doktorska disertacija
Marko Kolenc (Author), Boštjan Blažič (Mentor)

Abstract

Doktorska disertacija sega na področje kakovosti električne energije in pametnih distribucijskih omrežij v elektroenergetiki. V prvem poglavju so predstavljeni glavni pojmi in smernice v elektroenergetiki. V Sloveniji in svetu se poraba energije neprenehoma zvišuje. Tako je lahko pričakovati, da bomo leta 2035 porabili približno 36 % več energije kot leta 2000. Od tega se bo najbolj povečala raba električne energije, do leta 2035 za približno 70 %, največjo rast pa se pričakuje v državah v razvoju. Med večjimi izzivi, s katerimi se bo Evropa morala soočiti v prihodnjem desetletju, so poleg gospodarske krize, tudi energetski izzivi. Konkurenčnost našega gospodarstva je močno odvisna od zanesljive oskrbe z energijo: varna, zanesljiva, trajnostna in cenovno sprejemljiva energetska oskrba je ključna pri gospodarskih in strateških interesih Evropske zveze kot globalne igralke. Ta dejstva vodijo v razvoj nove dobe v elektroenergetiki in jo lahko povzamemo z besedno zvezo »pametna omrežja«. V tem poglavju je razložena točna definicija pametnih omrežij in njegovih gradnikov. Poudarek je tudi na razpršenih virih (RV), kot krivcih za probleme, ki se pojavljajo v omrežju. Opisana in nakazana je tudi nadaljnja smer razvoja distribucijskih omrežij v Sloveniji, skupaj z novimi tehnologijami. V celotnem poglavju bralec dobi hiter in nazoren vpogled v trenutno in bodoče stanje v elektroenergetiki. Tehnološki razvoj, kateremu smo priča v zadnjem desetletju, je omogočil prihod novih tehnologij, ki so gradniki pametnih omrežij. Za njihovo uspešno delovanje je potrebno usklajeno delovanje vseh njegovih delov, tako obstoječih, kot novih: klasičnih proizvodnih enot, razpršenih virov, komunikacijskih in informacijskih tehnologij (IKT), delujočo regulacijo virov z možnostjo upravljanja porabe, naprav močnostne elektronike, sistemov naprednega daljinskega merjenja itd. V drugem poglavju so predstavljene vse ključne tehnologije in tudi koncepti regulacije virov in bremen v pametnih omrežjih. Najprej so predstavljene tehnologije proizvodnje električne energije, to so male hidroelektrarne, vetrne elektrarne, sončne elektrarne, kogeneracijske elektrarne ipd. Nato so na kratko predstavljeni tudi električni stroji za proizvodnjo iz razpršenih virov, to so asinhronski generator, sinhronski generator in močnostni pretvorniki. Predstavljeni so tudi povsem novi elementi, ki jih do sedaj v distribucijskih omrežjih nismo srečevali, to so regulacijski SN/NN transformator, ki lahko spreminja napetostno stopnjo med obratovanjem, hranilniki energije (baterije, črpalne elektrarne, vztrajniki, …). Da distribucijskemu omrežju sploh lahko rečemo »pametno« omrežje, mora imeti vzpostavljen sistem informacijskih in komunikacijskih tehnologij. IKT so eden ključnih gradnikov pametnih omrežji. Polna uresničitev koncepta pametnega omrežja zahteva IKT povezave do vsakega elementa v elektroenergetskem sistemu (EES), torej mora vključevati elemente proizvodnje, prenosa, distribucije in segati tudi do vsakega končnega odjemalca. V tem poglavju so na kratko predstavljene potrebe in rešitve, ki so izvedljive in uporabne v pametnih omrežjih. Nadzor in upravljanje velikega števila priključnih mest (odjemalcev in proizvajalcev) zahteva razvoj posebnih IKT rešitev. Na koncu poglavja sta predstavljena še dva koncepta vodenja razpršenih virov in bremen, to sta upravljanje s porabo in virtualna elektrarna, ki je skupek razpršenih virov, vodenih iz skupnega centra za nadzor, ki nastopajo na trgu kot ena velika elektrarna. Eden glavnih problemov, ki jih prinašajo razpršeni viri, so težave z napetostjo. Razpršeni viri lahko vzdolž voda, zaradi injekcije delovne moči, dvignejo napetostni nivo preko zakonsko dovoljene meje, kar lahko povzroči poškodbe naprav, priklopljenih na omrežje, omrežja samega in tudi izpad napajanja, kar ima za posledico finančno breme. V doktorski disertaciji največ pozornosti posvečamo ravno problematiki vzdrževanja ustreznega napetostnega profila. V tretjem poglavju je problem porasta napetosti opisan z uporabo enačb, ki ponazarjajo realne razmere v omrežju. Opisan je klasičen način regulacije napetosti, ki pa kmalu ne bo več zadosten. Tradicionalna rešitev omenjene težave bi bila ojačenje elektroenergetskega omrežja z dodatnimi vodi in transformatorji oz. s povečanjem presekov vodnikov in s tem predimenzioniranjem omrežja. Takšne rešitve so sicer zanesljive, vendar so zaradi okoljevarstvenih razlogov, zakonskih omejitev in tudi cene, v večini primerov izredno draga in ekonomsko neupravičena rešitev. Eden od alternativnih