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Lokalna sekundarna regulacija napetosti elektroenergetskega sistema z upoštevanjem vpliva sončnih elektrarn
BANOVIĆ, DEJAN (Author), Gubina, Andrej (Mentor) More about this mentor... This link opens in a new window

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Abstract
Namen magistrskega dela je bil izdelati model lokalne sekundarne regulacije napetosti na osnovi umetnih nevronskih mrež (LSRN-UNM) in sposobnost regulacije modela preveriti s simulacijo na modelu elektroenergetskega sistema (EES), ki ima spremenljivo količino inštalirane moči sončnih elektrarn (SE). V magistrski nalogi smo najprej predstavili osnove delovanja regulacije napetosti v EES. Nato smo opisali metode za analizo modela EES in pri tem tudi definirali testni model IEEE RTS. Sledil je opis strukture UNM ter predstavitev Levenberg-Marquardtovega algoritma, ki smo ga uporabili za učenje. Učno množico, ki smo jo dobili z velikim številom simulacij modela EES smo uporabili za učenje LSRN-UNM, kateremu je sledila integracija naučenih LSRN-UNM v model EES. Opazovali smo delovanje LSRN-UNM v različnih letnih časih in za različne deleže sončnih elektrarn v EES. Ugotovili smo dobro delovanje LSRN-UNM, razen v primerih, ko je bil EES v stanju, ki ni bilo zajeto v učni množici.

Language:Slovenian
Keywords:regulacija napetosti, umetne nevronske mreže, simulacija EES, sončne elektrarne, prenosno omrežje
Work type:Master's thesis/paper (mb22)
Organization:FE - Faculty of Electrical Engineering
Year:2014
Views:1132
Downloads:536
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Secondary language

Language:English
Title:Local secondary voltage control of the power system accounting for the impact of the solar power plants
Abstract:
Objective of this master thesis was to design a model of local secondary voltage control with artificial neural networks and to verify its voltage control capability with simulations using a power system model with variable share of solar power plants. Firstly, we described the basics of voltage control in power systems. Afterwards, methods for power systems analysis and test power system IEEE RTS were defined. Artificial neural networks and Levenberg-Marquardt learning algorithm, which was used for learning process, were also described. Training data was acquired with large number of simulations and then used for learning of artificial neural networks of local secondary voltage control, which were then integrated in power system model. We observed local secondary voltage control for different seasons and different shares of solar power plants. Local secondary voltage control operated well, except when power system model was in a state not covered by training data.

Keywords:voltage control, artificial neural networks, power system simulation, solar power plants, transmission system

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