Абстрактный

ECONOMIC LOAD DISPATCH USING GENETIC ALGORITHM AND PATTERN SEARCH METHODS

M. Anuj Gargeya, Sai Praneeth Pabba

In a practical power system, the power plants are not located at the same distance from the centre of loads and their fuel costs are different. Also, under normal operating conditions, the generation capacity is more than the total load demand and losses. Thus, there are many options for scheduling generation. In an interconnected power system, the objective is to find the real and reactive power scheduling of each power plant in such a way as to minimize the operating cost. This means that the generatorâ??s real and reactive powers are allowed to vary within certain limits so as to meet a particular load demand with minimum fuel cost. This is called optimal power flow problem. In this project, ECONOMIC LOAD DISPATCH (ELD) of real power generation is considered. Economic Load Dispatch (ELD) is the scheduling of generators to minimize total operating cost of generator units subjected to equality constraint of power balance within the minimum and maximum operating limits of the generating units. In general by neglecting valve point loading effects, economic load dispatch can be solved either by lambda search or generation search (Pg) methods. In this project, valve point loading effects of the generating units are considered. To solve economic load dispatch, two of intelligent search methods are considered, namely, genetic algorithm and pattern search methods. Equality constraint is satisfied by penalty approach method.Economic load dispatch solved for three typical test cases of 5 generator, 13-generator and 40-generator (Tai-power systems) cases.

Отказ от ответственности: Этот реферат был переведен с помощью инструментов искусственного интеллекта и еще не прошел проверку или верификацию

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Академические ключи
ResearchBible
CiteFactor
Космос ЕСЛИ
РефСик
Университет Хамдарда
научный руководитель
Импакт-фактор Международного инновационного журнала (IIJIF)
Международный институт организованных исследований (I2OR)
Cosmos

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