About

Ali Abur is a University Distinguished Professor in the Department of Electrical and Computer Engineering at Northeastern University. He obtained his B.S. degree from Orta Dogu Teknik Universitesi in Ankara, Turkey, and his M.S. and Ph.D. degrees from The Ohio State University. He was a Professor at Texas A&M University until November 2005, when he joined Northeastern University as a Professor and Chair of the Electrical and Computer Engineering Department. His research and educational activities focus on power systems, particularly monitoring the power grid operating state during normal and faulted conditions, and modeling and estimation of power system operating conditions to improve reliability and efficiency. Abur has made significant contributions to power system state estimation and power engineering education, and he is recognized as a Fellow of the IEEE and a member of the National Academy of Engineering. Throughout his career, he has received numerous honors including the IEEE Power & Energy Society Outstanding Power Engineering Educator Award in 2014, the IEEE PES Charles Concordia Power Systems Engineering Award in 2025, and election to the National Academy of Engineering in 2023. He has held leadership positions such as Department Chair at Northeastern University and is involved in research projects related to resilient electric energy transmission networks and renewable energy integration.

Research topics

• Computer Science
• Engineering
• Electrical engineering
• Control engineering
• Reliability engineering
• Distributed computing
• Algorithm
• Telecommunications
• Electronic engineering
• Mathematics
• Real-time computing
• Systems engineering

Selected publications

• Convolutional Neural Network-assisted fault detection and location using few PMUs

  Electric Power Systems Research · 2024-06-29 · 8 citations

  articleSenior authorCorresponding
  PublisherDOI

• Ensuring Solution Uniqueness in Three-Phase Power System State Estimation

  2024-10-13 · 1 citations

  articleOpen accessSenior author

  This paper is concerned with the issue of potential non-unique solutions in three-phase state estimation. Theory of observability analysis for positive sequence power system state estimation is based on certain assumptions that avoid possibility of multiple solutions. Also, it is shown that observability of a positive sequence network remains independent of the network parameters or the operating state. When extending single-phase observability analysis directly to the three-phase case, this paper considers the possibility of converging to multiple solutions, i.e. solution non-uniqueness, even for cases where state estimator successfully converges. The study illustrates via numerical examples the likelihood of converging to entirely different solutions for certain network parameters. It also examines how the operating state, particularly under unbalanced loading, leads to solution non-uniqueness. The paper then describes an alternative approach to ensure a unique solution in three-phase state estimation. This method aims to accurately and uniquely estimate the state of any unbalanced three-phase system, irrespective of load imbalance, network configuration, existence of synchronous generators or transformers.

  PublisherOA PDFDOI

• Implementation of a State Estimator Ensuring Visibility for the Largest Possible Set of Buses

  2024-07-21

  article

  State estimation plays a crucial role in maintaining system security by assisting system operators to reliably monitor the system state at regular intervals. Challenges arise when measurements from substations become inaccessible, or certain communication channels malfunction. In these instances, operators often rely on previously received data, leading to inaccurate estimation results. Such data, inherently containing errors, are referred to as pseudo measurements, potentially biasing the state estimation. Complications intensify with topology errors, like incorrect branch parameters or breaker/switch status errors, causing the state estimator to fail to provide estimated states, yielding an empty output for all system states. The problem of divergence in state estimators also prohibits application of post-estimation bad data processing methods due to the absence of residuals, thus makes it impossible to determine the root cause of divergence. This shortcoming is addressed in this work by using two algorithms namely, "Resilient State Estimation" and "Recursive State Estimation", to implement a non-divergent state estimator. These algorithms work by isolating problematic areas with measurement issues and focusing on the remaining unaffected part of the system. Some simulation results have been presented to validate these algorithms under different anomalies.

