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|a 20220128d2021 k y0engy50 ba
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|a eng
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|a drcn ---uucaa
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|a i
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|a b
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|a Automation of the Adaptive Heavy-Loaded Trajectory Identification Algorithm
|f N. L. Batseva, V. A. Sukhorukov
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| 203 |
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|a Text
|c electronic
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| 300 |
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|a Title screen
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| 320 |
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|a [References: 13 tit.]
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|a The aim of this research is a software implementation of the adaptive heavy-loaded trajectory identification algorithm by development of the program and its testing at controlled sections of 500 kV power grid. Previously, developed algorithm of the adaptive heavy-loaded trajectory identification makes it possible to identify the heavy-loaded trajectory using a current power system digital model relatively to a current power system state. The algorithm, in calculating limited active power flows in terms of small-signal aperiodic stability and monitoring small signal aperiodic stability violation in the researched controlled section precisely, uses such criteria as voltage levels at the ends of researched and adjacent controlled sections connections, as well as normalized angles through these connections. A software implementation of the adaptive heavy-loaded trajectory identification algorithm is performed using the object-oriented programming language C# in the development environment Microsoft Visual Studio applying AstraLib library of RastrWin3 software. The program is tested at controlled section No.1, which is the part of 500 kV transit of the chain structure. Calculated values of limited active power flows manually and using the program applying the adaptive heavy-loaded trajectory identification algorithm differ by 62 MW or 2.3%, which does not exceed 5% error value. Developed “Adaptive heavy-loaded trajectory identification” program allows calculating values of limited active power flows for a current power system state automatically. A promising direction of the program development is decreasing the running time of program operation and program modification for the identification of the adaptive heavy-loaded trajectory in circular and multi-closed structures.
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| 463 |
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|t Ural Smart Energy Conference (USEC)
|o Ural-Siberian Smart Energy Conference (USSEC), 13-15 November 2021, Novosibirsk, Russian Federation
|v [P. 125-129]
|d 2021
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| 610 |
1 |
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|a электронный ресурс
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| 610 |
1 |
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|a труды учёных ТПУ
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| 610 |
1 |
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|a program
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| 610 |
1 |
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|a algorithm
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| 610 |
1 |
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|a adaptive heavy-loadedtrajectory identification
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| 610 |
1 |
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|a controlled section
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| 610 |
1 |
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|a small-signal aperiodicstability
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| 610 |
1 |
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|a stability margin monitoring system
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| 610 |
1 |
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|a идентификация
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| 610 |
1 |
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|a траектория
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| 610 |
1 |
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|a устойчивость
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| 610 |
1 |
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|a системы контроля
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| 610 |
1 |
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|a автоматизация
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| 610 |
1 |
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|a адаптивные алгоритмы
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| 700 |
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1 |
|a Batseva
|b N. L.
|c Specialist in the field of electric power engineering
|c Associate Professor of Tomsk Polytechnic University, Candidate of technical sciences
|f 1968-
|g Natalya Lenmirovna
|3 (RuTPU)RU\TPU\pers\31306
|9 15484
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| 701 |
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1 |
|a Sukhorukov
|b V. A.
|g Vasily Aleksandrovich
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| 712 |
0 |
2 |
|a Национальный исследовательский Томский политехнический университет
|b Инженерная школа энергетики
|b Отделение электроэнергетики и электротехники
|3 (RuTPU)RU\TPU\col\23505
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| 801 |
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2 |
|a RU
|b 63413507
|c 20220128
|g RCR
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| 856 |
4 |
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|u https://doi.org/10.1109/USSEC53120.2021.9655748
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| 942 |
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|c CF
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