Ali Abur, Ph.D.
Professor of Electrical and Computer Engineering, Northeastern University | Faculty Affiliate, Global Resilience Institute
Boston, MA, UNITED STATES
Professor Abur focuses on power systems.
Media
Publications:
Documents:
Photos:
Videos:
Audio/Podcasts:
Biography
Ali Abur obtained his B.S. degree from Orta Dogu Teknik Universitesi, Turkey in 1979 and both his M.S. and Ph.D. degrees from the Ohio State University in 1981 and 1985 respectively. He was a faculty member at Texas A&M University until November 2005 when he joined the faculty of Northeastern University as a Professor and Chair of the Electrical and Computer Engineering Department. His research and educational activities have been in the area of power systems. He is a Fellow of the IEEE for his work on power system state estimation. He co-authored a book and published widely in IEEE journals and conferences. He serves on the Editorial Board of IEEE Transactions on Power Systems and Power Engineering Letters.
Areas of Expertise (9)
Placement and Use of Phasor Measurement Units for Network Observability and State Estimation
Fault Location in Power Grids
Power Transmission and Distribution Systems
Energy
Power Systems
Design and Engineering
State Estimation
Power System State and Parameter Estimation
Detection and Identification of Measurement and Network Parameters
Education (3)
Ohio State University: Ph.D. 1985
Ohio State University: M.S. 1981
Orta Dogu Teknik Universitesi: B.S. 1979
Links (3)
Articles (8)
Constrained Iterated Unscented Kalman Filter for Dynamic State and Parameter Estimation
IEEE Transactions on Power Systems
A. Rouhani and A. Abur
2018 This paper presents a robust dynamic state estimator for the synchronous generators with unknown parameters. The estimator uses a constrained iterated unscented Kalman filter to estimate the state variables and unknown parameters of a two-axis model of a synchronous generator. The developed estimator's performance is validated using simulations, where the estimator is subjected to arbitrary initialization and large parameter errors. The developed dynamic estimator can potentially be used not only to track the dynamic states but also to detect and identify changes in model parameters with little a priori knowledge about the parameters other than a broad range which can be specified via appropriate constraints.
A New Framework for Detection and Identification of Network Parameter Errors
IEEE Transactions on Smart Grid
Y. Lin and A. Abur,
2018 Normalized Lagrange multiplier test has been shown to be very effective for network parameter error identification, but its validation has so far been solely based on extensive simulations. This paper presents a new framework by which: 1) the normalized Lagrange multiplier test is re-formulated from the perspective of hypothesis testing, enabling proper handling of missing bad parameter cases; 2) formal proofs are given for the combined utilization of normalized Lagrange multiplier test and normalized residual test for simultaneous handling of measurement and parameter errors; and 3) the concepts of detectability and identifiability for measurement errors are extended to parameter errors, and a systematic approach for identifying critical parameters and critical k-tuples is provided.
Linear Phasor Estimator Assisted Dynamic State Estimation
IEEE Transactions on Smart Grid
A. Rouhani and A. Abur
2018 Measurements provided by the phasor measurement units (PMUs) in a power network can be highly erroneous. Furthermore, some generating units may not have a local PMU, therefore it may not be possible to obtain high accurate and reliable results based on the previously studied dynamic state estimation approaches, which rely on the raw measurements provided by the PMUs. In order to address these issues, this paper presents a robust distributed dynamic state estimation approach that not only is robust against bad data, but also makes it possible to obtain the dynamic state estimation results for the generators without a local PMU. The proposed approach also accounts for expected delays in receiving estimated measurements by using a multi-step ahead state predictor to correct for delayed inputs. This procedure can also be useful for the short-term transient stability predictions.
