PhDThe method of using Fuzzy Sets Theory(FST) and Fuzzy Reasoning(FR) to aid
reliability evaluation in a complex and uncertain environment is studied, with special
reference to electrical power generating system reliability evaluation.
Device(component) reliability prediction contributes significantly to a system's
reliability through their ability to identify source and causes of unreliability. The main
factors which affect reliability are identified in Reliability Prediction Process(RPP).
However, the relation between reliability and each affecting factor is not a necessary and
sufficient one. It is difficult to express this kind of relation precisely in terms of quantitative
mathematics. It is acknowledged that human experts possesses some special characteristics
that enable them to learn and reason in a vague and fuzzy environment based on their
experience. Therefore, reliability prediction can be classified as a human engineer oriented
decision process. A fuzzy knowledge based reliability prediction framework, in which
speciality rather than generality is emphasised, is proposed in the first part of the thesis.
For this purpose, various factors affected device reliability are investigated and the
knowledge trees for predicting three reliability indices, i.e. failure rate, maintenance time
and human error rate are presented. Human experts' empirical and heuristic knowledge are
represented by fuzzy linguistic rules and fuzzy compositional rule of inference is employed
as inference tool.
Two approaches to system reliability evaluation are presented in the second part of
this thesis. In first approach, fuzzy arithmetic are conducted as the foundation for system
reliability evaluation under the fuzzy envimnment The objective is to extend the underlying
fuzzy concept into strict mathematics framework in order to arrive at decision on system
adequacy based on imprecise and qualitative information. To achieve this, various
reliability indices are modelled as Trapezoidal Fuzzy Numbers(TFN) and are proceeded by
extended fuzzy arithmetic operators. In second approach, the knowledge of system
reliability evaluation are modelled in the form of fuzzy combination production rules and
device combination sequence control algorithm. System reliability are evaluated by using
fuzzy inference system. Comparison of two approaches are carried out through case
studies. As an application, power generating system reliability adequacy is studied. Under
the assumption that both unit reliability data and load data are subjectively estimated, these
fuzzy data are modelled as triangular fuzzy numbers, fuzzy capacity outage model and
fuzzy load model are developed by using fuzzy arithmetic operations. Power generating
system adequacy is evaluated by convoluting fuzzy capacity outage model with fuzzy load
model. A fuzzy risk index named "Possibility Of Load Loss" (POLL) is defined based on
the concept of fuzzy containment The proposed new index is tested on IEEE Reliability
Test System (RTS) and satisfactory results are obtained
Finally, the implementation issues of Fuzzy Rule Based Expert System Shell
(FRBESS) are reported. The application of ERBESS to device reliability prediction and
system reliability evaluation is discussed
