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allows propagation of uncertainties along extended chains of reasoning, and eases
implementation of large knowledge bases.
Several techniques for representing uncertainty in expert systems have been
proposed in the AI literature including Bayesian analysis and certainty measures [4]. For the
most part, these techniques are ad-hoc methods that emphasize simplifying coding of the
uncertainty. This work begins from a fundamental model of uncertainty based on fuzzy
mathematics and leads to a rule-based representation for expert system development.
Techniques are developed which show the most effective method for extracting information
from an observation and suggest actions to take which will lead to the most coherent
conclusion. Fuzzy mathematics applications within power systems have been proposed in
several areas, see [5]. In particular, there have been several applications to transformer
diagnosis of fuzzy set methods. In [6,7], fuzzy logic is used to implement dissolved gas
analysis methods. An acoustic technique for finding partial discharges applied fuzzy logic to
representation of uncertainties [8]. The techniques developed in this chapter have also been
applied to transformer diagnostics and condition monitoring [9-11] and thus, examples in
this chapter will focus on this problem.
This chapter is organized as follows. The diagnostic framework is discussed and
requirements for a model of uncertainty are presented.
An introduction to fuzzy
mathematics with emphasis on the lesser known fuzzy information aspects is then given.
Several detailed examples show the usefulness of the proposed technique. Implementation
and representation issues are discussed. Learning methods and performance improvement of
a diagnostic expert system are explored. A method for performance evaluation and
improvement is proposed within the developed fuzzy set framework. Some directions for
further research are discussed.