Power Quality Assessment and Optimization in FUZZY-Driven Healthcare Devices

作     者：Nishad, Dinesh Kumar Khalid, Saifullah Singh, Rashmi 

作者机构：IBM Multi Act Co Ltd Khartoum 11115 Sudan 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2025年第13卷

页      面：9679-9688页

核心收录：

主　　题：FUZZY convolutional neural networks (CNN) cyber security deep learning energy management healthcare technology intrusion detection system (IDS) IoT security long short-term memory (LSTM) machine learning microgrids neural networks power quality reinforcement learning smart grids healthcare technology intrusion detection system (IDS) IoT security long short-term memory (LSTM) machine learning microgrids neural networks power quality reinforcement learning smart grids 

摘      要：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.