Authors : Edmond Randriamora; Olivier Mickaël Ranarison; Rivo Mahandrisoa Randriamaroson

Volume/Issue : Volume 11 - 2026, Issue 6 - June

Google Scholar : https://tinyurl.com/33y45rfd

Scribd : https://tinyurl.com/n6ap9txd

DOI : https://doi.org/10.38124/ijisrt/26jun465

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Efficient power grid operation requires minimizing technical losses to ensure economic viability and system stability. This paper presents an Improved Driving Training Based Optimization (IDTBO) algorithm to minimize active and reactive power losses in electrical transmission systems. The problem is formulated as a highly non-linear, multi-variable, and heavily constrained Optimal Reactive Power Dispatch (ORPD) problem. The proposed method enhances the standard Driving Training Based Optimization (DTBO) algorithm by integrating advanced mathematical mechanisms, such as Levy Flight distribution and Crowding Distance techniques. These improvements prevent premature convergence and balance global exploration with local exploitation. The algorithm fine-tunes critical network control variables, including generator voltage magnitudes, transformer tap settings, and switchable reactive power compensators, while strictly respecting system operating and security limits. The effectiveness and robustness of the IDTBO approach are validated on standard IEEE 30-bus system. Simulation results demonstrate that the proposed method achieves a higher percentage of power loss reduction and superior convergence speed compared to the Modified Driving Training Based Optimization (MDTBO) algorithm. Consequently, the IDTBO emerges as a highly competitive and efficient tool for modern power system optimization.

Keywords : Active power loss, Reactive power loss, Optimal Reactive Power Dispatch, Improved Driving Training Based Optimization, Levy Flight, Crowding Distance.

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