Framework detects anomalies in real-time PMU data.
problem Anomaly detection in power grid operations.
method Statistical learning and dynamical model.
result Effective anomaly detection and classification.
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9 results for “μPMU”
Study designs neural networks for fault localization, state estimation, and optimal PMU placement in power systems.
problem Fault localization, state estimation, and optimal PMU placement in power systems.
method Designs and compares various neural networks for fault localization, builds machine learning schemes for state estimation and parameter estimation, and designs an algorithm for optimal PMU placement.
result Comprehensive comparison of neural networks for fault localization shows that Graphical Convolutional NN and Neural Graph-based ODE perform best.
New method uses μPMU data to identify distribution grid line outages.
problem Limited performance of traditional outage detection methods in urban grids with DERs.
method Data-driven approach based on stochastic time series analysis from μPMU.
result μPMU data enables fast and accurate identification of line outages.
Bayesian method improves grid admittance matrix estimation from noisy data.
problem Accurate estimation of power grid admittance matrix in dynamic systems.
method Data-driven identification using voltage and current measurements, Bayesian approach.
result Significantly greater accuracy in admittance matrix estimation compared to existing methods.
CNN improves fault location in power grids with high accuracy.
problem Challenges in real-time fault location due to diverse fault types and fast reclosures.
method Convolutional Neural Network (CNN) classifier using bus voltages.
result CNN-based localization tool outperforms other machine learning methods.
We consider the problem of reconstructing the dynamic state matrix of transmission power grids from time-stamped PMU measurements in the regime of ambient fluctuations. Using a maximum likelihood based approach, we construct a family of convex estimators that adapt to the structure of the problem depending on the avail…
Paper uses DL and image embedding to classify power grid disturbances.
problem Classifying transient disturbances in power grids.
method Transformed time series data into images using Gramian Angular Field, then applied CNN and RNN for classification.
result DL algorithms outperform traditional data mining methods in power grid disturbance classification.
A new framework uses DDQN to simplify WECC CLM for efficient load modeling.
problem Complexity and high parameter count in WECC CLM.
method Two-stage approach with DDQN for load composition and parameter selection.
result The framework efficiently approximates WECC CLM transient responses.
New method identifies distribution grid outages using smart meter data.
problem Outages in urban distribution grids due to DERs and smart meters' last gasp signals.
method Data-driven approach based on stochastic time series analysis and maximum likelihood estimation.
result Proves optimal performance in identifying distribution grid outages using smart meter data.