pristopov je na splošno tehnološki pristop, kar pomeni, da se z minimalnimi vlaganji v omrežje izkoristi nove možnosti regulacije, kjer sistem to dopušča. V svetu se že pojavljajo razne ekonomsko ugodne rešitve, ki pa ne prinašajo trajnostne rešitve in poljubne optimizacije v omrežju; te rešitve so predstavljene v nadaljevanju. Do nedavnega, so razpršeni viri delovali s konstantnim faktorjem moči (cos= 1). Torej niso sodelovali pri regulaciji napetosti. Glede na to, da je ravno razpršena proizvodnja glavni krivec za poraste napetosti, je prav, da za to prevzamejo določeno odgovornost in obvezno sodelujejo pri sistemskih storitvah. Zadnjih nekaj let se že pojavljajo ideje in zahteve v tej smeri. Tako so bila v Sloveniji leta 2011 sprejeta navodila za priključevanje razpršene proizvodnje, kjer je bila določena statična Q(U) karakteristika za razpršeno proizvodnjo; ta so podrobneje predstavljena v tem poglavju. Na podlagi lokalne meritve napetosti in trenutne proizvodnje delovne moči razpršenega vira, se določi koliko jalove moči mora proizvajati razpršeni vir.  V elektroenergetiki se za opis realnih naprav pogosto uporablja ekvivalenten model vezja. Z uporabo teh modelov lahko, raje kot da s fizičnim testiranjem na terenu ali laboratoriju, njihovo obnašanje opišemo z matematičnimi enačbami. V četrtem poglavju je zato predstavljeno modeliranje elementov, ki so uporabljeni v simulacijah. To so bremena, razpršeni viri, vodi in transformatorji. Posebno pozornost smo namenili modeliranju bremen na NN strani. Pri analizi VN in SN omrežij lahko obremenitvene diagrame bremen (in generatorjev) opišemo s tipičnimi krivuljami, ki podajajo pričakovan odjem v določenem času. Analiza NN omrežja pa zahteva povsem drugačen pristop. Odjem na tem napetostnem nivoju je praviloma povsem naključen. Zato je potrebno za analize razmer v NN omrežjih uporabiti stohastične modele uporabnikov. Nadalje, smo preučili zmožnost uporabe metode Monte Carlo za ocenjevanje dovoljene količine razpršenih virov v NN omrežju. Dandanes načrtovanje omrežij poteka tako, da načrtovalec predpostavi maksimalno porabo in nato izračuna pretoke moči. Če je katerikoli izmed kriterijev prekoračen, je potrebno omrežje ojačiti z dodatnimi vodi ali transformatorji. Zaradi okoljskih vidikov, zakonskih omejitev, cene in uvajanja novih elementov v omrežje, tak pristop ni več zadosten. Načrtovanje je potrebno nadgraditi na višjo stopnjo, ki bo omogočala boljši izkoristek sistema. Zaradi stohastične narave NN omrežij, se zdi najbolj primeren statističen pristop. Začetna ideja statističnega pristopa načrtovanja predpostavlja, da je izvedba simulacij in načrtovanje glede na najslabše možne razmere v omrežju, preveč pesimistično. Opisana je metoda za načrtovanje NN omrežij z visoko stopnjo razpršene proizvodnje, ki ima za osnovo pristop na podlagi Monte Carlo metode in statistične analize podatkov. Z naključnimi vzorci in ponavljanjem poskusov lahko predvidevamo oz. napovemo kakšne bodo napetostne razmere v prihodnosti. Na podlagi teh podatkov lahko izberemo najboljše, oz. najcenejše rešitve in jih pravočasno umestimo v plan razvoja omrežja. Rezultat metode so krivulje, ki podajajo verjetnost nastopa neustreznih napetostnih razmer, kot funkcija inštalirane moči razpršenih virov. Metodo se da smiselno uporabiti tudi v SN omrežju. V okviru doktorske naloge je bilo analizirano tudi delovanje več optimizacijskih metod, ki služijo kot podlaga za izdelavo novih algoritmov vodenja in načrtovanja v pametnih omrežjih. Izsledki so predstavljeni v petem poglavju. Algoritmi slonijo na dejstvu, da bo večina distribucijskega omrežja v naslednjih desetih letih pokrita z meritvami v realnem času. Merjeni podatki iz pametnih števcev služijo kot vhodni parametri za koordiniran nadzor napetosti. Ta se izvaja v skupnem centru vodenja. Kot izhodni parameter tega algoritma, so nove obratovalne točke generatorjev in napetostna stopnja regulacijskega transformatorja v RTP. Regulacijski algoritem je izdelan tako, da omogoča vzpostavitev trga z jalovo močjo. Razpršeni viri podajo svoje ponudbe, nato pa algoritem izvede optimizacijo in poda nove obratovalne točke za razpršene vire, da so obratovalni stroški najmanjši. S tem pristopom pridobijo vsi akterji v omrežju. Najprej je predstavljena analiza razmer ob pogoju, da vsi razpršeni viri obratujejo z enakim tg. Če razpršeni viri delujejo po principu statične karakteristike, morajo viri, ki so bolj oddaljeni od razdelilne transformatorske postaje (RTP) vzdolž izvoda, proizvajati oz. porabljati več jalove moči, kot tisti na začetku izvoda, kar ima za posledico hitrejše staranje naprav (pretvornikov) in potrebno