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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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10824810/ |
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| author | Dinesh Kumar Nishad Saifullah Khalid Rashmi Singh |
| author_facet | Dinesh Kumar Nishad Saifullah Khalid Rashmi Singh |
| author_sort | Dinesh Kumar Nishad |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b23f261bd37f4b708cc9ba23b292dcc6 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-b23f261bd37f4b708cc9ba23b292dcc62025-01-21T00:02:03ZengIEEEIEEE Access2169-35362025-01-01139679968810.1109/ACCESS.2025.352600110824810Power Quality Assessment and Optimization in FUZZY-Driven Healthcare DevicesDinesh Kumar Nishad0Saifullah Khalid1https://orcid.org/0009-0005-6894-0087Rashmi Singh2https://orcid.org/0000-0002-6139-466XIBM Multi Activities Company Ltd., Khartoum, SudanIBM Multi Activities Company Ltd., Khartoum, SudanIBM Multi Activities Company Ltd., Khartoum, SudanThe 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.https://ieeexplore.ieee.org/document/10824810/FUZZYconvolutional neural networks (CNN)cyber securitydeep learningenergy managementhealthcare technology |
| spellingShingle | Dinesh Kumar Nishad Saifullah Khalid Rashmi Singh Power Quality Assessment and Optimization in FUZZY-Driven Healthcare Devices IEEE Access FUZZY convolutional neural networks (CNN) cyber security deep learning energy management healthcare technology |
| title | Power Quality Assessment and Optimization in FUZZY-Driven Healthcare Devices |
| title_full | Power Quality Assessment and Optimization in FUZZY-Driven Healthcare Devices |
| title_fullStr | Power Quality Assessment and Optimization in FUZZY-Driven Healthcare Devices |
| title_full_unstemmed | Power Quality Assessment and Optimization in FUZZY-Driven Healthcare Devices |
| title_short | Power Quality Assessment and Optimization in FUZZY-Driven Healthcare Devices |
| title_sort | power quality assessment and optimization in fuzzy driven healthcare devices |
| topic | FUZZY convolutional neural networks (CNN) cyber security deep learning energy management healthcare technology |
| url | https://ieeexplore.ieee.org/document/10824810/ |
| work_keys_str_mv | AT dineshkumarnishad powerqualityassessmentandoptimizationinfuzzydrivenhealthcaredevices AT saifullahkhalid powerqualityassessmentandoptimizationinfuzzydrivenhealthcaredevices AT rashmisingh powerqualityassessmentandoptimizationinfuzzydrivenhealthcaredevices |