Resumo:
The insertion of new devices, increased data flow, intermittent generation and massive
computerization have considerably increased current electrical systems’ complexity. This
increase resulted in necessary changes, such as the need for more intelligent electrical net works to adapt to this different reality. Artificial Intelligence (AI) plays an important role
in society, especially the techniques based on the learning process, and it is extended to the
power systems. In the context of Smart Grids (SG), where the information and innovative
solutions in monitoring is a primary concern, those techniques based on AI can present
several applications. This dissertation investigates the use of advanced signal processing
and ML algorithms to create a Robust Classifier of Advanced Power Quality (PQ) Dis turbances in SG. For this purpose, known models of PQ disturbances were generated with
random elements to approach real applications. From these models, thousands of signals
were generated with the performance of these disturbances. Signal processing techniques
using Discrete Wavelet Transform (DWT) were used to extract the signal’s main charac teristics. This research aims to use ML algorithms to classify these data according to their
respective features. ML algorithms were trained, validated, and tested. Also, the accuracy
and confusion matrix were analyzed, relating the logic behind the results. The stages of
data generation, feature extraction and optimization techniques were performed in the
MATLAB software. The Classification Learner toolbox was used for training, validation
and testing the 27 different ML algorithms and assess each performance. All stages of
the work were previously idealized, enabling their correct development and execution.
The results show that the Cubic Support Vector Machine (SVM) classifier achieved the
maximum accuracy of all algorithms, indicating the effectiveness of the proposed method
for classification. Considerations about the results were interpreted as explaining the per formance of each technique, its relations and their respective justifications.
