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ANN Based Power Quality Identification and Classification in Wind Solar Hybrid System

prasanna prem, Dr. P. Rathika

Abstract


With the development of new functionalities solar and wind energy based hybrid system are upcoming energy source with higher efficiency. Solar and wind energy being naturally available in abundance and non-polluting is one of the most promising sources. Due to the development of modern power electronics devices the power quality of wind solar hybrid system gets affected. Hence due to the increasing usage of sensitive electronic equipments in wind solar hybrid system Power Quality has become a major concern. Sag, swell and harmonics are the critical aspect of Power Quality issues. This project presents an approach that is able to provide the detection and identification of power quality problems. This method is developed by using discrete wavelet transform (DWT) analysis. The given signal is decomposed through wavelet transform. Later, using the wavelet coefficients, Feature Extraction is done and an Artificial Neural Network is developed to classify the power quality disturbances. The training and testing data required to develop the ANN model is generated through simulation. Here, it is demonstrated that each power quality disturbance has distinctive deviations from the pure sinusoidal waveform and this is adopted to provide a reliable classification of the type of disturbance. The combined mathematical transformation and artificial neural network-based approach is able to classify the power quality disturbances accurately.

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References


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DOI: https://doi.org/10.37628/ijepst.v3i2.650

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