring the last decades scholars and practitioners have been interested in the reasons why users either accept or reject Information Systems (IS). Users' perceptions of information technology have mainly been studied from acceptance, success, or usability perspectives. Although these research approaches have provided valuable information, they all have a limited view. Thus, there is a need for an integrated framework that fulfills the gaps between different approaches. In this study the acceptance and use of mobile systems are analyzed by combining the results of different disciplines. The main result of the study is a new model for Mobile IS Adoption and Use (MISAU). It integrates the elements of technology acceptance, information system success, and usability studies into a single model. As information system acceptance must always be analyzed in context of use, MISAU is based on the mobile service supply chain. The main differences between stationary and mobile systems can be found in network performance and usability of mobile devices. MISAU serves as a framework for case studies in which the effects of these special characteristics on users' perceptions are analyzed. The results of the study indicate that the ever-increasing transmission speeds of mobile networks are not alone adequate to increase the use of mobile services. Perceived quality of service is an outcome of multiple factors. The successful implementation of a mobile IS requires high quality in all elements of service supply chain (i.e. end-user devices, networks, and services). The small size of mobile devices is a serious threat to usability - especially to text entry and navigation within an application. Further studies are still needed in these sectors.Item Probabilistic prosumer node modeling for estimating planning parameters in distribution networks with renewable energy sources(2017) Millar, Robert John; Ekström, Jussi; Lehtonen, Matti; Koivisto, Matti; Saarijärvi, Eero; Degefa, Merkebu; Department of Electrical Engineering and Automation; Power Systems and High Voltage EngineeringWith the increase in distributed generation, the demand-only nature of many secondary substation nodes in medium voltage networks is becoming a mix of temporally varying consumption and generation with significant stochastic components. Traditional planning, however, has often assumed that the maximum demands of all connected substations are fully coincident, and in cases where there is local generation, the conditions of maximum consumption and minimum generation, and maximum generation and minimum consumption are checked, again assuming unity coincidence. Statistical modelling is used in this paper to produce network solutions that optimize investment, running and interruption costs, assessed from a societal perspective. The decoupled utilization of expected consumption profiles and stochastic generation models enables a more detailed estimation of the driving parameters using the Monte Carlo simulation method. A planning algorithm that optimally places backup connections and three layers of switching has, for real-scale distribution networks, to make millions of iterations within iterations to form a solution, and therefore cannot computationally afford millions of parallel load flows in each iteration. The interface that decouples the full statistical modelling of the combinatorial challenge of prosumer nodes with such a planning algorithm is the main offering of this paper.Item A Statistical Modeling Methodology for Long-Term Wind Generation and Power Ramp Simulations in New Generation Locations(2018-09-14) Ekström, Jussi; Koivisto, Matti; Mellin, Ilkka; Millar, Robert; Lehtonen, Matti; Department of Electrical Engineering and Automation; Department of Mathematics and Systems Analysis; Power Systems and High Voltage EngineeringIn future power systems, a large share of the energy will be generated with wind power plants (WPPs) and other renewable energy sources. With the increasing wind power penetration, the variability of the net generation in the system increases. Consequently, it is imperative to be able to assess and model the behavior of the WPP generation in detail. This paper presents an improved methodology for the detailed statistical modeling of wind power generation from multiple new WPPs without measurement data. A vector autoregressive based methodology, which can be applied to long-term Monte Carlo simulations of existing and new WPPs, is proposed. The proposed model improves the performance of the existing methodology and can more accurately analyze the temporal correlation structure of aggregated wind generation at the system level. This enables the model to assess the impact of new WPPs on the wind power ramp rates in a power system. To evaluate the performance of the proposed methodology, it is verified against hourly wind speed measurements from six locations in Finland and the aggregated wind power generation from Finland in 2015. Furthermore, a case study analyzing the impact of the geographical distribution of WPPs on wind power ramps is included.Item Tuntimittausdatan käyttö sähkökuorman ennustamisessa(Aalto University, 2010) Koivisto, Matti; Elektroniikan, tietoliikenteen ja automaation tiedekunta; Lehtonen, MattiTässä työssä tutkitaan sähkökuorman ennustamista regressionanalyysin avulla. Selittävinä tekijöinä käytetään lämpötilaa ja päivänpituutta. Vuoden mittainen tuntimitattua dataa sisältävä aineisto (1.7.2008 - 30.6.2009) saatiin Kainuun Energialta n. 1600 asiakkaalta ja se sisältää lähinnä kotitalouksien sähkönkulutusdataa; tehty ohjelma onkin pääasiassa suunniteltu kotitalouksien sähkönkulutuksen ennustamista varten. Ohjelmalla voi analysoida pienten asiakasryhmien summakulutusta, jolloin voidaan ennustaa esimerkiksi yhden jakelumuuntajan kulutus. Tämän lisäksi voidaan käsitellä suuremman asiakasjoukon keskiarvoa; tätä käytetään lähinnä ohjelman testaamiseen. Koska lämpötilan vaikutus sähkönkulutukseen on epälineaarinen, vuosi jaetaan päiväryhmiin jotka käsitellään erikseen. Ryhmän sisällä oletetaan lineaarinen riippuvuus. Regressioanalyysin antamat selittävien muuttujien kertoimet tarkistetaan automaattisesti ohjelmassa määriteltyjen periaatteiden mukaisesti, jotta ennustemalli olisi järkevä. Tämän lisäksi lasketaan residuaalien hajonta, jolloin voidaan antaa ennusteen lisäksi haluttu luottamustaso. Myös luottamustason käyttöä varten tarvittavaa normaalijakaumaoletusta sekä oletettua residuaalien riippumaattomuutta selittävistä tekijöistä tutkitaan. Lopuksi verrataan ennustemallin antamia arvoja toteutuneeseen vuoden 2010 tammikuun kulutukseen. Tehtyjen testien perusteella ennuste toimii melko hyvin, mutta joitain kysymyksiä jäi vielä auki. Nämä liittyvät lähinnä tapaan käsitellä epälineaarisuus, sekä arvioon analysoitavan ryhmän minimiasiakasmäärästä.Item Unlocking distribution network capacity through real-time thermal rating for high penetration of DGs(2014) Degefa, Merkebu; Humayun, Muhammad; Safdarian, Amir; Koivisto, Matti; Millar, John; Lehtonen, Matti; Power Systems and High Voltage Engineering; Department of Electrical Engineering and AutomationHighly stochastic loading in the emerging active distribution networks means that electric utilities need to use their assets to the fullest by deploying intelligent network management tools. Real-time thermal rating (RTTR) provides possibility for short term and even real-time active distribution network management enabling the network to run closer to an overload state without damage. In this study, a RTTR based active distribution network management framework is formulated giving hour-by-hour network capacity limits. Relationships of stochasticities in customer loads and DG output with thermal responses of underground cables, overhead lines and distribution transformers are explained. RTTR is applied on all distribution network components with simulated scenarios involving various levels of DG penetration. This study quantifies the potential for an increased DG utilization and an increased potential for new DG installations when RTTR is integrated with distribution management systems.