Artificial Intelligence Techniques




Neural Network

It exhibits high level of accuracy for diverse and mixed power quality disturbance classification.

In noisy conditions its efficiency is very limited or in other words it’s less.


Artificial Neural Network

It offers mathematical suppleness with high accuracy in real time applications.

Its accuracy and convergence depends on its network architecture and also dependent on noises in the signal.


Support Vector Machine

It has a high learning process with a prospective to grip hefty features; it also provides a stable solution in quadratic optimization.

Its total dependent on training samples if they are minimum, the classification accuracy is poor and vice versa.


Fuzzy Logic

It has a fascinated accuracy in analyzing complex systems and it’s accurate in modeling.

It cannot accommodate new disturbances as its training set is limited for every case.


Genetic Algorithm

It classifies accurately power quality disturbances which are generated due to damped sub harmonic signals and dynamic performance of the power system.

It has a very high computational time.


Expert System

It has the facility that it can be used with/without limited data.

In this, the actual situation and assumptions do not match exactly and the extract of conclusion is very difficult furthermore its execution is very slow, moreover the system is very expensive.