# Dynamic Reconfiguration of Active Distribution Network Based on Improved Equilibrium Optimizer
## 6 Conclusions  
This article is based on an improved balance optimizer algorithm to study the dynamic reconfiguration problem of active distribution networks. The main tasks completed are as follows:  
1. In the context of a large number of wind and photovoltaic power generators connected to the distribution network, a multi-objective reconstruction mathematical model combining constraints such as power balance, network topology, node voltage, and branch current was constructed, providing a theoretical basis for solving optimization algorithms. 2. An improved fuzzy C-means clustering algorithm was proposed for load period partitioning. In response to the problems of insufficient utilization of time information and unstable partitioning performance in traditional FCM for processing time-series data, this paper introduces a comprehensive similarity index between load characteristics and optimal network structure to improve the accuracy of similarity evaluation between load curves. At the same time, a time-weighted similarity matrix was constructed to achieve temporal feature fusion, making the clustering process more focused on the temporal evolution law of the load. The proposed method not only improves the rationality of time period division but also provides a theoretical basis for subsequent optimization. 3. Propose a feasible solution judgment model based on adaptive ordered loops, which improves the solution space quality of the reconstruction algorithm. In response to the interference problem of a large number of infeasible and inferior solutions in the optimization process, this paper establishes a set of judgment criteria based on the dual constraints of power verification and topology structure. By calculating power and branch verification to generate an adaptive ordered loop feasible solution matrix, combined with the directionality of branch power flow and the electrical connection relationship of nodes, infeasible solutions that do not meet topological constraints can be quickly eliminated. This mechanism effectively compresses the solution space, improves the effective sample ratio of the algorithm in the search process, and fundamentally enhances the running efficiency of the algorithm and the physical feasibility of the solution. 4. Propose an improved balance optimizer algorithm for active distribution network reconstruction, which enhances the optimization performance. Based on the EO algorithm, this article introduces Tent chaotic mapping to generate an initial population, improving the uniformity and diversity of the population in the search space, and integrating elite non-dominated sorting strategies during the iteration process to achieve simultaneous balancing and evolution of multiple objectives. By integrating random differential mutation and binomial crossover operation, the algorithm's ability to escape from local optima is improved. Finally, by combining the above improved algorithm with the adaptive ordered loop feasible solution judgment mechanism, an efficient and stable dynamic reconstruction optimization framework for active distribution networks is constructed. 5. The effectiveness, feasibility, and superiority of each method were verified through standard examples. This article selects IEEE 33-node and IEEE 69-node systems as examples, and analyzes them from multiple dimensions, such as time division effect, reconstruction performance, and comparative algorithm performance through multiple sets of experiments. In the IEEE 33-bus system, the proposed method reduced network losses from 1320.4 kWh to 729.3 kWh, achieving a 44.8% reduction, while voltage deviation decreased from 25.1 p.u. to 16.1 p.u., showing a 35.9% improvement. Similarly, in the IEEE 69-bus system, power losses were reduced from 1412.4 kWh to 846.7 kWh, achieving a 40.1% reduction, and voltage deviation was improved from 44.2 p.u. to 26.3 p.u., representing a 40.5% improvement.