A large variety of fault cases have been studied using different parameters like fault inception angle/time, location, resistance, ground resistance, and fault types (LG, LLG, LL and LLL).
From the above case studies, it can be observed that the proposed method succeeded to detect and classify the internal faults in the stator windings whatever the changes in the prescribed conditions i.e. the fault location, resistance, inception angle or the pre-fault loading conditions.
Twenty-seven common bean accessions were assessed over three seasons and locations for resistance to BBS.
In other words, it should be differentially expressed either between resistant and susceptible fish or differentially expressed after infection, and located at a genomic location the resistance phenotype is mapped.
This interaction is explained by the co-location of resistance genes for antimicrobial drugs and for other chemicals located on mobile genetic elements (as noted above) or by common resistance mechanisms (such as efflux pumps) with a broader specificity (cross-resistance).
The precise identity of the switching location where resistance change mainly occurs has not been revealed.
The fault location and resistance are considered as two important parameters in this method.
The fault conditions include fault type, location, and resistance and inception angle.
However, the proposed method is minimally influenced by the fault location and resistance.
Critical issues like a variation in fault location, fault resistance, fault inception angle and different source capacities are validated using the proposed scheme.
The test data obtained from simulation covers all possible states of the SG; namely normal state, internal fault state and external fault state at various conditions such as: fault type, fault location, fault resistance and fault inception angle.
