원문정보
초록
영어
In order to improve the fault positioning accuracy of distributed generation power distribution network, it puts forward fault positioning method of distribution network equipment based on blended data association rule data mining method. Based on fuzzy rough set theory, it studies classification rule mining method on hybrid data, through the introduction of the rule to obtain the a generalized threshold of the algorithm, and control the scale and complexity of the obtained rule set, so as to improve the classification efficiency of rough set method of knowledge discovery on the failure data and get the fault positioning feature of distribution network, finally adopts the support vector machine (SVM) to make fault classification, and tests the performance of the algorithm with simulation experiment. The simulation results show that, this paper can be quickly and accurately to locate fault power section, and the fault positioning accuracy is higher than other fault positioning methods of distribution network.
목차
1. Introduction
2. LERS Rule Mining System Based on Rough Set
2.1. Rough Set Rule Mining Model
2.2. Measurement of Classification Rule
3. Rule Mining Algorithm of Blended Data of Fault Diagnosis on PowerNetwork Equipment
3.1. Rule Mining Model Based on Fuzzy Rough Set
3.2. Rule Mining Algorithm of Fuzzy Rough Set on Blended Data
4. Simulation Experiment
4.1. Simulation Environment
4.2. Result Analysis
4.3. Performance Comparison with Other Fault Positioning Method of PowerDistribution Network
5. Conclusion
References