  PublisherDOI

• Mitigating the Effects of Nonlinear Systematic Errors Within PMU Measurement Infrastructure in Three-Phase Power Systems

  IEEE Transactions on Power Systems · 2024-11-14 · 2 citations

  articleSenior author

  Introduction of Phasor Measurement Units (PMUs) into power grids facilitated several new monitoring and control applications due to the synchronized voltage and current phasor measurements they provide at high sampling rates. However, like any other measurement device they are vulnerable to noise and other errors which may be introduced by the chain of devices such as front-end instrument transformers as well as the various inaccuracies in the overall communication chain. If these errors are systematic, then phasor measurement infrastructure needs to be calibrated to avoid biased estimates of state and network parameters. This paper presents a remote calibration approach to accomplish this within the state estimation algorithm. The proposed approach enables calibrating a diverse range of linear and nonlinear systematic errors in PMU measurements. The method is tested on three-phase 118-bus test system considering examples of quadratic and linear systematic errors. The results prove that the proposed method could be of use in calibrating various types of systematic errors along the PMU measurement chain such as errors in instrument transformers level and/or PMU device itself.

  PublisherDOI

• Specifying angular reference for robust three-phase state estimation

  International Journal of Electrical Power & Energy Systems · 2024-03-23 · 2 citations

  articleOpen accessSenior author

  While the phase angle of any of the bus voltages can be chosen as the angular reference in the state estimation formulation of positive sequence networks, the same approach does not readily extend to three-phase network state estimation problem. It is commonly assumed that there is at least one bus where the bus voltages are perfectly balanced with phase angles displaced ±120° and these balanced three phase voltages are used as the three-phase reference in solving the three-phase state estimation problem. This assumption may be quite realistic in transmission networks, and for distribution networks with a strong transmission system connection. However, it might not be realistic to assume existence of a perfectly balanced reference bus in today’s distribution systems with ever increasing penetration of renewable sources or for isolated operation of microgrids. In this paper, a novel state estimation formulation will be presented which facilitates correct solution irrespective of the existence of buses with perfectly balanced voltages. The new formulation is general, and lends itself to bad data processing. It yields accurate results in any three-phase power system irrespective of its operating conditions (balanced or highly unbalanced), configuration (isolated microgrid, connected to transmission system, etc.) and whether or not it contains any synchronous generators. The performance of the method is validated using the IEEE 123 bus three-phase system.

  PublisherDOI

• Graph-learning-assisted state estimation using sparse heterogeneous measurements

  Electric Power Systems Research · 2024-06-29 · 5 citations

  articleSenior authorCorresponding
  PublisherDOI

• Learning Power Flow Models and Constraints From Time-Synchronized Measurements: A Review

  Proceedings of the IEEE · 2024-12-01 · 1 citations

  reviewOpen access

  Key operational and protection functions of power systems (e.g., optimal power flow scheduling and control, state estimation (SE), protection, and fault location) rely on the availability of models to represent the system’s behavior under different operating conditions. Power system models require knowledge of the components’ electrical parameters and the system topology. However, these data may be inaccurate for several reasons (e.g., inaccurate information of components datasheets and/or outdated topological information). The deployment of time synchronization in phasor measurement units (PMUs) and remote terminal units (RTUs) enables the collection of large datasets of synchronized measurements to infer power system models and learn associated power flow constraints. Within this context, this article presents a comprehensive review of measurement-based estimation methods for power flow models using time-synchronized measurements. It begins by exploring advancements in time dissemination technologies and the characterization of uncertainties in PMUs and instrument transformers (ITs), along with their implications for parameter estimation. This article then examines the power system parameter estimation problem, highlighting key techniques and methodologies. In the following, this article focuses on measurement models for state-independent power flow model estimation, including line parameters, admittance matrices, topology, and joint state-parameter estimation. Finally, this article discusses recent approaches for estimating state-dependent power flow models, with particular reference to linearized power flow approximations because of their large use in control applications.

  PublisherOA PDFDOI

• A Fast Discrete-Time State Estimator for Monitoring Power Grids with Inverter-Based Resources

