Power Distribution Fault Diagnosis Based on Rough-Cloud Sets

The volume of data with a few uncertainties overwhelms classic information systems in the distribution control center and exacerbates the existing knowledge acquisition process of expert systems. To deal with the uncertainty and deferent structures of the system, rough sets and cloud theorem are introduced and the rough-cloud sets are proposed. The reduction algorithm based on them is improved. By which, the current and voltage information is employed to fault diagnosis. The paper describes a systematic approach for detecting superfluous data. It is considered as a "white box" rather than a "black box" like in the case of neural network. The approach therefore could offer user both the opportunity to learn about the data and to validate the extracted knowledge. The simulation result of a power distribution system shows the effectiveness and usefulness of the approach.