Abstract
Improvement of Power System Small-Signal Stability by Artificial Neural Network Based on Feedback Error Learning
Highlights
Electrical power system supplying electricity to facilities, commercial and residential users, is normally a huge and complex system
The contribution of this study is that a very effective control method called feedback error learning (FEL) is used to improve the smallsignal stabilities of an electric power system that is composed of a synchronous generator and an infinite bus connected to it, as it is well known that power system stabilizers (PSS) are used to damp the oscillations for the small signal stability
It consists of a synchronous generator, an external exciter, a driver that is a combination of a turbine and governor, a power system stabilizer (PSS), an automatic voltage regulator (AVR) and an infinite bus to which the synchronous machine is connected
Summary
Electrical power system supplying electricity to facilities, commercial and residential users, is normally a huge and complex system. In order to control frequency of loads for stochastic power systems, a deep reinforced learning method is proposed in [26] These studies were done to obtain robust systems and better operating performance, they still have some difficulties for achieving stability in some operating conditions. The contribution of our study is in the use of the artificial intelligent technique within the framework of feedback error learning control scheme in order to improve the small-signal stability of the power system composed of a synchronous machine connected to an infinite Bus. We use the Self-Adaptive Discrete Time MIMO Linear Neural Network (ADALINE) tool for this study [31, 32].
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