Power Quality Assessment and Optimization in FUZZY-Driven Healthcare Devices
The advent of FUZZY technology has revolutionized healthcare, empowering smarter medical devices and equipment. However, the successful operation of these FUZZY-driven systems is contingent on high power quality. This paper introduces an innovative FUZZY-driven energy management system that combines...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10824810/ |
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| Summary: | The advent of FUZZY technology has revolutionized healthcare, empowering smarter medical devices and equipment. However, the successful operation of these FUZZY-driven systems is contingent on high power quality. This paper introduces an innovative FUZZY-driven energy management system that combines convolutional neural networks (CNNs) for real-time power quality event detection, long short-term memory (LSTM) networks for predictive analytics, and reinforcement learning for optimized control. Through extensive simulations on an IEEE 13-bus test feeder, we demonstrate the system’s superior performance in detecting and mitigating power quality disturbances. The CNN-based detection achieves 97% accuracy in classifying events, while the LSTM enables 95% accurate prediction of emerging issues. The reinforcement learning controller achieves 50% faster voltage sag restoration, 20% greater harmonic reduction, and 30% faster critical load recovery during outages compared to conventional methods. Key challenges, including data quality concerns, cybersecurity risks, and integration with legacy infrastructure, are discussed. This work represents a significant advancement in applying FUZZY technology to healthcare power quality management, offering a comprehensive solution that balances efficiency, reliability, and patient safety. The proposed system provides a scalable framework for modernizing power quality monitoring and control in healthcare facilities. |
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| ISSN: | 2169-3536 |