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The paper deals with Power System State Estimation (PSSE) that plays essential role in modern dispatcher centres. The aim of the paper is to present results of original investigations on change of properties of PSSE when a symmetrical phase shifter is installed in a Power System (PS). The mentioned phase shifter is one of the types of phase shifters. Such the phase shifter is installed in the Polish Power System. In the paper, the following indices characterizing properties of PSSE are considered: the number of iterations in PSSE calculation, the conditionality of the solved equations and accuracy of estimation. The investigations are made using the IEEE 14-bus test system. The properties of PSSE are analysed when there is the phase shifter and when there is not such the device in PS for the polar and rectangular coordinate systems. The investigations are so organized to take into account possibly all space of states of PS. Results of the investigations show that there is noticeable change of the properties of PSSE as an effect of installation of the phase shifter in PS. In the paper, also such the cases are indicated, in which those changes are the smallest. DOI: http://dx.doi.org/10.5755/j01.eie.24.4.21473

Biokuro deginimas – sparčiai šalyje populiarėjantis šilumos energijos gamybos šaltinis. Valstybėje pastatyta dešimtys biokurą deginančių katilinių, vykdoma tolesnė plėtra. Dėl kuro specifikos, šiuolaikines katilines galima laikyti didelio masto pramoniniais objektais. Norint užtikrinti patikimą jų funkcionavimą būtinas pastovus įrangos aptarnavimas ir techninė priežiūra. Siekiama sukurti pavyzdinį katilinės modelį, kurį lyginant su eksploatuojamu objektu būtų galima pastebėti atsirandančius technologinių parametrų nukrypimus, realizuoti išankstinį galimų gedimų diagnozavimą, bei šalinimą. Darbe apžvelgiami tinkamiausi modeliavimo metodai, jų pritaikymo galimybės. Išskiriami svarbiausi parametrai, kurie apibrėžia pakuros darbo režimą, analizuojama 5,3MWh galios katilinė su kondensaciniu ekonomaizeriu, dirbanti ištisus metus, skirtingo apkrovimo sąlygomis. Surinkti ir apdoroti kelių mėnesių istoriniai duomenys. Neuroninių tinklų metodu sukuriamas pakuros modelis. Naudojantis juo realizuojama katilinės darbo režimo stebėjimo sistema, kuri praneša naudotojui apie pastebėtus nukrypimus, ir leidžia prognozuoti galimai juos sukėlusius įėjimo parametrus. Pateikti sistemos bandymų rezultatai., The use of biomass for energy production is becoming increasingly popular in Lithuania. There are tens of already operating power plants, and the expansion of this energy sector is still present. Because biomass is not of volatile nature, the machinery required for automated burning process is more complex compared to traditional fossil fuels. In order to ensure proper function of these plants, regular checks and maintenance must be performed. The goal of this paper is to create an accurate model of the biomass furnace, working in optimal conditions, which could be compared later, to the same furnace, during its operation. This would give the possibility for the responsible personnel to look for deviation in output parameters, which would allow early diagnosis of upcoming problems, thus providing an addition to existing maintenance procedures. The paper describes several possible modeling methods and provides analysis of their suitability for the task. The analysed plant features a condensing economizer and the maximum power capacity is 5,3MWh. Heat is produced all year round, with varying loads on the system. The most important technological parameters, which describe operation of the furnace, are selected and their historical values are collected. A neural network based model is created using the data, together with a special system, which informs the user of parameter deviations. Furthermore, there is a possibility to identify the input parameters, which could have caused the deviation, using evolutional programming method based approach.

Šis darbas susijęs su duomenų gavybos procesu, veiklos intelekto technologijomis ir prognozavimo algoritmais. Darbe analizuojami prognozavimo algoritmai, jų įvertinimo metodikos ir duomenų gavybos technologijos. Yra daugybė prognozavimo algoritmų. Dažnai iškyla klausimas kokį algoritmą pasirinkti prognozavimui ir kaip jį paruošti darbui konkrečiam prognozavimo uždaviniui. Šiame darbe aprašomas sukurtas modelis, kuris leidžia įvertinti ir pagreitinti pakartotiną algoritmų analizę skirtingiems prognozavimo uždaviniams. Modelis naudoja DMX, SQL šablonus ir įvertinimo metodus algoritmų analizei. Remiantis šiuo modeliu ir veiklos intelekto technologijomis aprašoma sukurta prognozavimo metodų analizės sistema. Panaudojant šią sistema darbe parodoma kaip pasirinkti ir paruošti prognozavimo algoritmą darbui su konkrečiu pacientų klasifikavimo uždaviniu., This work is related to the data mining process, Business Intelligence technology and forecasting algorithms. This paper analyzes the forecasting algorithms, their evaluation methods and data mining technologies. There are a number of forecasting algorithms. Often there are questions how to choose the algorithm for prediction and how to prepare for work with a particular prediction task. This paper describes an experimental model which allows to assess and accelerate the re-analysis of different algorithms for prediction tasks. The model is based on the DMX, SQL templates, and assessment methods for analysis of algorithms. According to this model, the experimental system for the analysis of forecasting methods is created through the Business Intelligence technology. Using this system, this work shows how to select and prepare a forecasting algorithm to work with a specific patients classification task.