There is an increasing need for monitoring and controlling uncertainties brought by distributed energy resources in distribution grids. For such goal, accurate multi-phase topology is the basis for correlating measurements in unbalanced distribution networks. Unfortunately, such topology knowledge is often unavailable …
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arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
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48 results for “Bus Voltages”
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 propose a decentralized Maximum Likelihood solution for estimating the stochastic renewable power generation and demand in single bus Direct Current (DC) MicroGrids (MGs), with high penetration of droop controlled power electronic converters. The solution relies on the fact that the primary control parameters are se…
Voltage control plays an important role in the operation of electricity distribution networks, especially with high penetration of distributed energy resources. These resources introduce significant and fast varying uncertainties. In this paper, we focus on reactive power compensation to control voltage in the presence…
The increasing penetration of distributed energy resources poses numerous reliability issues to the urban distribution grid. The topology estimation is a critical step to ensure the robustness of distribution grid operation. However, the bus connectivity and grid topology estimation are usually hard in distribution gri…
Optimal Volt/VAR control rules designed using deep learning.
problem Designing optimal Volt/VAR control rules for DERs to regulate voltage fluctuations.
method Formulated as a deep learning problem, where a DNN emulates Volt/VAR dynamics and optimizes rule parameters.
result DNN-based optimization outperforms MINLP in efficiency and accuracy.
New method selects critical DER scenarios for distribution grid investment planning.
problem Determining critical DER adoption scenarios for risk assessment in distribution grids.
method Bayesian Optimization framework using Gaussian Process surrogates and Pareto-critical acquisition function.
result Statistical guarantee and significant speed-up over exhaustive search in selecting critical DER scenarios.
A robust model handles up to 25% of outliers in time-series data for power flow calculations.
problem Handling outliers in time-series data for accurate power flow calculations.
method Robust data-driven process model with Schweppe-type generalized maximum likelihood estimator and projection statistics for outlier weighting.
result The model can handle up to 25% of outliers in the training data set.
Deep neural networks improve real-time power system state estimation and forecasting.
problem Real-time monitoring of power grids with large-scale renewable generation and electric vehicles.
method Developed a novel model-specific DNN for real-time PSSE and used deep RNNs for forecasting.
result Improved performance compared to existing alternatives, including Gauss-Newton PSSE solver.
Paper revisits five IF paradoxes using differential geometry.
problem Five paradoxes of Instantaneous Frequency in three-phase systems.
method Geometric interpretation of frequency to explain IF paradoxes.
result Revisits and explains five IF paradoxes through a common framework.
This paper uses deep reinforcement learning to automate electric transmission voltage control.
problem Automating voltage control in electric transmission systems.
method Deep reinforcement learning (DRL) applied to voltage control, with a novel DQN modification.
result DRL can automate voltage control at scale, but more research is needed.
This paper identifies critical cases for evaluating PV investment impacts on MV networks efficiently.
problem Challenges in maintaining and controlling voltages in MV distribution networks due to increasing PV generation.
method Clustering MV nodes based on electrical adjacency and time blocks, identifying critical cases for further study.
result A scalable method to time efficiently identify critical cases for PV investment evaluation.
Paper uses ensemble learning for more accurate power flow modeling.
problem Improving accuracy and efficiency of power flow modeling.
method Applies polynomial regression and ensemble learning (GB, bagging) to create a more accurate linear power flow model.
result Data-driven model outperforms traditional methods in accuracy and speed.
We use ellipsoids to solve power system voltage regulation problems.
problem Voltage regulation in power systems under uncertainty.
method Tractable ellipsoidal approximation for chance constrained optimizations.
result Efficiently trained machine learning model approximates uncertainty region.
This paper evaluates various bus arrival time prediction models.
problem Improving prediction accuracy of bus arrival times.
method General evaluation framework for various models, including raw data pre-processing.
result Preliminary results show strengths and weaknesses of common models.
BusTr predicts bus travel times from real-time traffic forecasts.
problem Improving accuracy of bus travel time predictions.
method Neural sequence model trained on real-time traffic forecasts.
result BusTr outperforms DeepTTE by 30% in Mean Absolute Percentage Error (MAPE).