  2024-10-13

  articleSenior author

  High penetration of inverter-based renewable energy resources (IBRES) introduced faster dynamics to power grids. Modelling and electromagnetic transient (EMT) simulation of these devices became important for their smooth integration. Monitoring of the IBRES dominated power grids holds similar critical importance. Present monitoring tools employ SCADA (Supervisory Control and Data Acquisition) and Phasor Mea-surement Unit (PMU) measurements, but they do not produce measurement samples at sufficient rate to monitor rapid dynam-ics associated with IBRES. Raw “point-on-wave” measurements could capture the fast IBRES responses, and enable the feasibility of monitoring them. Yet, monitoring the power grid at large scale and such a fast rate is a challenging task. Previously proposed transient state estimation approaches establish a theo-retical groundwork for it. However, further enhancements on the computational performance are necessary for the state estimators to keep up with the speed requirements. This paper proposes a method to accelerate state estimation to capture fast dynamics. Discrete-time element modelling based on numerical integration allows a two-step approach for state estimation if the selected time step is sufficiently small. The method introduces significant improvement in computational speed and memory without loss of accuracy. The proposed method is tested on 30-bus and 12589-bus systems, where the results of 30-bus system are verified using Alternative Transients Program (ATP).

  PublisherDOI

• Sparse PMU Placement Algorithm for Enhanced Detection and Identification of Power Grid Events

  IEEE Transactions on Power Systems · 2024-08-20 · 6 citations

  articleSenior author

  This paper is concerned about the challenge of timely and accurate detection and identification of events involving line outages and faults in power grids. Given the increasing number of installed phasor measurement units (PMU), their measurements are prime sources to be exploited to address this challenge since their high refresh rates allows to capture post- and pre- event phasor values. The challenge is mainly due to the fact that the number of installed PMUs is typically not sufficient to receive measurements from terminal bus(es) of every line. This challenge be addressed by applying model-based sparse estimation methods or data-driven approaches such as convolutional neural networks (CNN) and graphical neural networks (GCN). It should however be noted that success rates of these methods are very strongly related to strategic placement of PMUs, as their locations significantly influence the accuracy of line outage detection and fault identification as noted in <xref ref-type="bibr" rid="ref21" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[21]</xref>. This dependency constitutes the main motivation of the paper whose focus is development of a strategic PMU placement algorithm to maximize the performance of the event detection and identification methods that use PMU measurements as their inputs. An algorithm that is designed to yield the best possible locations to install a limited number of PMUs, optimizing their utility for model-based sparse estimation methods as well as advanced data-driven approaches is developed and implemented. Performance of the method is demonstrated with only 83 PMUs for the 1354 Bus system, and with 50 PMUs for the 196 bus system.

  PublisherDOI

• Computationally Robust Line Outage Detection and Identification in Three-Phase Networks

  2023-05-01

  articleSenior author

  Detection and identification of individual phase outages remains a challenging problem due to insufficient metering in three-phase unbalanced power networks. This problem was tackled for the transmission systems in our previous work. In this paper, this work is extended to detect phase outages in three-phase unbalanced systems using only the sparse estimation method. In addition, further improvements are introduced to increase the estimation accuracy for the virtual power injections at the terminal buses of disconnected lines, once the disconnected line is identified by sparse estimation methods. Simulation results are provided to experimentally validate the increased accuracy in detecting phase outages while decreasing the computational time by using the proposed approach.

  PublisherDOI

Recent grants

• Customized Wavelets for Analysis of Fault Transients in Power Systems

  NSF · $336k · 2008–2012

• New Methods of Fault Simulation and Location for Smart Grids Based on Synchronized Measurements

  NSF · $400k · 2012–2016

• A Comprehensive Approach to Monitoring Active Distribution Systems

  NSF · $379k · 2021–2025

• Multi-Area Monitoring and Transfer Capability Calculations in Restructured Power Systems

  NSF · $100k · 2005–2007

Frequent coauthors

• Yuzhang Lin

  New York University

  22 shared

• Antonio Gómez‐Expósito

  18 shared

• Murat Göl

  Middle East Technical University

  17 shared

• Mladen Kezunović

  15 shared

• Mert Korkali

  University of Missouri

  13 shared

• Fernando Magnago

  13 shared

• A. Keyhani

  12 shared

• Alireza Rouhani

  Dominion (United States)

  11 shared

Awards & honors

• IEEE Power & Energy Society Outstanding Power Engineering Ed…
• IEEE PES Boston Chapter Outstanding Engineering Award (2014)
• Elected Fellow of the Institute of Electrical and Electronic…
• National Science Foundation, Research Initiation Award (1989…
• IEEE Power and Energy Society (PES) Charles Concordia Power…