Search

This article presents the design of an improved smart grid model with an improved optimal VAr strategy using a bio-inspired approach. The integration of an artificial neural network technique with bio-inspired algorithms is the novelty of this research work. The objective of the work is attained by integrating the artificial neural network with three different evolving techniques like the Big Bang-Big Crunch algorithm, the gravitational search algorithm, and bacterial foraging algorithm. The three different layers of artificial neural network reconcile with the power grid for enhancing the voltage profile generated by bio-inspired techniques. The effectiveness and reliability of the algorithms are examined on the standard IEEE 30 bus system. The sample test bus system comprises six Generators, four Transformer tap changers, and 2 Static Shunt VAr Compensator. Artificial intelligence technology is applied for enhancing the voltage profile by managing reactive power across the power system network. The results produced by artificial intelligence technique are more competent and outstanding than any other conservative optimization techniques. It is found that the real power loss across the network has been reduced from 5.744 MW to 4.234 MW, while the bus voltage deviation has been minimized from 1.475 p.u. to 0.0918 p.u. and the voltage profile has been improved from 0.968 to 1.0985 p.u. after integration of the artificial neural network. The optimal values of objective functions are attained by evaluating the training and testing performance of artificial neural network criteria such as coefficient of determination, mean absolute error, root mean square error, and mean absolute percentage error. The obtained results show the remarkable improvement in voltage profile after integrating artificial intelligence technology with a power grid system. [ABSTRACT FROM AUTHOR]

This article presents the design of an advanced smart grid model with an improved optimal variation strategy by implementing renewable energy sources. The novelty involved in this article is designing a renewable energy-based smart grid system incorporating with Artificial Neural Network for maintaining an improved voltage profile with balanced reactive power level across the grid network. The objective is attained by implementing renewable energy sources, namely solar and wind energy modules, with artificial intelligence techniques for effective reactive power control using DSTATCOM. The power requirement across the power grid network is satisfied by providing solar and wind energy sources managing the smart-grid system's var level. The optimized power is trained on the Feed Forward Neural Network-based for calculating the amount of voltage profile and power loss based on system load. The DSTATCOM is an optimal var compensating device for maintaining the reactive power within the permissible limit across the smart grid system. The proposed system improves the voltage profile, reduces the power loss, and satisfies the grid by managing the system's reactive power balance. The proposed approach is executed in MATLAB/Simulink work site, and the performance is analyzed with three different cases. The smart grid system powered with hybrid energy sources strives to attain the objective functions effectively among the three different cases. Integration of hybrid renewable energy source with Artificial Neural Network achieves an improved voltage profile to about 98.45%, which states that the profile rating has improved from 1.056 to 1.083 p.u minimizations of real power loss has done from to 3.23 MW from 3.80 MW. [ABSTRACT FROM AUTHOR]