Power quality monitoring strategy based on an optimized multi-domain feature selection for the detection and classification of disturbances in wind generators

Abstract

Wind generation has recently become an essential renewable power supply option. Wind generators are integrated with electrical machines that require correct functionality. However, the increasing use of non-linear loads introduces undesired disturbances that may compromise the integrity of the electrical machines inside the wind generator. Therefore, this work proposes a five-step methodology for power quality disturbance detection in grids with injection of wind farm energy. First, a database with synthetic signals is generated, to be used in the training process. Then, a multi-domain feature estimation is carried out. To reduce the problematic dimensionality, the features that provide redundant information are eliminated through an optimized feature selection performed by means of a genetic algorithm and the principal component analysis. Additionally, each one of the characteristic feature matrices of every considered condition are modeled through a specific self-organizing map neuron grid so they can be shown in a 2-D representation. Since the SOM model provides a pattern of the behavior of every disturbance, they are used as inputs of the classifier, based in a softmax layer neural network that performs the power quality disturbance detection of six different conditions: healthy or normal, sag or swell voltages, transients, voltage fluctuations and harmonic distortion. Thus, the proposed method is validated using a set of synthetic signals and is then tested using two different sets of real signals from an IEEE workgroup and from a wind park located in Spain.