predimenzioniranje naprav. Ena izmed možnosti, ki se ponuja, je uporaba enotnega oz. skupnega tg za vse generatorje v omrežju. Tg je razmerje med generatorjevo trenutno proizvodnjo jalove in delovne moči in se lahko interpretira kot obdavčitev generatorjev z jalovo močjo. Z izvedbo simulacij je narejena popolna analiza obratovalnih razmer v primeru takšnega obratovanja. Nadalje smo analizirali razmere, pri čemer pustimo, da se cene na trgu same izoblikujejo. Dodatno pa je potrebno, ko je kriterijska funkcija definirana, izbrati ustrezno metodo, s katero bomo iskali optimalno rešitev. Problem, ki ga moramo rešiti je problematičen zato, ker je vpliv na izgube nelinearen. Optimizacija enega generatorja vpliva na izgube v celem omrežju. Njihov vpliv pa je različen za vsak generator posebej. Dejstvo, da so izgube v vejah nelinearna funkcija injekcij moči v vozliščih, otežuje reševanje optimizacijskega problema. Z majhnimi injekcijami jalove moči v postopku minimizacije izgub, lahko ta problem lineariziramo. Predstavljena je optimizacijska metoda, ki izrablja dejstvo, da so v pametnem omrežju razpršeni viri med seboj komunikacijsko povezani in da jim je poznana topologija omrežja. Generator lahko spremeni svojo izhodno moč samo tako, da za en korak zmanjša ali zveča izhodno moč. Na ta način algoritem izračuna prihranke moči za oba primera za vse generatorje in nato določi kateri generator bo spremenil svojo izhodno moč v trenutni iteraciji. Po določenem številu iteracij oz. izračunih pretokov moči, se algoritem ustali v določeni delovni točki. S tem pristopom, se problem minimizacije izgub razdeli na več manjših problemov, ki jih je lažje in hitreje rešiti. Izhodi algoritma so nove obratovalne točke generatorjev, ki se jih razpošlje mednje. Če je v omrežju velika količina razpršenih virov, algoritem v ta namen upošteva tudi faktorje občutljivosti in tako tudi v izrednih razmerah določi najcenejšo angažiranost razpršenih virov. Nadalje sta predstavljeni še dve metodi za iskanje optimalne točke obratovanja. Zelo opevana metoda v znanstvenih člankih je uporaba genetskih algoritmov. Logika, na kateri bazirajo, je ta, da naj bi bili procesi v naravi zaradi evolucije najbolj optimalni. Populacija osebkov se skozi generacije razmnožuje in razvija po načelu naravne selekcije, tako preživijo samo najboljši osebki, ki tudi lažje najdejo partnerja za razmnoževanje. Slabši posamezniki znotraj populacije bodo imeli manj možnosti za razmnoževanje in bodo skozi več generacij izginili. Če obnašanje narave opišemo z matematičnimi enačbami, lahko razvijemo uporabno optimizacijsko metodo. Druga metoda, zopet evolucijska, pa je metoda iskanja kukavice oz. metoda kukavičjega gnezda. Raziskave obnašanja živali so pokazale, da se nekatere obnašajo po določenem vzorcu, ki je tipičen za Lévijev let, ki ga oponaša kukavica pri podtikanju jajc. Kukavica išče najbolj primerno gnezdo, da odloči jajce in s tem poveča verjetnost, da potomec preživi. Matematiki so na podlagi tega razvili novo optimizacijsko metodo. Metoda kukavičjega gnezda je relativno nova in ker je publikacij v strokovni literaturi malo, smo jo uporabili kot način za optimiziranje kriterijskih funkcij pri analizi delovanja distribucijskih omrežij. Rezultate pa smo primerjali z ostalimi metodami. Njena prednost je tudi enostavna implementacija. V šestem poglavju smo delovanje novih regulacijskih metod preverili na modelu dveh omrežij. Prvo omrežje je srednjenapetostno in drugo nizkonapetostno, kjer je upoštevana stohastična narava uporabnikov. Najprej so uporabljene najbolj preproste regulacijske metode in nato kompleksnejše. Vsi rezultati so na koncu zbrani v tabeli in ponazorjeni grafično za lažjo primerjavo. Kot omenjeno, smo se v doktorski disertaciji dotaknili tudi problematike načrtovanja NN omrežja. Rezultati podajajo verjetnost, da bomo pri določeni inštalirani moči razpršenih virov imeli težave z napetostjo. Metoda veliko obeta, z njenim nadaljnjim razvojem in dopolnitvami je pričakovati, da bo dobila mesto znotraj bodočih metod načrtovanja omrežij. V sedmem poglavju sledi zaključek, kjer so opisane najpomembnejše ugotovitve doktorske disertacije.

Keywords

aktivna omrežja;distribucijska omrežja;regulacija napetosti;minimizacija izgub;kakovost električne energije;pretoki moči;jalova moč;

Data

Language: Slovenian
Year of publishing:
Typology: 2.08 - Doctoral Dissertation
Organization: UL FE - Faculty of Electrical Engineering
Publisher: [M. Kolenc]
UDC: 621.311.1(043.3)
COBISS: 280300032 Link will open in a new window
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Other data

Secondary language: English
