Notes

The power quality of the electric power has become an important issue for the electric
utilities and their customers. In order to improve the quality of power, electric utilities
continuously monitor power delivered at customer sites. Thus automatic classification of
distribution line disturbances is highly desirable. The detection and classification of the
power quality (PQ) disturbances in power systems are important tasks in monitoring and
protection of power system network. Most of the disturbances are non-stationary and
transitory in nature hence it requires advanced tools and techniques for the analysis of PQ
disturbances. In this work a hybrid technique is used for characterizing PQ disturbances using
wavelet transform and fuzzy logic. A no of PQ events are generated and decomposed using
wavelet decomposition algorithm of wavelet transform for accurate detection of disturbances.
It is also observed that when the PQ disturbances are contaminated with noise the detection
becomes difficult and the feature vectors to be extracted will contain a high percentage of
noise which may degrade the classification accuracy. Hence a Wavelet based de-noising
technique is proposed in this work before feature extraction process. Two very distinct
features common to all PQ disturbances like Energy and Total Harmonic Distortion (THD)
are extracted using discrete wavelet transform and is fed as inputs to the fuzzy expert system
for accurate detection and classification of various PQ disturbances. The fuzzy expert system
not only classifies the PQ disturbances but also indicates whether the disturbance is pure or
contains harmonics. A neural network based Power Quality Disturbance (PQD) detection
system is also modeled implementing Multilayer Feedforward Neural Network (MFNN).