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Enrichment of voltage stability in power system through novel generalized approximate reasoning based intelligent control with african buffalo optimization approach

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Abstract

In recent times, the modern power system has become more complex because of high penetration of generation power, heavy load changes, voltage fluctuation, high reactive power, and environmental and economic problems. These problems can cause voltage disintegration issues in the power system. Thus, the voltage stability in the system should be predicted and required to improve the stable conditions. However, the earlier investigations do not significantly improve the coordination's voltage stability. For this reason, in this research, a novel generalized approximate reasoning based intelligent control based unified voltage collapse proximity indicator is proposed for calculating the system's weak buses voltage. Moreover, the African buffalo optimization is proposed to estimate the finest location of unified power flow controller based flexible alternative current transmission system devices in the scheme for voltage strength enrichment. The implementation of these proposed approaches is done via MATLAB/Simulink. The projected system has been experienced on IEEE 118 and IEEE 30 bus systems. Consequently, the simulation outcome indicates that the proposed methods effectively predict and upgrade the voltage stability index. The results are compared with the conventional models concerning cost, real power loss, and computational time. The proposed system attained real power for IEEE 118 bus is 4.5502 MW and IEEE 30 bus for 4.902 MW, which is very low compared with the existing methods.

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Correspondence to Gitanjali Saha.

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Saha, G., Chakraborty, K. & Das, P. Enrichment of voltage stability in power system through novel generalized approximate reasoning based intelligent control with african buffalo optimization approach. Soft Comput 27, 7473–7496 (2023). https://doi.org/10.1007/s00500-022-07688-3

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  • DOI: https://doi.org/10.1007/s00500-022-07688-3

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