This research presents a novel approach to improving electric power quality using semiconductor devices by integrating Machine Learning (ML), Deep Learning (DL), and advanced control strategies. The research addresses key power quality challenges – including voltage sags, swells, harmonics, and transient disturbances – through a data-driven framework that combines traditional control techniques with adaptive learning models. A variety of algorithms, including Support Vector Machines (SVM), Random Forests, Neural Networks, Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks, were tested using real-time data. The results showed notable differences in performance, with deep learning models, especially LSTM, proving to be more accurate and dependable in identifying and forecasting power quality issues. In contrast, traditional ML models like SVM and Random Forest had difficulties with class imbalance, resulting in lower precision and recall. DL models, however, managed these challenges effectively, with CNN achieving a precision of 91.8% and LSTM attaining perfect accuracy (100%) and a recall of 94.5%. The study also highlighted the complications of handling imbalanced datasets, as indicated by classification warnings, emphasizing the importance of improved preprocessing and model adjustments for reliable predictions. The execution times varied significantly, with traditional control systems being faster but less capable in identifying complex patterns compared to the computationally intensive DL models. These findings highlight the promise of hybrid systems that integrate both traditional and data-driven control strategies to achieve adaptive and dependable power quality management. Both simulations and real-world experiments support the effectiveness of this hybrid method, suggesting a strong foundation for intelligent power quality solutions in future smart grid applications. The research concludes that although deep learning models offer superior accuracy and predictive power for complex power quality scenarios, practical deployment requires careful balancing of computational demands and addressing class distribution challenges.
The quality of electric power is a fundamental aspect of the stability and reliability of contemporary power systems1. Subpar power quality can result in a range of issues, including voltage sags, swells, harmonic distortions, and transient disturbances, which can negatively affect industrial machinery, household electronics, and sensitive equipment2,3,4,5. Traditional power quality management methods often depend on static control strategies and preset thresholds, which may not be sufficient to manage the increasingly complex and dynamic characteristics of modern power systems6. With the integration of renewable energy sources and a rise in electricity demand, advanced techniques that can adapt to these rapid changes are increasingly necessary7. This research investigates how Machine Learning (ML), Deep Learning (DL), and control algorithms can be integrated with semiconductor devices to create a sophisticated power quality management system. By combining the adaptability of data-driven models with the reliability of traditional control systems, the study proposes a hybrid approach to improve monitoring, fault detection, prediction, and active control of power quality disturbances8. This research aims to overcome the limitations of conventional techniques and highlight the advantages of adaptive algorithms for managing complex, real-time power quality data9.
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