Abstract—This paper proposes a fully distributed robust state-estimation (D-RBSE) method that is applicable to multi-area power systems with nonlinear measurements. Section II, power system state estimation model is briefly reviewed. Section III describes the robust bilinear state estimation (RBSE). A fully distributed algorithm to solve
Jul 28, 2018· State estimation for power systems was first formulated as a weighted least-squares problem by Schweppe [] in early 70s and has become an integral part of power system monitoring and operation .State estimation is a mathematical procedure to process the set of real-time measurements to come up with the best estimate of the current state of the system.
This article proposes a new distributed robust PSSE method for multiarea power systems. The non-Gaussian model is utilized to fit the measurement noise distribution to reach high model
Aug 15, 2015· A fully distributed robust bilinear state-estimation (D-RBSE) method that is applicable to multi-area power systems with nonlinear measurements and can compress bad measurements by introducing a robust state estimation model. This paper proposes a fully distributed robust bilinear state-estimation (D-RBSE) method that is applicable to multi-area
Jan 26, 2023· This paper summarizes a review of the distribution system state estimation (DSSE) methods, techniques, and their applications in power systems. In recent years, the implementation of a distributed generation has affected the behavior of the distribution networks. In order to improve the performance of the distribution networks, it is necessary to implement state
Apr 4, 2012· Request PDF | Distributed Robust Power System State Estimation | Deregulation of energy markets, penetration of renewables, advanced metering capabilities, and the urge for situational awareness
Apr 4, 2012· Distributed Robust Power System State Estimation all call for system-wide power system state estimation (PSSE). Implementing a centralized estimator though is practically infeasible due to the complexity scale of an interconnection, the communication bottleneck in real-time monitoring, regional disclosure policies, and reliability issues.
Apr 4, 2012· Deregulation of energy markets, penetration of renewables, advanced metering capabilities, and the urge for situational awareness, all call for system-wide power system
Jan 8, 2019· In the envisioned smart grid, high penetration of uncertain renewables, unpredictable participation of (industrial) customers, and purposeful manipulation of smart meter readings, all highlight the need for accurate, fast, and
and computationally efficient, making them robust to cyber-attacks on the grid and capable of scaling to large networks. We showcase the promise of GCNNs in distributed state estimation of power systems in numerical experiments on IEEE test cases. 1. Introduction Power system state estimation aims to recover the
Jan 16, 2019· In this paper, we propose an optimal robust state estimator using maximum likelihood optimization with the t-distribution noise model. In robust statistics literature, the t-distribution is used to model Gaussian and non-Gaussian statistics. The influence function, an analytical tool in robust statistics, is employed to obtain the solution to the resulting maximum
Jan 31, 2023· Robustness is an important performance index of power system state estimation, which is defined as the estimator''s capability to resist the interference. However, improving the robustness of state estimation often reduces the estimation accuracy. To solve this problem, this paper proposes a power system state estimation method for generalized M-estimation of
IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 28, NO. 2, MAY 2013 1617 Distributed Robust Power System State Estimation Vassilis Kekatos, Member, IEEE, and Georgios B. Giannakis, Fellow, IEEE Abstract—Deregulation of energy markets, penetration of renewables, advanced metering capabilities, and the urge for
Aug 14, 2015· A distributed bilinear state-estimation procedure is developed. In both linear stages, the state estimation problem in each area is solved locally, with minimal data exchange with its neighbors.
Distributed Robust Power System State Estimation Vassilis Kekatos, Member, IEEE, and Georgios B. Giannakis*, Fellow, IEEE Abstract—Deregulation of energy markets, penetration of renewables, advanced metering capabilities, and the urge for situational awareness, all call for system-wide power system state estimation (PSSE).
Jun 24, 2019· This paper presents a robust statistical approach to distributed power system state estimation (DPSSE) under bad data based on iterative reweight least squares (IRWLS) method and an improved alternating direction method of multipliers (ADMM) framework. In particular, the Hampel''s redescending and the Schweppe–Huber generalized M-estimators (SHGM) are
Jun 3, 2020· Abstract: The unscented Kalman filter (UKF) provides a powerful tool for power system forecasting-aided state estimation (FASE). However, when the power systems are affected by the abnormal operating situations, i.e., the non-Gaussian communication noises, sudden loads or state changes, and instrument failures, the original UKF based on the
Feb 19, 2024· For the power system state variables U and θ, the proposed DRSE significantly improves the accuracy, which proves that adding second-order cone constraints related to power system auxiliary state variables in the constraints can significantly improve the power system state variables estimation accuracy. For the heat system state variables, the
Dec 1, 2021· For power system state estimation, the measurement noise is usually assumed to follow the Gaussian distribution and the widely used estimator is the weighted least squares (WLS).
