Detection And Classification Of Power Quality Disturbances Using Wavelet Transforms And Probablistic Neural Networks
The use of sensitive electronic equipments is on the rise lately and power quality studies have progressed a lot.
Detection and classification of power quality signals is of greater importance both in case of Power quality studies and
denoising. This paper proposes a detection and classification technique for several power quality disturbances, by
introspecting the energy of the distorted signals at different resolutions using the Multiresolution Analysis technique (MRA)
of Discrete wavelet transform (DWT) .i.e. the Energy Difference MRA (EDMRA) technique is used .This is employed on
the distorted signals to extract the energy distribution features at different levels of resolution. Db4 mother wavelet is used to
decompose the signal. The power quality disturbances are identified based on the energy difference of disturbance signal
with pure sinusoidal signal of 50Hz at each decomposition level. This forms a feature vector that is fed to the input nodes of
probabilistic neural network which classifies the power quality disturbances. To validate the efficiency and preciseness of
the proposed method the simulation results are analyzed.