Qi HUANG, NgocKhoat NGUYEN, Thi-Mai-Phuong DAO

An investigation of intelligent controllers based on fuzzy logic and artificial neural network for power system frequency maintenance

In this paper, the design of 3 intelligent control strategies applying fuzzy logic (FL) and artificial neural network (ANN) techniques is investigated to deal with network frequency maintenance against load variations in a largescale multiarea interconnected power system. These intelligent frequency controllers proposed in this study include FL-PI, ANN-NARMA(nonlinear autoregressive moving average)-L2, and ANN-RMC (reference model control). In principle, they are designed depending upon the tie-line bias control method, which has been applied efficiently for damping frequency oscillations. A mathematical model of an n-control-area interconnected power system with different generation units is built first to apply the control methodologies in order to maintain the grid frequency at its nominal value (50 Hz or 60 Hz). Such a model is considered to be typical candidate of a complicated large-scale power system in reality. Numerical simulation with various cases of load conditions are also implemented in this study using the MATLAB/Simulink package to demonstrate the feasibility and effectiveness of the proposed control strategies. It is found that the 3 intelligent controllers presented in this paper are capable of achieving superiority over the conventional integral regulators in system frequency stabilization. The main dynamic control indexes obtained, especially the overshoot and settling time, are highly promising to effectively extinguish the dynamic responses of the frequency and tie-line power deviations. Thus, the steady state of the power network can be restored more quickly after load variation occurrence. In that way, the stability, reliability, and economy of an electric power grid are able to be guaranteed effectively

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