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GWO Optimized LFC Control for PV Wind Thermal Power system

GWO Optimized LFC Control for PV Wind Thermal Power system


Introduction: 

We delve into the implementation of a Gray Wolf Optimized Load Frequency Controller integrated with renewable energy resources. This innovative concept draws inspiration from a reference paper, providing insights into the integration of variable energy sources into thermal systems for effective load frequency control. The controller architecture, based on a combination of PA+1 and PD controllers, is optimized using the Gray Wolf Optimization algorithm.

Controller Design and Optimization: The proposed load frequency controller comprises two areas: one connected to a photovoltaic (PV) system and the other to a wind energy system. Each area incorporates transfer functions representing the dynamic response of the respective renewable energy sources. The controller parameters, including gains and time constants, are fine-tuned using the Gray Wolf Optimization algorithm to minimize the objective function. This objective function, formulated based on integral time multiplied absolute error (ITA), serves as a metric for optimizing controller performance.


Implementation and Optimization Process: The optimization process involves iteratively adjusting the controller parameters to minimize the objective function. Through multiple iterations, the Gray Wolf Optimization algorithm converges to optimal values for the controller gains and time constants. The MATLAB simulation environment facilitates the execution of the optimization process, providing insights into the dynamic behavior of the load frequency control system.


Results and Performance Evaluation: Upon completion of the optimization process, the controller parameters are obtained, enabling the simulation of the load frequency control system. The simulation results demonstrate the effectiveness of the Gray Wolf Optimized controller in regulating frequency deviations and maintaining grid stability in the presence of variable renewable energy inputs. The obtained results align closely with those reported in the reference paper, validating the efficacy of the proposed controller design.


Conclusion: In conclusion, the integration of Gray Wolf Optimization with load frequency control systems offers a promising approach to enhance grid stability and reliability in renewable energy-integrated power systems. By leveraging the capabilities of renewable energy sources and advanced optimization algorithms, such as Gray Wolf Optimization, operators can achieve efficient and robust load frequency control, ensuring the seamless integration of renewable energy into existing power grids.

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