Optimal load frequency control of two area power system

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Oluseyi, P.O.
Akinbulire, T.O.
Yellowe, K.M.
Babatunde, O.M.
Alayande, A.S.
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Faculty of Engineering, Ahmadu Bello University, Samaru-Zaria, Nigeria
Modern power systems are operated under various constraints which are meant to ensure an appropriate delivery of service. Meanwhile power system faces imbalance in power generation and its consumption in which the higher the load consumption the lower the frequency of operation. The governor-turbine combination will then experience a devastating reduction in its frequency of operation due to disturbance that may lead to system collapse. Both governor and turbine are included in the model of this power system. The control objective is to regulate the frequency error, tie-line power error and area control error despite the presences of external load disturbance (0.01 pu) and system uncertainties. Various control policies were investigated using various combinations of system parameters on a platform of a series of combination of the PI, fuzzy logic and neuro-fuzzy controller with the power system for better stability. This could be found in the other approaches. The neuro-fuzzy based controller load frequency controller is simulated on this two-area interconnected nonlinear power system. To verify the performance of the various controllers, the data from a typical hydrothermal power grid was adapted for the study. From the simulation results; it was recorded the neuro-fuzzy controller enjoyed a settling time of 5 seconds while under the same operating condition the system stability is achieved at 12 seconds using the PI controller. This, thus, demonstrates the robustness of the neuro-fuzzy controller in contrast to the fuzzy logic and proportional-integral (PI) controllers. This thus shows that the neuro-fuzzy logic controller is superior to the other two considered in this work. Hence for a two-area network, the neuro fuzzy approach is recommended for the steady state operation of the system so as to ensure the dynamic stability of the network.
power system, load frequency, neuro-fuzzy, controller, hydro-thermal, proportional-integral