Oct 27, 2024· Due to its vulnerability to a variety of cyber attacks, research on cyber security for power systems has become especially crucial. In order to maintain the safe and stable operation of power systems, it is worthwhile to gain insight into the complex characteristics and behaviors of cyber attacks from the attacker''s perspective. The consensus-based distributed state
May 16, 2019· where is a given positive scalar parameter that bounds the model uncertainties; indicates the maximum iteration time; and are the true state vector and its estimation results, respectively; and represent the initial state vector and its covariance matrix, respectively; is the estimated covariance matrix; and are the respective covariance matrices of process noise and
Jun 23, 2021· The proposed distributed computational methods for power system state estimation are based on operator splitting. Our methods are computationally decomposable over sensor networks, so distributed
Jan 4, 2024· The forecasting of of pseudo-measurements play an important role in distribution system state estimation (DSSE). This paper proposes robust DSSE method based on forecasting-aided graphical learning method. The nodal power consumption models are first built to produce...
DOI: 10.1016/J.IJEPES.2021.107267 Corpus ID: 237656734; A distributed robust state estimation algorithm for power systems considering maximum exponential absolute value @article{Chen2021ADR, title={A distributed robust state estimation algorithm for power systems considering maximum exponential absolute value}, author={Tengpeng Chen and Tong Wu
Apr 4, 2012· Distributed Robust Power System State Estimation. Vassilis Kekatos, Georgios B. Giannakis. Deregulation of energy markets, penetration of renewables, advanced metering
Jul 31, 2023· For power system state estimation, the measurement noise is usually assumed to follow the Gaussian distribution and the widely used estimator is the weighted least squares (WLS).
TY - JOUR. T1 - Distributed robust power system state estimation. AU - Kekatos, Vassilis. AU - Giannakis, Georgios B. PY - 2013. Y1 - 2013. N2 - Deregulation of energy markets, penetration of renewables, advanced metering capabilities, and the urge for situational awareness, all call for system-wide power system state estimation (PSSE).
May 7, 2020· The proposed method is illustrated with two emerging applications respectively in robust distributed power system state estimation (DSSE) with nonlinear zero injection constraints and gene
In practical power system applications, the distribution of the measurement noise is sometimes unknown and deviates from the assumed Gaussian noise model due to measurement outliers. In such cases, the performances of the state estimators based on Gaussian noise assumption may deteriorate significantly. In this article, we propose a fully distributed and robust power system
Sep 1, 2023· The calculated result of calibrated electricity consumption, is a data for state estimation whose quality largely depends on the quality of consumption calibration. The calculated values by the calibration procedure are the initial values for calculating power flows for state estimation in the distribution power systems .
Jun 18, 2020· This study proposes a three-phase unbalanced distribution system state estimation which is robust against noisy distribution system measurements, bad data attacks and missing or delayed measurements. This method considers measurement from hybrid sources such as SCADA, micro-phasor measurement units (PMUs) and SMs. Kalman smoother is used to
Dec 1, 2023· This paper summarizes the technical activities of the Task Force on Power System Dynamic State and Parameter Estimation. This Task Force was established by the IEEE Working Group on State
May 7, 2020· In this article, we propose a fully distributed and robust power system state estimation approach based on the t-distribution noise model and the maximum likelihood
Jan 1, 2015· Without loss of generality, the global system is shown in Fig. 1 which consisted by two areas: a PMU observable area and an RTU observable area. These two areas are connected by tie lines, and the terminal buses of the tie lines are boundary buses with state variables denoted by x B.The boundary bus set B can be subdivided into the bus sets BL and BN,
This paper proposes a fully distributed robust bilinear state-estimation (D-RBSE) method that is applicable to multi-area power systems with nonlinear measurements. We extend the recently introduced bilinear formulation of state estimation problems to a robust model. A distributed bilinear state-estimation procedure is developed. In both linear stages, the state estimation
The distribution of measurement noise applied in practical power system state estimation (PSSE) can deviate from the assumed Gaussian model, and the performance of an estimator becomes bad if the Gaussian model is still used. This article proposes a new distributed robust PSSE method for multiarea power systems. The non-Gaussian model is utilized to fit the
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