Secondary title: COORDINATED VOLTAGE CONTROL IN DISTRIBUTION NETWORKS WITH DISTRIBUTED GENERATION VARIOUS REACTIVE POWER OPTIONS
Secondary abstract: Doctoral dissertation extends the scope of power quality and smart distribution networks in power engineering. The first section presents the key concepts and guidelines in power engineering. The world power consumption is increasing. Thus, we can expect that we will spend in 2035 approximately 36 % more energy than in 2000, of which the electricity consumption will increase the most, for about 70 %. The largest growth is expected in developing countries. Among the major challenges that Europe will have to face in the coming decade in addition to the economic crisis is the energy crisis. Competitiveness of our economy is heavily dependent on energy security: safe, secure, sustainable and cheap energy supply is crucial to the economic and strategic interests of the European Union as global player. These facts lead to the development of a new era in electricity and can be summarized by the phrase »smart grid«. This chapter explains the exact definition of smart grids and its building blocks. The emphasis is also on distributed power sources as the culprit for the problems that occur in the network. Also further direction of distribution networks development, along with new technologies in Slovenia is presented. Throughout the chapter the reader gets a quick and vivid insight into the current and future situation in the electricity sector. Technological progress, which we have witnessed in the last decade, has enabled the arrival of new technologies, which are the building blocks of smart grids. Their successful operation requires harmonized operation of all its parts, both - existing and the new ones: production units, distribution sources, information and communication technologies (ICT), functional control of distributed generation with the possibility of power management, power electronic devices, advanced remote measurement systems, etc.. The second chapter presents the key technologies and concepts of power sources and load control in smart grids. Firstly, the power production technologies are presented, i.e. small hydro power plants, wind farms, solar power plants, cogeneration plants etc. Then the electric machines, used in distribution generation, to produce electricity are presented, such as induction generator, synchronous generator and power converters. New elements, which haven't been used in distribution networks until now, are presented. These are MV/LV OLTC transformer, energy storage (batteries, pumped power plants, flanges …). The distribution network cannot become smart grid if the communication and information technologies are not established. ICT is one of the key building blocks of smart grids. Full realization of the concept of smart grids requires ICT links to each element in the electric power system, so it should include elements of power generation, transmission, and distribution and extend up to the end of each client. In this chapter, the most practical and useful ICT technologies for smart grids are presented. Control and management of a large number of connection points (customers and suppliers) requires the development of specific ICT solutions. Finally, the chapter presents two concept of managing distribution generation loads, namely demand-side management, and virtual power plant, which is a set of distributed generation, managed from a common control center, which appears in the electricity market as one big power plant. One of the main problems posed by distributed generation is related to the voltage rise. Distributed generation can along the feeder, because of the injection of active power, raise the voltage offer the statutory defined limits, which can cause damage to the devices connected to the network, the network itself and the loss of power, resulting in a financial burden. In this doctoral thesis, most attention is paid exactly to the problem of maintaining adequate voltage profile. The third section describes the voltage rise problem using the equations that represent real network conditions. Classical approach of voltage control, which will soon no longer be sufficient, is presented. The traditional solution in these problems would be reinforcing the network with additional transformers and by increasing the conductor’s cross-sections and thus oversizing the networks. Such solutions are reliable, however, due to environmental considerations, legal constraints and also price, in most cases, very expensive and economic unjustified solution. One of the alternative approaches is in