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Journal of Applied Sciences
  Year: 2007 | Volume: 7 | Issue: 7 | Page No.: 1078-1084
DOI: 10.3923/jas.2007.1078.1084
Neural Network Generalized Predictive Control of the Unified Power Flow Controller
Soraya Zebirate, Abdelkader Chaker and Ali Feliachi

The Unified Power Flow Controller (UPFC) is the most comprehensive tool for real time control of alternative transmission systems. It can be used to control the transmitted real and reactive power flows through a transmission line. Different control techniques for the UPFC system have been proposed. This present study investigates an efficient and robust control method for the UPFC in order to improve the stability of the power system, thus providing the security for the increased power flow. It is now becoming clear that only the classical method based on information processing tools issued from artificial intelligence may lead to a new stage in the automatic control technology. With Artificial Neural Networks (ANNs) issues such as uncertainty or unknown variations in plant parameters and structure can be dealt with more effectively and hence improving the robustness of the control system. The basic idea of a Neural Network Generalized Predictive Controller (NNGPC) is to calculate a sequence of future control signals in such a way that it minimizes a multistage cost function defined over a prediction horizon. The NNGPC performances are compared in terms of reference tracking, sensitivity to perturbations and robustness against line transmission parameters variations.
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How to cite this article:

Soraya Zebirate, Abdelkader Chaker and Ali Feliachi, 2007. Neural Network Generalized Predictive Control of the Unified Power Flow Controller. Journal of Applied Sciences, 7: 1078-1084.

DOI: 10.3923/jas.2007.1078.1084