The bad data identification of SE is the detection and identification of measurements of a power system telemetry snapshot that deviate from the power system security dispatching error margin. Its identification mechanism is based on the power system power flow equations that these measurements should satisfy.
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Based on LSR test, a comprehensive strategy is developed for detection and identification of multiple gross errors which may exist simultaneously in the data. A six-bus power system data is used for the application of LSR test for detecting and identifying the
It is important to notice that, the discrepancy between the values obtained with and without the presence of bad data is remarkable, being an useful parameter to be used for bad data detection. It means that, if no bad data is suspected in the set of measurements, the objective function tends to assume a very low value.
Chi-squares ($Chi^2$) analysis is a bad data detection method. If bad data are detected, these still need to be identified. The method is based on the following assumptions: if all measurement errors follow a Normal distribution, and there are no bad data, then the sum of the weighted squared residuals follows a Chi-square distributions with m
III. BAD DATA DETECTION AND IDENTIFICATION A. Detection Due to the problem definition, assuming the measurement residuals as approximately Gaussian distributed and in absence of bad data, the
where α = (α 1, α 2, , α m) T should be highlighted that, this is for the first time that the effect of topology uncertainties M i f and p = 1 norm are explicitly included in the SDP-based problem formulations. This means that
sidered for estimating system states in regular time intervals and also to track daily load profiles based on an appropriate optimization model with non linear constraints. Bad data detection is performed based on the comparison of the objective function with a threshold value determined by Monte Carlo. Once it is detected, bad data
The reliability of results generated by state estimation are heavily impacted by measurement errors, topology errors, system transients and bad data injections. Detection and identification of such errors in a power system is important not only to monitor instrumentation performance but also to ensure system resilience. Despite their extensive application in power system state
The effectiveness of the proposed LNERT-based bad data analysis is extensively compared with respect to other proposals for bad data identification by means of state estimation studies performed in the IEEE 14-bus and 118-bus test systems as well as on the very large 7659-bus representation of the Mexican interconnected power system.
Index Terms—Bad data detection, conventional measurements, linear estimation, phasor measurement units, weighted least square I. INTRODUCTION State Estimation (SE) is a mathematical algorithm that has a crucial role in power system monitoring. SE processes raw measurement data collected from measurement devices
Li, Z.: Identification of Bad Data of Electric Power System State Estimation. Journal of Qinghai University, Natural Science Edition 19(1), 49–51 (2001) Google Scholar Liu, H., Cui, W.: The Detection and Identification Method of Bad Data Combined RN Detection and State Forecast. Proceedings of the EPSA 13(2), 39–43 (2001)
This paper solves this problem based on a proposed artificial neural network (ANN) scheme. The state estimation/bad data detection and identification (SE/BDDI) process is conducted via a
IEEE Trans Power Syst 34 (6):4910–4920 Cheng G, Song S, Lin Y, Huang Q, Lin X, Wang F (2019) Enhanced state estimation and bad data identification in active power distribution networks using photovoltaic power forecasting. Electric Power Syst Res 177:105974
This study is motivated by major needs for accurate bad data detection and topology identification in the emerging electric energy systems. Due to the non-convex problem formulation, past methods usually reach a local optimum.
This paper presents a novel approach for bad data correction in state estimation (SE) for three-phase distribution systems. Based on an optimization model, a SE technique is presented considering branch currents as state variables to be estimated in regular time intervals. In this work, the presence of gross errors is detected by a comparative analysis
This paper presents an efficient method for power system static state estimation along with a statistical technique of bad data detection and identification. In the estimation process, the exponential function is utilized to modify the variances of measurements in anticipation of maintaining the estimation performance under the bad data scenario. Besides,
This study addresses state estimation in three-phase distribution systems, and proposes a new method for bad area detection (BAD) and identification of bad data, via decoupled Chi-squares test with newly proposed metrics.
where α = (α 1, α 2, , α m) T should be highlighted that, this is for the first time that the effect of topology uncertainties M i f and p = 1 norm are explicitly included in the SDP-based problem formulations. This means that aside from state estimation and optimal power flow, for the first time, topology identification and bad data identification with p = 1 can be
A new approach for detection and identification of multiple bad data in power system data estimation IEEE Trans. Power Appar. & Syst., Vol PAS-101 ( No 2) ( 1982), pp. 454 - 462 An application of estimation-identification approach of multiple bad data in power system state estimation IEEE Trans. Power Appar.