general, technological approach, which means that with the minimum investments in the network new control possibilities are exploited where this is possible. In the world there are already new low-costs solutions emerging, which do not bring any sustainable solutions at these time. Until recently, the distributed generation operated with constant power factor (cos= 1). This means that they did not participate in the voltage control. Given that distributed generation is the main culprit for the voltage rise problems; it is fair that they also take some responsibility for this and participate in the voltage control. Over the last few years there have already been few ideas in this direction. Slovenia, for example, issued guidelines for connection of distributed generation where static Q(U) characteristic for distributed generation are prescribed. On the basis of local voltage measurements and active power output, their reactive power output is determined. To describe real electrical devices equivalent circuit model is commonly used. By using these models, rather than by physical testing in the field or laboratory, their behavior can be described by mathematical equations. In the fourth chapter modeling of the elements that are used in in the simulations is presented. These are loads, distributed generation, lines and transformers. Special attention is paid to the modeling of loads on LV side. In the analysis of HV and MV networks typical load diagrams can be used. Analysis of LV networks, however, requires a completely different approach. Consumption on this voltage level is usually completely random. Therefore, it is necessary for the analysis of the LV networks to use stochastic load models. Furthermore, the use of statistical method Monte Carlo is considered when evaluating allowed amount of distributed generation in the LV network. Nowadays, network planning takes place so that the maximum consumption is assumed and then power-flow calculated. If any of the criteria is exceeded, it is necessary to strengthen the network with additional lines or transformers. Due to environmental concerns, legal restrictions, prices and the introduction of new elements in the network, this approach is no longer sufficient. Planning should be upgraded to the next level, which will allow better utilization of the system. Due to the stochastic nature of the LV networks, statistical approach seems most appropriate. The basic idea of statistical approach of planning assumes that the planning for the worst possible conditions in the network is too pessimistic. That is why statistical approach using Monte Carlo method is presented. With random samples and repetition of experiments voltage situation in the future can be predicted with desirable level of accuracy. Based on the results we can choose the best or the cheapest solutions and fit them into the network development plan. The results of the method are curves that give the probability of inadequate voltage conditions as a function of hosting capacity. Method can be easily applied to MV network analysis. The thesis analyses also the functioning of several optimization methods that serve as the basis for the creation of new control algorithms to be used in smart grids. The results are presented in the fifth chapter. Firstly, analysis of the network situation where all the generation operates with the same tan is presented. If a customer lives far away from the substation, where voltage deviations are more frequent, the inverters will have to inject or consume more reactive power than for those located in areas where the voltage deviations are minor. Also their location may change depending on how the entire distribution system is configured. As retail customers typically have no choice where they are located along the feeder, it seems inappropriate that they are required to produce or consume a large amount of reactive power and hence, take all the burden and responsibility for the voltage rise along the entire feeder. Reactive power consumption/generation contributes to aging of inverters and requires also their oversizing possible solutions is that all the generation on one feeder operates with uniform tanφ. Uniform tanφ can be seen as a tax system with a constant marginal tax rate in the business world. The situation when all distribution generation operated in such a way is presented. Next, the control algorithm which allows establishment of reactive power market is presented. The algorithm is, as also previous algorithm, based on the fact that most of the distribution network will be covered with two-way communications and measurement