Simultaneous identi cation and correction of measurement and branch parameter errors (original) (raw)
Related papers
2011
Summary form only given. This paper proposes a three stages off-line approach to detect, identify and correct series and shunt branch parameter errors. In Stage 1 the branches suspected of having parameter errors are identified through an Identification Index (II). The II of a branch is the ratio between the number of measurements adjacent to that branch, whose normalized residuals are higher than a specified threshold value, and the total number of measurements adjacent to that branch. Using several measurement snapshots, in Stage 2 the suspicious parameters are estimated, in a simultaneous multiple-state-and-parameter estimation, via an augmented state and parameter estimator which increases the V-θ state vector for the inclusion of suspicious parameters. Stage 3 enables the validation of the estimation obtained in Stage 2, and is performed via a conventional Weighted Least Squares Estimator. Several simulation results (with IEEE bus systems) have demonstrated the reliability of the proposed approach to deal with single and multiple parameter errors in adjacent and non-adjacent branches, as well as in parallel transmission lines with series compensation. Finally the proposed approach is confirmed on tests performed on the Hydro-Québec Trans-Énergie network.
IET Generation, Transmission & Distribution, 2011
In this study, the innovation approach is used to estimate the measurement total error associated with power system state estimation. This is required because the power system equations are very much correlated with each other and as a consequence part of the measurements errors is masked. For that purpose an index, innovation index (II), which provides the quantity of new information a measurement contains is proposed. A critical measurement is the limit case of a measurement with low II, it has a zero II index and its error is totally masked. In other words, that measurement does not bring any innovation for the gross error test. Using the II of a measurement, the masked gross error by the state estimation is recovered; then the total gross error of that measurement is composed. Instead of the classical normalised measurement residual amplitude, the corresponding normalised composed measurement residual amplitude is used in the gross error detection and identification test, but with m degrees of freedom. The gross error processing turns out to be very simple to implement, requiring only few adaptations to the existing state estimation software. The IEEE-14 bus system is used to validate the proposed gross error detection and identification test.
IEEE Transactions on Power Systems, 2000
In this paper it is shown that once the measurements with gross errors are detected and identified, the geometrical approach to recover gross errors in power system state estimation, as we have previously proposed, is a convergent process. For the purpose of correcting the measurement gross errors, the measurement residuals are computed, and then the measurement gross errors are composed. For the detection and identification of the measurements with gross errors, the composed measurement error in the normalized form is used. The measurement magnitude corrections otherwise are performed using the composed normalized error . To support the thesis that, after the detection and identification of the measurements containing gross errors, the measurement correction is a convergent procedure, the generalization of the largest normalized error test is also provided. A two-bus power network is used to show in a didactic way the behavior of the gross error correction. The IEEE 14-bus system and the 45-bus equivalent of Brazil south are used to perform the tests for the multiple measurement gross errors case. Many gross error scenarios with different redundancy levels, for multiple gross error situations, have been tested. The measurement correction is made one at a time.
IEEE Transactions on Power Systems, 2000
In this paper a geometrical approach is described to detect, identify, and recover multiple gross errors in power system state estimation. Using the classical WLS estimator, the measurement residuals is computed, and then the error is composed. For the detection and identification of the measurements with gross errors, the composed measurement error in the normalized form is used. The measurement magnitude corrections otherwise are performed using the composed normalized measurement error . To give support to the detection and identification of the measurements containing gross errors, a generalization of the classical largest normalized residual test is provided.
Assessment and Enhancement of Power System State Estimation Quality
2005
The accuracy of the power system state estimation determines the usefulness of real-time power system operation and control applications. The quality of the state estimator results is judged by computing two classes of accuracy indexes namely, the post-estimation value of the ratio between the weighted least square (WLS) objective function and its corresponding threshold, as well as the ratios between the standard deviation of the estimated and measured quantities. The paper describes (a) an original method to compute the standard deviations of the estimated values (branch power transits and nodal power injections) even if the corresponding measured quantities are not available and (b) an original procedure for the detection of topology errors. Finally numerical simulations based on real-time measurement data are presented.
