Load Frequency Control of Modern Interconnected Power System Using SCSO-SNN Approach
Keywords:
Photovoltaic system, Wind Turbine, Hybrid, Load frequency, Power plant, Converter, PID controller and solar fieldAbstract
The operation and design of modern interconnected power systems rely heavily on load frequency regulation. The integration of wind power, photovoltaic energy, and other new energy sources into the power grid has presented significant challenges to the power system. The objective of this work is maximizing the energy output, efficiency and reduced the error of the wind and solar energy system. The less efficient and older power plants are not operated continuously and are only used during peak hours. As a result, an integrated system can help power plants run more efficiently. Also, in wind power fluctuation adds negatively to power imbalance and frequency deviation. To overcome this Problem, we proposed hybrid method is the combined with Sand Cat Swarm Optimization (SCSO), and Spiking Neural Network (SNN). The SCSO generate the control signal of the converter and the SNN method predicts the control signal from the SCSO method. By then, the SCSO-SNN technique is performed in MATLAB platform and the implementation is calculated with the present procedures. The proposed method shows better results in all approaches like, Particle Swarm Optimization (PSO), Ant Lion Optimization (ALO) and Sand Cat Swarm Optimization (SCSO). The proposed method SCSO-SNN compared with existing methods error of battery, Photovoltaic, wind with battery error value is 2.7%.
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