Resumo:
Power quality (PQ) is not a new theme, but it should not be neglected in any way, as
its performance parameters will reveal problems in the adequacy between the consumer
equipment and the electrical grid. With the ongoing transformations in electrical power
systems, characterized by the high penetration of renewable energy sources, the massive
insertion of components based on power electronics in the network, and the decentralization
of generation, these issues are becoming increasingly important. In Smart Grids,
solutions are sought for more advanced solutions to solve PQ disturbances problems. Advanced
signal processing plays an essential role in dealing with the network and supporting
various applications within this context and Artificial Intelligence (AI), which has gained
significant prominence to feed applications with innovative solutions in several areas. This
research investigates the use of advanced signal processing and Deep Learning techniques
for pattern recognition and classification of signals with PQ disorders. For this purpose,
the Continuous Wavelet Transform with a filter bank is used to generate 2-D images
with the time-frequency representation from signals with voltage disturbances. The work
aims to use Convolutional Neural Networks (CNN) to classify this data according to the
images’ distortion. In this implementation of AI, specific stages of design, training, validation,
and testing were carried out for a model elaborated by the case file and a knowledge
transfer technique with the pre-trained networks SqueezeNet, GoogleNet, and ResNet-50.
The work was developed in the MATLAB/Simulink software, all signal processing stages,
CNN design, simulation, and the investigated data generation. All steps have their objectives
fulfilled, culminating in the excellent execution and development of the research. The
results sought high precision for CNN de Scratch and ResNet-50 in classify the test set.
The other two models obtained not-so-high accuracy, and the results are consistent when
compared with different methodologies. Considerations about the results were pointed
out. Finally, some conclusions were established and a philosophical reflection on the role
of AI and advanced signal processing in electrical power systems.
