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Kumar Pandey, Anubhav
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Electrical & Electronics Engineering

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An economical approach of structural strength monitoring utilizing internet of things S. Nayak, Deekshitha; Kumar Pandey, Anubhav
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8752

Abstract

In the current environment, structural health monitoring (SHM), has become increasingly important. The cost of sensors and connectivity has significantly decreased, allowing for remote data gathering for critical analysis and structure monitoring. This allows for the assessment and improvement of the structures' residual lifespan. The internet of things (IoT) is a network of intelligent sensors that combines the identification and detection followed by sending the different structural responses to remote computers for further analysis i.e., processing and monitoring. In this work, an integrated IoT platform for damage detection is proposed which includes an Arduino, Wi-Fi module, and sensors. The sensors gather responses from the host structure which follows a precise mathematical model is introduced to determine and measure the structural damage in comparison to the reactions of the structural member that is in good health. To determine the degree of damage, the responses recorded from the damaged and healthy beams are analyzed using the cross-correlation (CC) damage index. Moreover, the analysis carried out reveals the CC values are uploaded to the cloud, where, if the CC value is over the threshold limit, a mobile warning message is delivered.
Machine learning based annual solar energy forecasting for enhanced grid integration of photovoltaic systems K. Krishnamurthy, Nandini; Kumar Pandey, Anubhav; Sreenivasa Rao, Sumana
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10215

Abstract

The increase in electricity demand is witnessed by many nations due to the rise in population and ongoing developments. To cope with energy requirements, countries are looking towards cleaner alternatives to reduce overreliance on energy generation from conventional resources. The introduction of artificial intelligence (AI) in real-world applications is acknowledged positively by experts as it enhances the performance and efficiency of the system. This paper reports the advancement of AI in harnessing renewable energy sources (RESs) to their true potential by leveraging their response when the grid is not able to fulfill the power requirement from conventional resources. Moreover, the prediction also remains a challenge with renewables due to their volatile behavior, especially with solar-based energy generation. This issue is also addressed by interfacing AI-enabled applications and the difference between true and predicted values for one year is observed. The result reveals that the true response aligns with the predicted response, which ensures the ability of AI to harness solar energy by consuming minimal time. The proposed approach is also promising from the utility operators’ and end users’ perspectives in designing any large-scale renewable projects for sustainable development and also encourages the utilization of renewables to a larger extent.
Co-Authors K. Krishnamurthy, Nandini S. Nayak, Deekshitha Sreenivasa Rao, Sumana