Data Analysis and Algorithm Innovation in Power System IntelligentMonitoring and Early Warning Technology
DOI:
https://doi.org/10.69996/jsihs.2024015Keywords:
Sensor Data, Early Warning System, Machine Learning, Classification, Power System, Intelligent MonitoringAbstract
Data analysis and algorithm innovation play a pivotal role in enhancing power system
intelligent monitoring and early warning technology. With the increasing complexity of modern power
grids, the integration of advanced data analytics enables real-time monitoring, fault detection, and
predictive maintenance. By leveraging machine learning algorithms, anomaly detection techniques, and
big data analytics, power systems can efficiently identify potential risks and failures before they escalate
into serious issues. These innovations not only improve grid reliability and resilience but also optimize
resource utilization. Early warning mechanisms based on intelligent algorithms provide timely alerts,
allowing for preventive measures that ensure the stability and safety of the power network. This approach
fosters a smarter, more adaptive power infrastructure capable of meeting growing energy demands while
minimizing downtime and disruptions.This paper presents a comprehensive investigation into the
development and efficacy of an intelligent early warning system for power systems. Leveraging machine
learning algorithms, IoT sensors, and cloud computing frameworks, the system aims to enhance real-time
monitoring capabilities and facilitate proactive intervention and maintenance. Through a series of
simulations and iterations, the study demonstrates significant improvements in performance metrics such
as accuracy, precision, recall, and F1 score. The integration of data analytics and classification techniques
enables the system to accurately predict and classify anomalies, thereby minimizing risks and ensuring
the reliability and efficiency of power systems. Through a series of simulations and iterations, the study
demonstrates significant improvements in performance metrics such as accuracy, precision, recall, and F1
score. Specifically, the system achieves an average accuracy of 95%, precision of 92%, recall of 94%, and
F1 score of 92% across multiple iterations. The integration of data analytics and classification techniques
enables the system to accurately predict and classify anomalies, thereby minimizing risks and ensuring
the reliability and efficiency of power systems.
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