Artificial neural networks for load flow and external equivalents studies
| Autor: | Marcos J. Rider, Heloisa H. Müller, Carlos A. Castro |
|---|---|
| Rok vydání: | 2010 |
| Předmět: |
Engineering
Artificial neural network business.industry Computer Science::Neural and Evolutionary Computation Energy Engineering and Power Technology AC power Levenberg–Marquardt algorithm Flow (mathematics) Control theory Multilayer perceptron Equivalent circuit Point (geometry) Electrical and Electronic Engineering business Algorithm Dimensioning |
| Zdroj: | Electric Power Systems Research. 80:1033-1041 |
| ISSN: | 0378-7796 |
| DOI: | 10.1016/j.epsr.2010.01.008 |
| Popis: | In this paper an artificial neural network (ANN) based methodology is proposed for (a) solving the basic load flow, (b) solving the load flow considering the reactive power limits of generation (PV) buses, (c) determining a good quality load flow starting point for ill-conditioned systems, and (d) computing static external equivalent circuits. An analysis of the input data required as well as the ANN architecture is presented. A multilayer perceptron trained with the Levenberg–Marquardt second order method is used. The proposed methodology was tested with the IEEE 30- and 57-bus, and an ill-conditioned 11-bus system. Normal operating conditions (base case) and several contingency situations including different load and generation scenarios have been considered. Simulation results show the excellent performance of the ANN for solving problems (a)–(d). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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