This paper reviews low voltage load forecasting methods and applications.
problem Reliable forecasting for low voltage networks is needed for decarbonization.
method Comprehensive survey of current approaches, challenges, and trends.
result Established an open list of low voltage datasets for further research.
Paper uses deep reinforcement learning for adaptive emergency control of power systems.
problem Traditional emergency control schemes are inadequate for modern power grids due to increasing uncertainties.
method Developed deep reinforcement learning (DRL) for adaptive emergency control of power systems.
result Demonstrated excellent performance and robustness of DRL-based emergency control schemes in various scenarios.
Due to limited metering infrastructure, distribution grids are currently challenged by observability issues. On the other hand, smart meter data, including local voltage magnitudes and power injections, are communicated to the utility operator from grid buses with renewable generation and demand-response programs. This…
Regression-based Inverter Control for Decentralized Optimal Power Flow and Voltage Regulationeess.SY
The paper proposes a data-driven method for optimal power flow and voltage regulation in distribution grids.
problem Optimal power flow and voltage regulation in decentralized power grids.
method The approach uses a network model, historic data, and regression to find functions approximating optimal reactive power injections for inverters.
result The method achieves near-optimal results in voltage- and capacity-constrained loss minimization and voltage flattening.
Motivated by advantages of current-mode design, this brief contribution explores the implementation of weight matrices in neuromemristive systems via current-mode memristor crossbar circuits. After deriving theoretical results for the range and distribution of weights in the current-mode design, it is shown that any we…
Study develops ML emulators for generator models from terminal bus data.
problem Reconstruct generator models from terminal bus measurements.
method Used machine learning techniques, including VAR and LSTM models.
result Established trade-offs between linear AR and powerful LSTM models.
Neural network predicts electrochemical cell faults with 53% less error.
problem Predicting faults in electrochemical cells to avoid safety hazards and reduce costs.
method Self-supervised encoder-decoder neural network that learns degradation from operating conditions.
result Predicted voltage with 53% less error than parametric models, 64% faster fault prediction.
Two neural network models analyze bus system efficiency and demand.
problem Identify service gaps and quantify demand in public transportation.
method Two neural network models considering demographic data and metrics.
result Models can generalize to other cities' bus systems.
The wave equation is generally regarded as a linear approximation to the equation describing the amplitude of a transversely vibrating elastic string in the plane. But, as is shown in \cite{BC96}, the assumption of transverse vibration in fact implies that the wave equation describes the vibration…
Research presents a dataset and algorithm for optimizing bus timetables in New Delhi.
problem Improving efficiency of public transport in New Delhi.
method Real-time GPS data, constrained clustering algorithm, statistical analysis.
result Algorithm reduces waiting time and provides an efficient timetable.
Paper compares ML models for fast power system contingency case identification.
problem Identifying critical power system states quickly.
method Comparison of regression and classification ML models.
result MLPs most suitable for fast contingency case prediction.
LEAP nets model power grid disruptions for rapid response.
problem Modeling and predicting power grid disruptions.
method Transfer learning neural network embedding approach.
result LEAP nets can rapidly assess human operators' actions in emergencies.
Two novel models predict bus travel times with uncertainty, improving connection assurance.
problem Improving bus connection assurance by handling travel time uncertainty.
method Two novel approaches: Deep Quantile Regression (DQR) and Bayesian Recurrent Neural Networks (BRNN).
result DQR model performs best for 80%, 90%, and 95% prediction intervals, with small underestimation.
Thompson Sampling with bilateral uncertainty improves performance in Bayesian Optimization.
problem Twin difficulties of modeling and searching complex functions in high dimensions.
method Exploiting conditional independence, Thompson Sampling respecting bilateral uncertainty (BU).
result Thompson Sampling with BU is more effective than the additive approximation in small budgets.
This paper presents a bus travel time prediction system using deep neural networks.
problem Accurate travel time predictions for urban buses to compete with other modes of transport.
method Multi-output, multi-time-step deep neural network combining convolutional and LSTM layers.
result The proposed model significantly outperforms other methods and detects small irregular peaks quickly.
