Multiobjective reconfiguration of unbalanced distribution networks using improved transient search optimization...

Area of Science:

• Electrical Engineering
• Optimization Algorithms
• Power Systems

Background:

• Unbalanced distribution networks present challenges in optimal configuration.
• Conventional optimization algorithms may struggle with convergence and balancing local/global search.

Purpose of the Study:

• To introduce an improved transient search optimization algorithm (ITSOA) for optimal unbalanced distribution network configuration.
• To enhance power quality and network reliability through multiobjective optimization.

Main Methods:

• The transient search optimization algorithm (TSOA) was enhanced using opposition learning and nonlinear decreasing strategies.
• A multiobjective function was formulated to address power loss, voltage sag/unbalance, and energy not supplied.
• ITSOA was applied to determine optimal network configurations, satisfying operational and radiality constraints.

Main Results:

• The proposed ITSOA effectively identified optimal network configurations for various loading conditions.
• Significant improvements were observed in power loss reduction, voltage profile enhancement, and minimized energy not supplied.
• ITSOA demonstrated superior performance compared to TSOA, PSO, GWO, BA, MRFO, and ALO.

Conclusions:

• The ITSOA provides an effective approach for optimizing unbalanced distribution networks.
• The methodology enhances power quality and reliability without compromising network objectives.
• ITSOA offers a robust solution for complex distribution network reconfiguration problems.