Power Quality Monitoring Strategy Based on an Optimized Multi-domain Feature Selection for the Detection and Classification of Disturbances in Wind Generators

Abstract

[EN] Wind generation is an essential power supply in the last time, as a renewable option. These 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 5-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 problem dimensionality, the features that provide redundant information are eliminated through an optimized feature selec-tion performed by means of a genetic algorithm and the principal component analysis. Addi-tionally, each one of the characteristic feature matrices of every considered condition is 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, tran-sients, voltage fluctuations and harmonic distortion. Thus, the proposed method is validated using a set of synthetic signals and then it is tested using two different sets of real signals from an IEEE workgroup and from a wind park located in Spain.