Identifying security vulnerabilities of weakly detectable network parameter errors
2017 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton)
Y. Lin and A. Abur
2017 This paper is concerned about the security vulnerabilities in the implementation of the Congestion Revenue Rights (CRR) markets. Such problems may be due to the weakly detectable network model parameter errors which are commonly found in power systems. CRRs are financial tools for hedging the risk of congestion charges in power markets. The reimbursements received by CRR holders are determined by the congestion patterns and Locational Marginal Prices (LMPs) in the day-ahead markets, which heavily rely on the parameters in the network model. It is recently shown that detection of errors in certain network model parameters may be very difficult. This paper's primary goal is to illustrate the lack of market security due to such vulnerabilities, i.e. CRR market calculations can be manipulated by injecting parameter errors which are not likely to be detected. A case study using the IEEE 14-bus system will illustrate the feasibility of such undetectable manipulations. Several suggestions for preventing such cyber security issues are provided at the end of the paper.
Robust transformer tap estimation
2017 IEEE Manchester PowerTech, Manchester, 2017
Y. Lin and A. Abur
2017 The methods currently used for transformer tap estimation are not robust against measurement errors, while the well-documented Least Absolute Value (LAV) State Estimator (SE) is not robust against transformer tap errors. This paper addresses these shortcomings by introducing the so-called Sparse Extended Least Absolute Value (SELAV) SE. By strategically modifying the formulation of LAV SE, the “sparse” nature of l1optimization is exploited for the tap estimation problem. The transformer tap positions can be reliably estimated, while the simultaneously produced state estimates (bus voltage angles and magnitudes) remain robust against tap errors. Case studies done using IEEE 57-bus test system are provided to illustrate the effectiveness of the proposed approach.
Observability Analysis for Dynamic State Estimation of Synchronous Machines,"
IEEE Transactions on Power Systems
A. Rouhani and A. Abur
2017 This paper is concerned about the observability analysis of time-varying nonlinear dynamic model of a synchronous generator with its associated control systems. A byproduct of observability study is a set of guidelines to choose the appropriate set of measurements or sensors to be used to ensure strong observability for the dynamic states. The proposed analysis is developed using a Lie derivative based observability matrix and its singular values. A two-axis synchronous generator model and an associated IEEE-Type1 exciter are used to validate the results of observability analysis with dynamic simulations of disturbance scenarios using different types of measurements. It is shown that the dynamic state estimates will converge to the true trajectory faster and smoother if appropriate measurements that provide higher level of observability based on the proposed analysis are chosen. Results can be extended to other dynamic elements such as loads or time-varying parameters of machines.
Enhancing Network Parameter Error Detection and Correction via Multiple Measurement Scans
IEEE Transactions on Power Systems
Y. Lin and A. Abur
2017 Although the normalized Lagrange multiplier (NLM) method has been shown to be very effective for network parameter error identification, errors in parameters corresponding to insensitive NLMs still remain difficult to detect and correct. This paper proposes an enhanced method for detecting and correcting network parameter errors based on multiple measurement scans. The method is developed by first deriving the relationship between parameter errors and the associated Langrage multipliers in state estimation. This is then used to clarify the reason behind the sensitivity issue of NLMs and the improvements made by performing multiple scans. An approach for estimating the necessary number of scans for satisfying various detection requirements is also proposed. Moreover, a local parameter error correction procedure based on multiple scans is presented, with detailed discussion of the local network selection and the number of required measurement scans. Simulation results in a very large utility system in North America illustrate the effectiveness of the analysis and methods proposed in this paper.
Highly Efficient Implementation for Parameter Error Identification Method Exploiting Sparsity
IEEE Transactions on Power Systems
Y. Lin and A. Abur
2017 Accuracy of the network parameters has a strong influence on the results of power system state estimation. It has been shown earlier that normalized Lagrange multipliers can be used as a systematic way for identifying errors in network parameters. However, this approach carries a rather heavy computational burden limiting its practical utilization to small-size systems. In this paper, a computationally efficient algorithm is proposed to address this limitation. The idea is to derive and compute only the necessary subset of the gain matrix and covariance matrix, thus avoiding the computation and storage of large dense matrices. The proposed efficient procedure can be applied either to the single-scan or multiple-scan schemes with equal ease. Test results confirm that the improvements in computational speed and memory requirements brought by the proposed algorithm are quite remarkable. The proposed implementation of the normalized Lagrange multipliers method is tested using a large utility power system. The effectiveness and limitations of the single-scan scheme, and the improvements brought by incorporating multiple measurement scans, are discussed in detail.
Social