Foundational and state-of-the-art anomaly-detection methods through power system state estimation are reviewed. Traditional components for bad data detection, such as chi-square testing, residual-based methods, and hypothesis testing, are discussed to explain the motivations for recent anomaly-detection methods given the increasing complexity of power
The State Estimation (SE) problem in electric power systems consists of three main functions: estimation, bad data detection and identification. D''Antona formalized the estimation procedure considering the contribution of both the measurement and the network
Bad Data Analysis for Power System State Estimation. April 1975; The paper interrelates various detection and identification methods (sum of squared residuals, weighted and normalized
From the survey it can be concluded that there are still areas in the developing DSE that can still be improved in terms of system computational time, redundancy and robustness of the system. State Estimation (SE) is the main function of power system where Energy Management System (EMS) is obliged to estimate the available states. Power system is a
Abstract. State Estimation (SE) is the main function of power system where Energy Management System (EMS) is obliged to estimate the available states. Power system is a quasi-static
Most of the authors mentioned in this paper assumed that only one type of bad data source i.e. parameters or topological or bad measurement data errors is present at any one time and presented their solution for that case. However, in reality, the sources of errors are mixed and thus complicate enormously the bad data problem.
This paper presents a new approach for detection and identification of ultiple bad data --- estimation-identification approach which has been developed in order to improve the performance of static-state estimators of power systems. It describes the important function of detection and identification of bad data which is of crucial importance in designing a static-state estimator as
residual test is used to detect bad data. To validate the accuracy and robustness of the proposed algorithm, several test cases of different sizes are solved and the results are presented and
Bad Data Detection in State Estimation of Power System: The ability to identify Bad Data Detection in State Estimation measurements is extremely valuable to a load dispatch centre. One or more of the data may be affected by malfunctioning of either the measuring instruments or the data transmission system or,both. Identification of Bad
The basic concepts and mathematical models of the power system state estimation along with bad data detection, identification and elimination method are described briefly.Study of the various
Recently proposed hybrid power system state estimators allow the imbedding of synchronized phasor measurements into the estimation process by using a two-stage architecture. Such a scheme preserves existing SCADA-based estimators and enhances computational efficiency by processing phasor data through a linear second-stage estimator that exhibits superior
The issue of bad data detection and identification can be incorporated into the SE algorithm or implemented as a preprocessing or post-processing process. In power systems, state estimation is
The paper interrelates various detection and identification methods (sum of squared residuals, weighted and normalized residuals, nonquadratic criteria) and presents new results on bad
ELEOTRIC POlaR s E L S E V I E R Electric Power Systems Research 34 (1995) 127-134.. A neural network preestimation filter for bad-data detection and identification in power system state estimation H. Salehfar, R. Zhao Department of Electrical and Computer Engineering, Clarkson University, Potsdam, NY 13699-5720, USA Received 7 February 1995 Abstract
DOI: 10.1016/0378-7796(95)00966-7 Corpus ID: 109919195; A neural network preestimation filter for bad-data detection and identification in power system state estimation @article{Salehfar1995ANN, title={A neural network preestimation filter for bad-data detection and identification in power system state estimation}, author={Hossein Salehfar and Rui Zhao},
The results show how single and multiple bad data are cleaned from raw measurements. The correct data can be introduced then in the state estimation algorithm allowing a more efficient
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