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This project is associated with a master's thesis titled "Machine Learning Applications for a Robust Classifier of Advanced Power Quality Disorders in Smart Grid" by Gabriel C. S. Almeida at the Federal University ItajubГЎ. Here are the MATLAB code in detail.
Inserting new devices, increased data flow, intermittent generation and massive computerization have significantly increased the complexity of the electrical systems. This increase led to necessary changes, eg. B. the need for intelligent electrical networks to adapt to this different reality. The production of artificial intelligence (AI) technology, which is represented by large data, machine learning (ML), deep learning (DL) and pattern recognition, represents a new era in society and global development based on information and knowledge. With the last smart grids (SG) is the use of techniques that use this type of intelligence is more necessary. This dissertation examines the use of the extended signal processing and L-algorithms to create a robust classifier of advanced power quality disorders in SG. For this purpose, known models of PQ disturbances with random elements were generated to tackle real applications. Thousands of signals with the performance of these disturbances were generated by these models. Signal processing techniques with discrete wavelet transform (DWT) were used to extract the main characteristics of the signal. This research aims to use ML algorithms to classify this data according to their respective functions. ML algorithms were trained, validated and tested. The accuracy and confusion matrix was analyzed, which refers to the logic behind the results. In the MATLAB software, the phases of data generation, feature extraction and characteristic selection were performed. The classification-learning Toolbox was used for training, validation and testing of the 27 different ML algorithms and evaluates the evaluation of any service. All phases of work were previously idealized, making their proper development and execution. The results show that the Cubic Council Classifier classifier has reached the maximum accuracy of all algorithms, indicating the efficacy of the proposed method of classification. Considerations on the results were interpreted as the explanation of the performance of any technology, relationships and their respective reasons.
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