Enhancing Power Quality and Grid Control with Real-Time Monitoring Sensors
DOI:
https://doi.org/10.63278/1275Keywords:
Smart Grid, Photoelectric Sensors, Optical System, Performance.Abstract
Monitoring and controlling power quality is become crucial to ensure the power system runs steadily as smart grids have developed quickly. Issues related to conventional power quality monitoring techniques include insufficient real-time performance and limited monitoring accuracy. In order to increase real-time monitoring accuracy, this paper suggests smart grid reliability tracking and modification technique that employs photoelectric sensors combined optical systems signal treatment. In this article, the power grid's power quality is continuously monitored using photoelectric sensors, which then send information about the monitoring to an optical system. Key power quality metrics are extracted via signal processing of information collected via optical devices. Finally, the control and improvement of the quality of power can be achieved by making changes to the smart grid's important equipment. The tests show that using photoelectric sensors along with optical system processing to observe and change the power quality in a smart grid can make tracking more accurate and improve real-time performance. The power grid is much more stable and reliable now that it could be checked and managed at real time for changes in power quality.
References
Bubshait, Abdullah S., et al. "Power quality enhancement for a grid connected wind turbine energy system." IEEE Transactions on Industry Applications 53.3 (2017): 2495-2505.
Zargar, Behzad, et al. "Power quality improvement in distribution grids via real-time smart exploitation of electric vehicles." Energies 14.12 (2021): 3533.
Qi, Hairong, et al. "A resilient real-time system design for a secure and reconfigurable power grid." IEEE Transactions on Smart Grid 2.4 (2011): 770-781.
Tabassum, Saleha, Attuluri R. Vijay Babu, and Dharmendra Kumar Dheer. "Real-time power quality enhancement in smart grids through IoT and adaptive neuro-fuzzy systems." Science and Technology for Energy Transition 79 (2024): 89.
Qureshi, Kashif Naseer, Raza Hussain, and Gwanggil Jeon. "A distributed software defined networking model to improve the scalability and quality of services for flexible green energy internet for smart grid systems." Computers & Electrical Engineering 84 (2020): 106634.
Artale, Giovanni, et al. "Real-time power flow monitoring and control system for microgrids integration in islanded scenarios." IEEE Transactions on Industry Applications 55.6 (2019): 7186-7197.
Dhotre, Virendrakumar Anna, Habibulla Mohammad, Prakash Kumar Pathak, Anurag Shrivastava, and T. Rajasanthosh Kumar. "Big data analytics using MapReduce for education system." Linguistica Antverpiensia (2021): 3130-3138.
Karthick, T., and K. Chandrasekaran. "Design of IoT based smart compact energy meter for monitoring and controlling the usage of energy and power quality issues with demand side management for a commercial building." Sustainable Energy, Grids and Networks 26 (2021): 100454.
Mewada, S., Saroliya, A., Chandramouli, N., Kumar, T. R., Lakshmi, M., Mary, S. S. C., & Jayakumar, M. (2022). Smart diagnostic expert system for defect in forging process by using machine learning process. Journal of Nanomaterials, 2022(1), 2567194.
Tabassum, Saleha, Attuluri R. Vijay Babu, and Dharmendra Kumar Dheer. "A comprehensive exploration of IoT-enabled smart grid systems: power quality issues, solutions, and challenges." Science and Technology for Energy Transition 79 (2024): 62.
Singh, Chaitanya, et al. "Applied machine tool data condition to predictive smart maintenance by using artificial intelligence." International Conference on Emerging Technologies in Computer Engineering. Cham: Springer International Publishing, 2022.
Morello, Rosario, et al. "A smart power meter to monitor energy flow in smart grids: The role of advanced sensing and IoT in the electric grid of the future." IEEE Sensors Journal 17.23 (2017): 7828-7837.
Buduru, Naveen Kumar, and Srinivas Bhaskar Karanki. "Real-time power quality event monitoring system using digital signal processor for smart metering applications." Journal of Electrical Engineering & Technology 18.4 (2023): 3179-3190.
Gupta, Nitin, et al. "An experimental investigation of variable-step-size Affine Projection Sign based algorithm for power quality enhanced grid-interactive solar PV system." Electric Power Systems Research 228 (2024): 110036.
Alonso-Rosa, Manuel, et al. "Novel internet of things platform for in-building power quality submetering." applied sciences 8.8 (2018): 1320.
Anderson, David, et al. "Intelligent Design" Real-Time Simulation for Smart Grid Control and Communications Design." IEEE Power and Energy Magazine 10.1 (2011): 49-57.
Alyousef, Ammar, et al. "Enhancing power quality in electrical distribution systems using a smart charging architecture." Energy Informatics 1 (2018): 127-148.
Hand, Banjamin M., and Colleen P. Bailey. "Real-Time Power Quality Classification Using a Raspberry Pi 3B+." 2024 IEEE MetroCon. IEEE, 2024.
He, Shunfan, Kaicheng Li, and Ming Zhang. "A real-time power quality disturbances classification using hybrid method based on S-transform and dynamics." IEEE transactions on instrumentation and measurement 62.9 (2013): 2465-2475.
Chawda, Gajendra Singh, et al. "Comprehensive review on detection and classification of power quality disturbances in utility grid with renewable energy penetration." IEEE Access 8 (2020): 146807-146830.
Tabassum, Saleha, Attuluri R. Vijay Babu, and Dharmendra Kumar Dheer. "Real-time power quality enhancement in smart grids through IoT and adaptive neuro-fuzzy systems." Science and Technology for Energy Transition 79 (2024): 89.
Solanki, Mehul Dansinh, and S. K. Joshi. "Recapitulation of electric spring: A smart grid device for real time demand side management and mitigating power quality issues." 2016 National Power Systems Conference (NPSC). IEEE, 2016.
Ahmadi, Abdollah, et al. "Power quality improvement in smart grids using electric vehicles: a review." IET Electrical Systems in Transportation 9.2 (2019): 53-64.
Patro, Pramoda, et al. "A hybrid approach estimates the real-time health state of a bearing by accelerated degradation tests, Machine learning." 2021 Second International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE). IEEE, 2021.
Kumar, Dinesh, Firuz Zare, and Arindam Ghosh. "DC microgrid technology: system architectures, AC grid interfaces, grounding schemes, power quality, communication networks, applications, and standardizations aspects." Ieee Access 5 (2017): 12230-12256.
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