I think it is very useful for mundane tasks like fault diagnosis and operational optimisation.
It is a small, quiet role that resonates outwards like fault lines opening in the earth, irrevocably altering the landscape.
However, most solutions are based on centralized systems and barely fulfil criterion like fault tolerance or adaptability.
Effect of variation in different fault parameter like fault types, locations, inception angles, resistances were also exemplified here.
There are many ISs developed to control and monitor the drifting behaviour of production lines like fault detection and classification (FDC), statistical process control (SPC) and automation systems.
We have also measured different parameters like fault recovery time, power consumption, resource overhead and error correction efficiency to estimate the performance of our proposed methods.
Because HD Tree supports multiple routes between any two nodes in the P2P system, routing in HD Tree is very flexible; it can be designed for many purposes, like fault tolerance, or dynamic load balancing.
The software tool developed identifies different types of faults like: fault in a photovoltaic module, fault in a photovoltaic string, fault in an inverter, and a general fault that may include partial shading, PV ageing, or MPPT error.
These models, however, can be non realistic in many practical contexts because of the presence of measurement errors that play an important role in applications like fault diagnosis and optimal filtering.
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).
Also the other factors like fault on voltage point of wave, CT saturation, high SIR value, fault location and fault inception angle are not a cause of error for the method as cleared from the results.
