Distribution network risk prediction based on data mining and improved PSO fused with SVM

In view of the challenges such as feature redundancy and insufficient small sample risk prediction accuracy caused by the proliferation of multi-source heterogeneous data in the distribution network in the ubiquitous power Internet of Things environment, this study proposes a distribution network risk prediction method based on data mining and improved swarm intelligence algorithm fusion support vector machine, namely the DM-IS model. First, this study uses kernel principal component analysis technology to map high-dimensional nonlinear data to low-dimensional space to eliminate redundant feature. Then, the nonlinear adaptive weights and chaotic mutation strategy of the improved particle swarm optimization algorithm based on logistic mapping are introduced. A hybrid model that can accurately optimize the penalty factors and kernel parameters of support vector machines globally is constructed. The results revealed that this model not only effectively overcome the premature convergence defect in the parameter optimization process, but also achieved high-precision fitting with a median error of only 0.022 in 92.0 s under extreme small-sample constraints, outperforming baseline models by significantly narrowing the error distribution bandwidth. In actual engineering scenarios, its average detection time for typical faults such as transformer overload was shortened by 77.90%, and monthly operation and maintenance costs were reduced by 42.65%. In addition to confirming the efficacy of multi-source heterogeneous data fusion in enhancing forecast robustness, this study offers quantitative algorithm reference and decision support for conversing power systems from passive repair to active defense by continuously quantifying risk probability indices and dynamic early-warning time margins to drive predictive parameter adjustments and early equipment replacements prior to critical systemic failures.