An Entropy–Envelope Approach for the Detection and Quantification of Power Quality Disturbances

The importance of power quality has increased these days due to the growth in the use of renewable energies and nonlinear loads. Although the use of renewable energies provides power generation sources that help in reducing greenhouse gas emissions, they might have a detrimental effect on the power quality due to their intermittency and dependence on weather conditions. Due to the importance of keeping an optimal power quality, in this work, a novel methodology is developed whose main contribution relies on the use of entropy features and envelope analysis for the detection and quantification of power quality disturbances. The proposed method is implemented within a machine learning framework, where linear discriminant analysis (LDA) is employed to optimize entropy-based features. Subsequently, a neural network classifier performs an automatic classification and quantifies the magnitude of affectation associated with grid disturbances. The training is performed using synthetic signals, and validation is conducted with real signals from a photovoltaic park and from an IEEE working group. The results obtained are compared with those provided by other methodologies proving the accuracy and the viability of the proposed approach.