a Data mining algorithm to mine the historical big data of the automation operation system of the distribution system based on the Support-Confidence-Lift framework and its application
Affected by the faults, defects of communication equipments and secondary circuits, and artificial records made by operating staff etc., the quality of the mass and complicated datum stored in the database of automation operation system of distribution grids becomes poorer and more worse: incorrect, ununiform, unreliability and so on. To figure out this problem, a data-mining algorithm based on the Support-Confidence-Lift framework is designed to mine frequent itemsets, strong association and correlation rules that conform to the logic of the automation operation system from the massive poor and complicated datum of the automation operation system of distribution networks. In addition, a case on a practical application of the algorithm is studied carefully to illustrate that the designed algorithm can not only offer an exact, reliable data mining method, a theoretical foundation for setting up an objective and reasonable utility indices system, but also provide intelligent judging tools to routine operation, maintenance, defect elimination and management for the management departments of distribution networks, with saving a lot of labor, physical resources and time cost. Therefore, the algorithm is valuable to the engineering practices of distribution grids.