Interval state estimation method for distribution networks considering multi-attribute decision making and multi-source measurement data uncertainty

The operation of distribution networks faces significant challenges due to uncertainties and measurement errors. This paper presents an innovative interval state estimation method that leverages multisource measurement data and multi-attribute decision-making to enhance accuracy and reliability. We first extract deterministic uncertainty measures from heterogeneous measurement data. By analyzing the interplay between determinacy and uncertainty, we construct an interval representation space that enables precise extraction, integration, and analysis of multisource data. Subsequently, we develop an interval multi-attribute decision-making model that reflects the dynamic preferences of the distribution network, resulting in a robust interval state estimation model that incorporates uncertainty. An improved Krawczyk-Moore (KM) algorithm facilitates iterative solutions, while Monte Carlo simulations validate the model's effectiveness in handling multisource data fusion and uncertainty analysis. Simulation results demonstrate a 0.02 improvement in measurement error statistics, with estimation error statistics remaining stable and the estimated interval coverage probability decreasing by approximately 0.4. Overall, this method outperforms traditional approaches, particularly in scenarios characterized by data uncertainty and multisource data integration, thereby enhancing estimation accuracy.