In response to the significant load characteristic variations within intelligent distribution network stations and challenges in balancing clustering effectiveness

accuracy

and robustness of traditional clustering algorithms

this paper presents a clustering method for flexible loads in intelligent distribution networks based on a voting strategy.Combining the K-means algorithm with the fuzzy C-means clustering algorithm based on grey wolf optimization (GWO-FCM)

the clustering stability and effectiveness are considered.To reduce the dimensionality of high-dimensional data related to flexible loads in intelligent distribution networks

an integrated tree-fitting approach is employed for dimensionality reduction of high-dimensional data pertaining to flexible loads.The Mahalanobis distance is used to overcome the correlation among clustering indices and determine the optimal cluster number.The benchmark clustering algorithm is determined based on the Calinski-Harabaz (CH) index.A consistency function matrix is used to unify the clustering results

mitigating instability in clustering outcomes and yielding a more consistent and stable overall clustering result.Finally

the validity of the proposed clustering algorithm based on the voting strategy is verified through actual operation data from intelligent distribution stations in the Ningxia Hui Autonomous Region.