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Resiliency Metrics for Monitoring and Analysis of Cyber-Power Distribution System With IoTs
Not provided.
Equalization of Intensity-Modulated Fiber-Optic Voltage Sensors for Power Distribution Systems
We test fiber-optic voltage sensors based on optical reflection from a piezoelectric transducer. Our specific devices possess a 2 kHz fundamental resonance, and we verify a readily usable frequency band from approximately 10 Hz to 3 kHz, with a dynamic range of 60 dB for a detection system integrating over this entire band. Additionally, we demonstrate a digital signal processing approach to equalize the measured frequency response, enabling accurate retrieval of short-pulse inputs. These results suggest the value and applicability of intensity-modulated fiber-optic voltage sensors for measuring both steady-state waveforms and broadband transients which, coupled with the straightforward and compact design of the sensors, should make them effective tools in electric grid monitoring.
Quantum Reinforcement Learning for Volt-VAR Control in Power Distribution Systems
Volt-VAR control (VVC) is crucial in active distribution networks for optimizing voltage profiles and minimizing network losses. While traditional deep reinforcement learning (DRL) algorithms exhibit promise for VVC, they often require extensive computational resources to handle such a high-dimensional problem. As a potential solution, quantum reinforcement learning (QRL) algorithms integrate the computational capabilities of quantum computing into the DRL framework. However, existing QRL algorithms struggle with complex VVC problems due to the limitations of current quantum hardware. To bridge this gap, this paper proposes an innovative QRL algorithm featuring an end-to-end architecture that integrates a classical autoencoder, variational quantum circuits (VQCs), and classical post-processing layers. This design efficiently compresses high-dimensional grid states, enabling VQCs to leverage quantum advantages while producing multiple control device outputs tailored for VVC tasks. Numerical studies on three representative distribution systems verify the effectiveness and scalability of the proposed QRL algorithm, and demonstrate its enhanced performance over classical approaches with only approximately 1% of the parameters. Additionally, the robustness of our developed algorithm is validated through noisy quantum environments.
Topology Identification of Power Distribution Systems Using Time Series of Voltage Measurements.
Abstract not provided.
Deep Reinforcement Learning For Online Distribution Power System Cybersecurity Protection.
Abstract not provided.
Dynamic Model and Converter-Based Emulator of a Data Center Power Distribution System
Not provided.
Real-Time Asynchronous Information Processing in Distributed Power Systems Control
Not provided.
Hybrid Deep Reinforcement Learning For Online Distribution Power System Optimization and Control.
Abstract not provided.
Discrete Deep Reinforcement Learning For Online Distribution Power System Cybersecurity Protection.
Abstract not provided.
Machine Learning Based Fault Detection And Location In Electric Power Distribution Systems.
Abstract not provided.