A5016220869- Power System Optimization and Stability
- Optimal Power Flow Distribution
- Power Systems Fault Detection
- Microgrid Control and Optimization
- Energy Load and Power Forecasting
- Advanced Computational Techniques and Applications
- Industrial Technology and Control Systems
- Smart Grid Energy Management
- Islanding Detection in Power Systems
- Infrastructure Resilience and Vulnerability Analysis
- Smart Grid Security and Resilience
- Power Quality and Harmonics
- Disaster Management and Resilience
- Remote Sensing and LiDAR Applications
- Power System Reliability and Maintenance
- Electric Power System Optimization
- Fault Detection and Control Systems
- Traffic Prediction and Management Techniques
- Remote Sensing in Agriculture
- Anomaly Detection Techniques and Applications
- Time Series Analysis and Forecasting
- Landslides and related hazards
- Power Systems and Technologies
- IPv6, Mobility, Handover, Networks, Security
- Hydrology and Watershed Management Studies
Western Norway University of Applied Sciences
2018-2025
Norwegian University of Science and Technology
2023
Virginia Tech
2012-2022
Institute of Electrical and Electronics Engineers
2020-2021
StormGeo (Norway)
2021
Florida A&M University - Florida State University College of Engineering
2017-2018
Florida State University
2015-2018
California Institute for Regenerative Medicine
2014-2015
University of California, Berkeley
2013-2015
Berkeley College
2015
This paper describes a research project to develop network of high-precision phasor measurement units, termed micro-synchrophasors or μPMUs, and explore the applications μPMU data for electric power distribution systems.
This paper proposes a novel method for topology detection in distribution networks called the Time-series signature verification (TSV-Top). The TSV-Top analyzes data from phasor measurement units (PMU or μPMU) installed on power feeders. relies fact that time series systems contain similar trends when network changes occur, and basically performs projection of actual voltage phasorial patterns onto library signals associated with possible transictions given network. proposed algorithm is...
This paper proposes a data-driven approach to detect the switching actions and topology transitions in distribution networks. It is based on real time analysis of time-series voltages measurements. The draws data from high-precision phasor measurement units ($\mu$PMUs or synchrophasors) for key fact that taken network has specific patterns representing state such as changes. proposed algorithm comparison actual voltage measurements with library signatures derived possible topologies...
In this work we address the problem of static state estimation (SE) in distribution grids by leveraging historical meter data (pseudo-measurements) with real-time measurements from synchrophasors (PMU data). We present a Bayesian linear estimator based on approximation power flow equations for networks, which is computationally more efficient than standard nonlinear weighted least squares (WLS) estimators. show via numerical simulations that proposed strategy performs similarly to WLS small...
The power system has been incorporating increasing amount of unconventional generations and loads, such as distributed renewable resources, electric vehicles, controllable loads. induced dynamic stochastic flow require high-resolution monitoring technology agile decision support techniques for diagnosis control. This paper discusses the application micro-phasor measurement unit (μPMU) data distribution network event detection. A novel data-driven detection method, namely hidden structure...
After decades of research, automatic synthetic aperture radar (SAR)-optical registration remains an unsolved problem. SAR and optical satellites utilize different imaging mechanisms, resulting in imagery with dissimilar heterogeneous characteristics. Transforming translating these characteristics into a shared domain has been the main challenge SAR-optical matching for many years. Combining two sensors will improve quality existing future remote sensing applications across multiple...
Forecasting electricity demand is an essential part of the smart grid to ensure a stable and reliable power grid. With increasing integration renewable energy resources into grid, forecasting for critical at all levels, from distribution household. Most existing methods, however, can be considered black-box models as result deep digitalization enablers, such neural networks, which remain difficult interpret by humans. Moreover, capture inter-dependencies among variables presents significant...
Flywheel energy storage (FES) has attracted new interest for uninterruptible power supply (UPS) applications in a facility microgrid. Due to technological advancements, the FES become promising alternative traditional battery technologies. This paper aims at developing tool demonstrate use of units securing critical loads during utility outage microgrid environment. The is modeled, simulated and evaluated MATLAB/SIMULINK® A data center used represent case study. It illustrates how an can...
