Abstract
In recent years, interest in the application of Artificial Neural Networks (ANN) to electrical power systems has grown rapidly. In particular the use of ANN in the solution of the load-flow problem in wide electrical networks is an interesting research topic, because it constitutes a good alternative to the classical numerical algorithms. In this paper a refined solution strategy based on statistical methods, on a particular Grouping Genetic Algorithm (GGA) and on Progressive Learning Network (PLN) is presented. Tests on the solution of load-flow equations of the standard IEEE 118 bus network confirm the good potential of this approach; in particular the search for optimal values of the PLN’s parameters, with the aim at reducing the number of processing units, reducing further the calculation time, it’s possible to make the procedure presented extremely competitive.
Zusammenfassung
In den letzten Jahren ist das Interesse an künstlichen neuralen Netzwerken (Artificial Neural Networks — ANN) rasch gewachsen. Insbesondere ist die Verwendung von ANN bei der Lösung des Flussproblems bei weiterreichenden elektrischen Netzwerken ein interessantes Thema, da es eine gute Alternative zu klassischen nummerischen Algorithmen darstellt.
Dieser Beitrag stellt eine auf statistischen Methoden beruhende erweiterte Lösung dar sowie eine auf einem besonderen gruppierenden genetischen Algorithmus (Grouping Genetic Algorithms — GGA) und einem progressiven Lern-Netzwerk (Progressive Learning Network — PLN). Prüfungen auf Grund der Lösung von Leistflussgleichungen des Standard-IEEE-118-Bus-Netzwerks bestätigen die guten Möglichkeiten dieser Verfahrensweise; insbesondere ermöglicht die Suche nach optimalen Werten von PLN-Parametern, die auf die Verringerung von Verfahrenseinheiten und damit der Rechenzeit abzielen, eine extreme Konkurrenzfähigkeit des vorgelegten Verfahrens.
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Augugliaro, A., Cataliotti, V., Dusonchet, L. et al. Influence of ANN parameters on the performance of a refined procedure to solve the load-flow problem. Elektrotech. Inftech. 116, 348–353 (1999). https://doi.org/10.1007/BF03159194
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DOI: https://doi.org/10.1007/BF03159194
Keywords
- Artificial Neural Networks (ANN)
- load-flow problem
- Grouping Genetic Algorithm (GGA)
- Progressive Learning Networtk (PLN)