Analysis of Power Quality in Technology Based Power Systems
DOI:
https://doi.org/10.64803/joeer.v2i1.23Keywords:
Power Quality, Smart Grid, Machine Learning, Internet of Things, Power ElectronicsAbstract
The rapid advancement of power system technologies has significantly transformed the way electrical power quality (PQ) is monitored, managed, and improved. The increasing penetration of non-linear loads, automation systems, and renewable energy sources has intensified power quality challenges, including harmonic distortion, voltage sags, voltage fluctuations, and transient disturbances. This study aims to analyze the impact of modern technological approaches on power quality enhancement in contemporary power systems. A descriptive-analytical research methodology was employed through a systematic review of recent scientific literature, international standards, and technical reports related to power quality management. The collected data were analyzed using qualitative content analysis and comparative evaluation to assess the effectiveness of automation, machine learning, Internet of Things (IoT)-based monitoring systems, and power electronic mitigation devices. The results indicate that integrated technological solutions significantly improve power quality performance compared to conventional approaches. Machine learning techniques, particularly Artificial Neural Networks, demonstrate high accuracy in disturbance classification and adaptive control, while IoT-based systems enable real-time monitoring and rapid response to power quality deviations. Furthermore, power electronic devices such as Active Power Filters, Static Var Compensators, and Dynamic Voltage Restorers effectively mitigate harmonics and stabilize voltage. However, challenges related to implementation cost, system complexity, cybersecurity, and regulatory inconsistency remain. The study concludes that an integrated, intelligent, and data-driven framework is essential for sustainable power quality management in modern power systems.
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