This paper proposes a novel phase identification method for distribution networks where phases can be severely unbalanced and insufficiently labeled. The analysis approach draws on data from high-precision phasor measurement units (micro-synchrophasors or uPMUs) systems. A key fact is that time-series voltage phasors taken network show specific patterns regarding connected at points. algorithm based analyzing cross-correlations over magnitudes along with angle differences two candidate to...
In recent years, extreme weather events have severely affected the performance of electric grid. Very large-scale (VLSE) with potentially catastrophic impacts on grid pose more than an inconvenience in today?s electricity-driven lifestyle, and frequency severity such may continue to increase as a consequence global climate change. This article summarizes state art leveraging distributed resources improve resilience It also highlights technical questions that need be addressed through...
Smart buildings today are aimed at providing safe, healthy, comfortable, affordable, and beautiful spaces in a carbon energy-efficient way. They emerging as complex cyber-physical systems with humans the loop. Cost, need to cope increasing functional complexity, flexibility, fragmentation of supply chain, time-to-market pressure rendering traditional heuristic ad hoc design paradigms inefficient insufficient for future. In this paper, we present platform-based methodology smart building...
Network topology in distribution networks is often unknown, because most switches are not equipped with measurement devices and communication links. However, knowledge about the actual critical for safe reliable grid operation. This paper proposes a voting-based detection method based on micro-synchrophasor measurements. The minimal difference between measured calculated voltage angle or magnitude, respectively, indicates topology. Micro-synchrophasors micro-Phasor Measurement Units (μPMU)...
Power system has been incorporating increasing amount of unconventional generations and loads such as renewable resources, electric vehicles, controllable loads. The induced short term stochastic power flow requires high resolution monitoring technology agile decision support techniques for diagnosis control. In this paper, we discuss the application micro-phasor measurement unit (μPMU) distribution network monitoring, study learning based data-driven methods abnormal event detection. We...
Transformer-based models have greatly improved Land Use and Cover (LULC) applications. Their revolutionary ability to analyze extract key information has advanced the field. However, high computational cost of these presents a considerable obstacle their practical implementation. Therefore, this study aims strike balance between accuracy when employing transformer-based for LULC analysis. We exploit transfer learning fine-tuning strategies optimize resource utilization models. Furthermore,...
Short-term household electricity load forecasting is important for utility companies to ensure reliable power supplies. Traditional methods relied on historical records from one single data source and have limitations with insufficient or missing data. Recently, an emerging family of machine learning algorithms, multitask (MTL), has been developed the potential forecasting. By MTL, consumption multiple communities can be fused improve accuracy. However, appropriate modeling relatedness...
This paper proposes a novel approach to detecting the topology of distribution networks based on analysis time series measurements. The draws data from high-precision phasor measurement units (PMUs or synchrophasors) for systems. A key fact is that time-series taken dynamic system show specific patterns regarding state transitions such as opening closing switches, kind signature each change. algorithm proposed here comparison actual recent transition against library signatures derived...
Vegetation Management is a significant preventive maintenance expense in many power transmission and distribution companies. Traditional operational practices have proven ineffective are rapidly becoming obsolete due to the lack of frequent inspection vegetation environmental states. The rise satellite imagery data machine learning provides an opportunity close loop with continuous data-driven monitoring. This paper proposes automated framework for monitoring along lines using...
Geohazards such as landslides, rock avalanches or falls from unstable slopes can seriously threaten human life and infrastructure. Monitoring coupled with real-time data analyses to assess the risk they pose mitigate this is thus indispensable. Machine learning-based methods for analysing monitoring recently significantly improved forecasting possibilities failure events. However, one major limitation of Learning-based that primarily provide "Black Box"-models. These models can, example,...
ABSTRACT Access to smart metre data at higher resolutions has the potential of improving energy management and load forecasting. However, such presents several complexities, as added pressure on resources increased expenses. Super‐Resolution (SR) is a technology with capability solving this problem, by reconstructing low‐resolution into high‐resolution data. This study examines predicting obtained from The conducted in Bergen, Norway, where power consumption was used residential building. To...