An improved corona-virus herd immunity optimizer algorithm for network reconfiguration based on fuzzy multi-criteria...

Area of Science:

• Electrical Engineering
• Optimization Algorithms
• Power Systems

Background:

• Distribution network reconfiguration is a cost-effective strategy for enhancing network performance.
• Power quality and reliability are critical metrics in modern power systems.
• Existing optimization algorithms face challenges in multi-objective distribution network problems.

Purpose of the Study:

• To present an optimal reconfiguration strategy for unbalanced distribution networks.
• To minimize power loss, voltage unbalance, voltage sag, and energy not supplied (ENS).
• To introduce and evaluate a novel improved corona-virus herd immunity optimizer algorithm (ICHIOA).

Main Methods:

• Utilizing a fuzzy multi-criteria approach (FMCA) for optimization.
• Implementing the improved corona-virus herd immunity optimizer algorithm (ICHIOA).
• Testing the methodology on 33 and 69 bus IEEE standard networks under various loading conditions.

Main Results:

• The ICHIOA demonstrated superior performance compared to PSO, GWO, MFO, ALO, BA, and conventional CHIOA.
• Achieved significant improvements in power loss, voltage unbalance, voltage sag, and ENS.
• FMCA-based multi-objective optimization effectively balanced competing criteria, unlike single-objective approaches.

Conclusions:

• The ICHIOA-based FMCA is a highly effective method for optimal distribution network reconfiguration.
• The proposed approach enhances power quality and system reliability.
• ICHIOA offers improved convergence accuracy and tolerance for complex optimization problems.