An expert system was developed to consider words' grammar case in Arabic phrases without diacritics. First, the system gets words' morphology and tags using Microsoft tool (ATK), then it depends on Arabic grammar to get words' grammar case in nominal
phrases. The system gave a very good results as they compared with Arabic language expert.
This paper introducesa new expert system (ES) for faulted section determination in
electrical power system andinterpretingthe performance of the protective system (relays
and circuit breakers). The introducedESrequiresinformation about the power sy
stem
configuration and about the contacts status (open/closed) of the circuit breakers and
protective relays. It can determine the faulted section quickly and accurately for all types
of faults including simultaneous faults. It is general, i.e.it can be usedwith any power
system,due to the separation between the Facts and Rules. The introducedES isdeveloped
and tested by CLIPS environment (C Language Integrated Production System) which uses
forward chaining to derive conclusion.
The performance of the introduced ES is tested for several power systems, IEEE–6 bustest
system, IEEE–9 bustest system andIEEE–14 bustest system, and it shows a distinct
performance for all tested systems. But for space limitation, we present in this paper the
performance results of the introduced ES for the IEEE–9 bustest system only.
Through our research we develop an Expert System called
Transformer Fault Detection and abbreviation Exformer, to help
engineers and technical's in detecting and diagnosis of oiled power
transformer faults before it going out of service. We also u
se Fuzzy
Logic in ambiguous data cases about gas ratios in transformer oil,
which require use of fuzzy rules in knowledge base of expert
system. We also discuss basis of using Artificial Neural Networks
and choose number of layers, number of neurons and suitable neural
network for power transformers faults analysis and compare.
