Power Distribution Diagnosis with Uncertainty Information Based on Improved Rough 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. 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. To deal with the uncertainty and deferent structures of the system, rough sets and fuzzy sets are introduced. The reduction algorithm based on uncertainty rough sets is improved. The rule reliability is deduced using fuzzy sets and probability. The worked example and simulation result of a power distribution system shows the effectiveness and usefulness of the approach