Data-Driven Multidimensional Analysis of Data Reliability's Impact on Power Supply Reliability

The increasing integration of renewable energy and electronic power devices into power systems is expanding the data volume and seriously challenging data interaction. The improvement of data reliability contributes to ensuring power supply reliability through the accurate assessment of the power grid's status and the correct response of equipment. To quantify the impact of data reliability on power supply reliability, a data-driven analysis method is proposed. First, based on low-voltage telemetry data, a set of evaluation indicators for data reliability and high-quality power supply reliability is proposed. Then, a reliability scoring method considering combined weight is developed based on the improved technique for order preference by similarity to an ideal solution and grey relational analysis. Additionally, the analysis method based on convolutional neural networks and support vector machines nonlinear fitting is proposed to indicate the impact of data on power supply reliability and find a reasonable range of data reliability indicators. Finally, the effectiveness of the proposed method is verified by the experiments based on actual distribution network data from the eastern province of China.