Description:This paper explores the application of Artificial Intelligence (AI) to Synchrophasor Measurement Units (PMUs) for improved analysis and management of electrical power systems. Synchrophasors provide real-time, synchronized data on voltage, current, and frequency, which is crucial for monitoring grid health and stability. However, the large volume and complexity of this data pose significant challenges for traditional analysis methods. AI techniques such as machine learning, deep learning, and reinforcement learning offer advanced solutions for tasks like anomaly detection, fault diagnosis, predictive maintenance, and real-time system optimization. By leveraging AI, power system operators can achieve faster fault identification, better predictive insights, and more efficient grid operation. The paper also discusses the challenges of integrating AI with synchrophasor systems, including data quality, computational complexity, and the need for model interpretability. Overall, AI-enhanced synchrophasor measurement has the potential to revolutionize grid management, making it more resilient, efficient, and responsive to dynamic conditions.

, C , C , Claims:1. A phasor Measurement Unit (PMU) for measuring electrical parameters such as voltage, current, and frequency in a power system
a processor for analyzing the synchrophasor data using artificial intelligence algorithms, including machine learning or deep learning models, to detect anomalies, classify faults, or optimize grid performance
a communication interface for transmitting the analyzed data to a control center or monitoring system
a controller for adjusting system parameters based on real-time AI analysis, such as adjusting load distribution, generator outputs, or fault isolation actions.
2. The device of claim , wherein the artificial intelligence algorithms are used to classify grid faults into different categories based on synchrophasor data, and the controller adjusts grid operations based on the fault classification.
3. The device of claim , wherein the processor utilizes machine learning techniques, including supervised learning, unsupervised learning, or reinforcement learning, to continuously improve the accuracy of grid state estimation and fault detection.