Efficient treatment of parameter errors in power system state estimation
Electric Power Systems Research, 1992
This paper presents efficient data structure management algorithms to reduce the amount of CPU time required during the recomputational process of updating the Jacobian matrix and mismatching vector for power system state estimation. This updating is required after the identification of bad data, such as a parameter error of a line in a power network, in order to remove their influence on the state estimation process. The performance of the proposed algorithms is evaluated using several large power system networks. A considerable reduction in the CPU time is obtained with the proposed algorithms.
A two steps procedure in state estimation gross error detection, identification, and correction
International Journal of Electrical Power & Energy Systems, 2015
In this paper a two steps approach for the power system state estimation is proposed. The first step is the gross error detection test when all the measurements are assumed as possible of having errors. With that assumption, a rule for the measurement's weights as being the inverse of a constant percentage of the measurement's magnitudes is proposed. Then, using the error as the objective function of the state estimation (SE) process to be minimized, the gross error analysis is performed. In case a gross error is detected, the Composed Measurement Error, in its normalized form (CME N ), is used to identify the measurement(s) with error(s). The measurement(s) with error(s) is corrected using the Composed Normalized Error (CNE). In the second step, the state estimation is again performed, but using as the weight for each measurement the inverse of the measurement's standard deviation as proposed in the classical estimators. In this step, the set of measures is the original set, but with the measures flagged with gross errors replaced by their estimated values. The IEEE-14, IEEE-57 bus systems and the 45-bus equivalent of the Southern part of Brazil are used to perform the tests.
2011 IEEE Trondheim PowerTech, 2011
In this paper, a topological and geometrical based approach is used to define an index, undetectability index (UI), which provides the distance of a measurement from the range space of the Jacobean matrix of the power system. The higher the value of this index, for a measurement, the closer it will be to the range space of that matrix, that is, the error in measurements with high UI is not reflected in their residuals, thus masking a possible gross error those measurements might have. Using the UI of a measurement, the possible gross error the state estimation process might mask is recovered; then the total gross error of that measurement is composed and used to the gross error detection and identification test. A two bus system is used to show how this geometrical view of gross error analysis works. The classical three bus system used in many papers of the state estimation field is used as an example of a small application of the proposed methodology.
Off-line validation of power network branch parameters
Iet Generation Transmission & Distribution, 2008
Straightforward, efficient, offline methods for dealing with suspicious power network branch parameters are proposed. State estimation (SE) is the natural tool for tackling the parameter estimation problem. The proposed methods explore the concept of irrelevant/barely relevant branches to eliminate/ mitigate temporarily the participation of suspicious parameters in the SE process, until the suspicions are remedied. Strategies are built to handle different situations, such as occurrence of single or multiple network parameter errors and redundancy reduction of available measurements. Suspicious parameter values can be corrected through procedures which interact with an existing conventional state estimator. This is achieved according to different strategies, depending on the situations previously described. Results with the IEEE-14 bus test system illustrate the application of the proposed methodology.
Power system state estimation: modeling error effects and impact on system operation
Proceedings of the 34th Annual Hawaii International Conference on System Sciences, 2001
State estimation has been introduced to power systems and implemented in the 60s, using a single frequency, balanced and symmetric power system model under steady state conditions. This implementation is still prevalent today. The single frequency, balanced and symmetric system assumptions have simplified the implementation but have generated practical problems. This paper examines these simplified assumptions and their impact on the state estimation performance. It provides a theoretical basis for the well known fact that the reliability of the state estimator algorithms has been below expectations. Specifically, sensitivity analysis methods are used to quantify the impact of modeling simplifications and measurement schemes on the performance of state estimation. The results clearly illustrate that the traditional state estimation algorithm is biased. These biases affect the accuracy of state estimation and its convergence characteristics. The paper also reviews the traditional state estimation approach against recent technological advances that have enabled synchronized measurements. The implications and possibilities of this new technology are discussed in this paper. Specifically, an example application of the new technology for a Three Phase State Estimator is described. A power system state estimation based on a) multiphase model, b) voltage and current waveform measurements, and c) synchronized measurements is formulated. The paper focuses on the following: a) modeling, b) implementation, c) observability and d) performance. The overall performance of the system is described in terms of confidence level versus error. These concepts are illustrated with simple systems. In addition, we demonstrate the performance of the proposed methods on an actual system (New York Power Authority system) using actual synchronized measurements. The paper concludes with a commentary on the implications of improved state estimation methods on the security/reliability monitoring and control of an electric power system.