<![CDATA[This study presents the design and implementation of a Power Quality Network Analyzer (PQNA) employing an ESP32 microcontroller to monitor and analyze key power quality parameters, including voltage, current, power factor, and total harmonic distortion (THD) for each phase of a three-phase induction motor. Traditional monitoring systems rely on manual or fixed data acquisition methods, which are inadequate for achieving efficient real-time data analysis. In contrast, the proposed system utilizes the RS-485 communication protocol, ensuring robust and reliable industrial data transfer, and functions as a data gateway that transmits all acquired parameters to the Thinger.io cloud platform for real-time visualization and analytics. This configuration enables enhanced diagnostic capabilities and predictive maintenance, improving system reliability and operational efficiency. Furthermore, the project demonstrates the potential of IoT integration in enabling remote assessment of power quality, thereby minimizing motor downtime and facilitating data-driven decision-making for performance optimization in industrial environments. The proposed framework also contributes to the advancement of intelligent industrial automation, emphasizing how real-time data analytics can significantly enhance productivity and sustainability.]]>
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