IOT Based Assisted Adaptive Control of Multilevel Inverter for Grid Tied Renewable Energy System

Abstract

This study presents an IoT-assisted adaptive control approach for a multilevel inverter used in grid-connected renewable energy systems, where maintaining stable and efficient power delivery is critical despite fluctuations in input sources and load variations. The proposed system integrates the multilevel inverter within a smart grid environment, utilizing IoT-based sensors and communication technologies to continuously monitor grid conditions, renewable energy generation, and load requirements in real time. To improve system performance, an advanced adaptive control strategy is employed, which dynamically modifies parameters such as modulation index, switching sequences, and control gains based on real-time feedback obtained through the IoT network. This approach enhances the system’s transient behavior, minimizes voltage and current distortions, and ensures effective regulation of both active and reactive power, overcoming the limitations of traditional linear control methods. Additionally, the inverter supports multiple modes of operation, including constant voltage, constant current, and constant power control, making it adaptable for applications involving solar photovoltaic systems, wind energy integration, and hybrid energy storage systems within smart grids.These results highlight the potential of combining multilevel inverter technology with IoT-enabled adaptive control to meet the demands of modern grid-tied renewable energy systems.

Introduction

The text discusses the challenges of integrating renewable energy sources like solar and wind into power grids due to their variable output, which affects voltage stability, frequency, and power quality. Multilevel inverters are used to improve AC power conversion for grid integration, but traditional PI controllers struggle with rapid changes in load and input conditions, leading to inefficiencies, higher harmonic distortion, and slower response. The absence of real-time monitoring further limits system performance.

To address these issues, the proposed system uses an ESP32-based IoT-enabled controller with an AC voltage sensor to continuously monitor grid conditions. Under normal operation, Relay 1 connects the load to the main AC supply. When a voltage drop or fault is detected, the system automatically switches to Relay 2, connecting the load to an inverter to ensure uninterrupted power supply. Real-time system status and voltage readings are displayed on an LCD, while data is also transmitted to the Adafruit IO cloud platform for remote monitoring and analysis.

The hardware setup includes the ESP32 microcontroller (with Wi-Fi/Bluetooth for IoT communication and multiple I/O capabilities), an AC voltage sensor for safe voltage measurement, and an LCD display for local system feedback. Overall, the system improves reliability, monitoring, and energy management in renewable grid applications by enabling automated switching and real-time IoT-based supervision.

Conclusion

In summary, the proposed IoT-enabled adaptive control system for a multilevel inverter in a grid-connected renewable energy setup offers a dependable and efficient approach for ensuring uninterrupted power delivery under varying conditions. By incorporating the ESP32 microcontroller, the system facilitates continuous voltage monitoring and enables intelligent switching between the main AC supply and inverter backup. The automated relay operation allows smooth transition during voltage disturbances or outages, enhancing system reliability and minimizing interruptions. The inclusion of a 16×2 LCD provides clear real-time display of system parameters for local observation, while integration with Adafruit IO supports remote monitoring, data storage, and effective energy management. Compared to traditional methods, this system improves power quality, reduces the need for manual control, and enhances overall operational efficiency.

References

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Copyright

Copyright © 2026 B. Ragul, S. Uma Bharathi , M. Yokesh, N. Thillaikarasi, Dr. P. Selvakumar . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.