Abstract
The economic impact associated with power quality (PQ) problems in electrical systems is increasing, so PQ improvement research becomes a key task. In this paper, a Stockwell transform (ST)-based hybrid machine learning approach was used for the recognition and classification of power quality disturbances (PQDs). The ST of the PQDs was used to extract significant waveform features which constitute the input vectors for different machine learning approaches, including the K-nearest neighbors’ algorithm (K-NN), decision tree (DT), and support vector machine (SVM) used for classifying the PQDs. The procedure was optimized by using the genetic algorithm (GA) and the competitive swarm optimization algorithm (CSO). To test the proposed methodology, synthetic PQD waveforms were generated. Typical single disturbances for the voltage signal, as well as complex disturbances resulting from possible combinations of them, were considered. Furthermore, different levels of white Gaussian noise were added to the PQD waveforms while maintaining the desired accuracy level of the proposed classification methods. Finally, all the hybrid classification proposals were evaluated and the best one was compared with some others present in the literature. The proposed ST-based CSO-SVM method provides good results in terms of classification accuracy and noise immunity.
Highlights
Power quality (PQ) is essential for electrical systems to operate properly with the minimum possible deterioration of performance
This paper addressed the optimal hybrid classification methods based on machine learning approaches for meeting detection, identification, and classification of simulated power quality disturbances (PQDs)
Stockwell transform (ST) was selected for detection and feature extraction of PQDs, and, following the trend nowadays to further optimize the recognition approach, several optimization algorithms were tested for optimal feature selection
Summary
Power quality (PQ) is essential for electrical systems to operate properly with the minimum possible deterioration of performance. Emerging PQ challenges, such as the growing integration of large power plants based on renewable sources, improvements in nonlinear loads, and the recent requirements of smart grids, must be considered to obtain an optimal operation of the existing power grid. These factors increasingly require constant revisions of the common power quality problems, enhanced standards, further optimization of control systems, and more powerful capabilities for measuring instruments. In the stage of PQDs’ analysis, some advanced mathematical techniques were used to extract the feature eigenvectors that enable disturbance identification
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