Quantifying electric power quality via fuzzy modelling and analytic hierarchy processing
S.A. Farghal, Mahmoud Kandil, Akram Ibrahim Elmitwally
Abstract
S.A. Farghal, Mahmoud Kandil, Akram Ibrahim Elmitwally
Abstract
There is no one universal, even approximate, piece of data that entirely characterises, qualitatively or quantitatively, the status of the power quality at a utilisation point. This is due, on the one hand, to the great dimensionality of the parameters involved in the problem of power quality evaluation. On the other hand, the available data is featured with imprecision, uncertainty and vagueness, which renders it a very tedious and problematical task to assess the power quality level through one index. It is attempted to formulate one such comprehensive measure for the level of power quality at a loading point. Knowledge acquisition sessions, analytic hierarchy processing and fuzzy reasoning are the assistant tools employed to propose this new measure.
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There is no one universal, even approximate, piece of data that entirely characterises, qualitatively or quantitatively, the status of the power quality at a utilisation point. This is due, on the one hand, to the great dimensionality of the parameters involved in the problem of power quality evaluation. On the other hand, the available data is featured with imprecision, uncertainty and vagueness, which renders it a very tedious and problematical task to assess the power quality level through one index. It is attempted to formulate one such comprehensive measure for the level of power quality at a loading point. Knowledge acquisition sessions, analytic hierarchy processing and fuzzy reasoning are the assistant tools employed to propose this new measure.
Key concepts: Vagueness, Measure (data warehouse), Fuzzy logic, Quality (philosophy), Computer science, Point (geometry), Hierarchy, Task (project management)