devices in the next ten years. The algorithm takes advantage of real time data measurements from the network. The load-flow algorithm is implemented into the coordinated control which, for every generator separately, determines the optimal operating point using a modeled network. The heart of the control system is a load-flow algorithm, which in small steps minimizes the losses using a modelled network. Possessing periodically power measurements, the simulations are carried out to minimize the reactive power flow. In a number of load-flow steps the optimal distributed generation’s reactive power is determined and new set points are sent to the generators to correlate their outputs. Coordinated control also takes advantages of unused reactive power capabilities of inverters and enables them to participate in emerging markets with reactive power. With this approach every actor in the network benefits. The problem that it has to be solved is problematic because the impact on the losses is nonlinear. Optimizing a generator affects the losses in the whole networks. Their impact is different for each generator. The fact that losses in the branches are nonlinear function of injected power in the nodes makes it difficult to solve the optimization problem. With small injections of reactive power this problem can be linearized. Generator may change its output only by one step to reduce or increase the power output. In this way, the algorithm can calculate savings for both cases for all generators and then determine which generator will change its output in the current iteration. After a certain number of iterations or load-flow calculation, the algorithm stabilizes in a particular operating point. With this approach, the problem of losses minimization is divided into a number of smaller problems, which are easier and faster to solve. If there is a large amount of distributed generation in the network, the algorithm uses sensitivity factors theory and in this way in case of emergency still the cheapest engagements of distributed generation is determined. Furthermore, the two methods are presented, which are used for optimal engagement of distributed generation. Very vaunted method in scientific articles is to use genetic algorithms. The logic behind this is that processes in the nature are due to the evolution the most optimal. Population of individuals multiplies through the generations and develops through the principle of natural selection, so only the best specimens find a partner for reproduction. Poorer individuals within a population will have fewer chances to reproduce and will disappear over many generations. If the nature of this behavior can be described by mathematical equations, we can develop a useful optimization method. Another method, again based on evolution principles, is the cuckoo search algorithm. Studies of some animals have showed that Levi’s flights can be used to describe their behavior. Cuckoos “use” this technique when searching a proper nest to lay an egg. The nest has to be carefully chosen so that the hosting bird cannot find the parasite egg. Mathematicians have developed an optimization method on the basis of this behavior. This method is relatively new and because there is little publication on this topic in scientific literature, we used this method to optimize the operation costs of distribution networks. The results are compared with other methods. Its advantage is also the ease of implementation. In the sixth chapter control algorithms are tested by simulation on two real distribution networks. The first is a medium-voltage and second low-voltage network, which takes into account the stochastic nature of the users. Firstly, the most simple control is used, and then more complex. All results are at the end of this chapter tabulated and illustrated graphically to facilitate comparison. As mentioned above, the doctoral thesis touches the problem of low-voltage network planning. The results give a probability that there will be a problem with the voltages in the network at certain point of installed distribution generation method has a great potential, with its further development and modifications it is expected to find its place within the future network planning methods. The seventh chapter concludes the doctoral dissertation and pinpoints the lessons learned.
Secondary keywords: Elektroenergetski sistemi;Disertacije;Elektroenergetika;Pametna omrežja;
Type (COBISS): Dissertation
Study programme: 1000319
Embargo end date (OpenAIRE): 1970-01-01
Thesis comment: Univ. v Ljubljani, Fak. za elektrotehniko
Pages: VIII, 163 str.
ID: 8757026