Enhanced Fault Localization in Energy Systems Using an Improved MVO Algorithm and Multistrategy Optimization

With the advancement of energy systems toward intelligence and renewability, fault localization in active distribution networks (ADNs) has become a critical issue due to its direct impact on power supply reliability and system stability. Existing fault localization methods often suffer from low accuracy and slow convergence, particularly in complex network structures and under variable fault scenarios. This paper proposes an Improved Multiverse Optimization (IMVO) algorithm to address these challenges. The IMVO algorithm enhances the standard Multiverse Optimization (MVO) by introducing a three-phase search strategy—exploration, development, and hybrid—and dynamically adjusting key parameters such as the Travel Distance Ratio (TDR) and Wormhole Existence Probability (WEP) to balance global and local search capabilities. Additionally, elite retention and differential evolution strategies are integrated to improve population diversity and prevent premature convergence. Validation on the IEEE 33-node ADN model with distributed energy sources demonstrates that the IMVO algorithm achieves fault localization accuracies of 97.2% for single-point faults and 97.6% for multipoint faults, with average convergence within 6 and 8 generations, respectively. Further experiments on the IEEE 69-node network validate the algorithm’s scalability, achieving 96.8% and 97.0% accuracy for single-point and multipoint faults, respectively, with only a moderate increase in computational time. The results indicate that the IMVO algorithm maintains near-linear computational complexity as network size grows, making it a viable solution for real-time fault diagnosis in large-scale ADNs. These findings highlight the superior accuracy, convergence speed, and robustness of the proposed IMVO algorithm, demonstrating its potential as an efficient and reliable fault localization method for complex ADN environments and future smart grid applications.