The paper uses Frenet frame to unify electrical and geometric quantities.
problem Defining time derivatives of electrical quantities in various conditions.
method Utilizes Frenet frame from differential geometry to define time derivatives in both stationary and transient conditions.
result Unifies and generalizes time- and phasor-domain frameworks.
Distribution grids are currently challenged by frequent voltage excursions induced by intermittent solar generation. Smart inverters have been advocated as a fast-responding means to regulate voltage and minimize ohmic losses. Since optimal inverter coordination may be computationally challenging and preset local contr…
Paper tackles adversarial attacks on ANN state estimation in smart grids.
problem Adversarial attacks degrade ANN state estimation accuracy without detection.
method Proposes population-based and gradient-based algorithms to generate attack vectors.
result DE algorithm is more effective than SLSQP in generating attack vectors.
DDSTN improves breast cancer diagnosis by leveraging imbalanced ultrasound modalities.
problem Imbalanced ultrasound modalities in diagnosing breast cancer.
method Integrates LUPI and MMD into a deep transfer learning framework.
result Outperforms state-of-the-art algorithms in BUS-based CAD.
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.
Generative model predicts ETA for bus routes using local data.
problem Accurate ETA prediction for public transit, especially buses, in cities with limited data.
method Generative deep learning model trained on local bus route data.
result Model updates ETA in real-time based on current trip information.
Given a complete, smooth metric measure space with the Bakry-Émery Ricci curvature bounded from below, various gradient estimates for solutions of the following general -heat equations and \[ u_t=Δ_f u+Ae^{pu}+Be^{-pu}+D \] are studied. As by-product, we obt…
Optimal Volt/VAR control rules are designed using deep neural networks.
problem Designing optimal Volt/VAR control rules for distributed energy resources (DERs).
method Formulate optimal rule design as a bilevel program, then reformulate it as training a deep neural network (DNN). Use proximal gradient descent (PGD) iterations to emulate Volt/VAR dynamics.
result The proposed solution can be adapted to single/multi-phase feeders and achieves enhanced steady-state voltage profiles.
A Neural-Network-Based Model Predictive Control of Three-Phase Inverter With an Output LC Filtereess.SY
Neural network improves voltage quality in three-phase inverters.
problem Achieving high-quality voltage with low THD in three-phase inverters.
method Combining MPC and ANN for voltage tracking.
result ANN-based control outperforms MPC in steady and dynamic performance.
Proposes BU-SPO method to improve text classification robustness.
problem Vulnerability of deep models in text classification.
method Bigram and unigram based adaptive Semantic Preservation Optimization (BU-SPO) method.
result Achieves highest attack success rates and semantic similarity by changing the smallest number of words.
Optimizes bus schedules to improve on-time performance.
problem Improving on-time performance of public transit systems.
method Formulated as a single-objective optimization task, solved using greedy algorithm, GA, and PSO.
result Enhanced bus timetables leading to better on-time performance.
Paper uses ML to predict insulator flashover risk.
problem Predicting flashover risk of aging insulators.
method Supervised ML with XGBoost, using LC and voltage features.
result Model accurately estimates insulator flashover probability.
The paper investigates how reducing memory supply voltage improves DNN accuracy under bit-cell faults.
problem Reducing energy consumption in deep neural networks by lowering memory supply voltage introduces bit-cell faults.
method The authors explore the robustness of DNN architectures to bit-cell faults and propose a regularizer to mitigate their effects.
result Operating the system in a faulty regime can save energy without significantly reducing accuracy.
RMCSE improves voltage estimation in low-observability distribution systems.
problem Insufficient measurements in distribution system state estimation.
method Combines matrix completion and power system model, minimizes rank and residual with different weights.
result Robust voltage estimation in low-observability systems without bad data detection.
Paper improves DNN accelerator robustness against bit errors with energy savings.
problem Bit errors in quantized DNN weights reduce energy efficiency.
method Combines robust fixed-point quantization, weight clipping, and random bit error training.
result Significantly improves robustness against random bit errors with high energy savings.
The increasing complexity of the power grid, due to higher penetration of distributed resources and the growing availability of interconnected, distributed metering devices re- quires novel tools for providing a unified and consistent view of the system. A computational framework for power systems data fusion, based on…
We compute the rings for a closed -manifold and then determine the Borsuk-Ulam indices with in .