Innovation concept for measurement gross error detection and identification in power system state estimation

Abstract

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.

Optimal Static State Estimation Using hybrid Particle Swarm-Differential Evolution Based Optimization

“…Bretas et al [9] has been adopted in this paper for its high efficiency. The idempotent matrix is formed as…”

“…As the presence of bad data is detected and indentified, the measurements should be recovered from errors. The corrected normalized measurement error is computed using the following equation as suggested in [9]. In a power system, a sudden large change of load may occur.…”

“…Thereafter, the volume of research works on SE has grown enormously and it has become a basic function in power system control centers (ECCs), specified by electric utilities as a mandatory requirement and supplied by all major control centers as a standard software product. Although the SE has become a mature, field-proven workhorse, various aspects of SE like the solution algorithm [3][4][5][6], detection and identification of bad data [7][8][9], topological error detection [10][11][12], observability analysis [13,14] continue to be explored so as to enrich the SE software used in ECCs. Conventional SE methods assume that the objective function related to SE is differentiable and continuous.…”

A two steps procedure in state estimation gross error detection, identification, and correction

“…More recently, using topological and geometrical approaches Bretas et al [10][11][12][13][14][15][16][17][18][19][20], have proposed methodologies that beyond having the classical SE functions it also composes the error and then correct the magnitude of the measurements in error.…”

Simultaneous identification and correction of measurement and branch parameter errors

“…In [2], a topological/geometrical based approach was used to define the undetectability index for bad data analysis and measurement error detection. To detect gross errors in the power system SE, a new method based on the definition of innovation index and concepts of [3] was proposed in [4]. Moreover, bad data detection was presented in [5] using a robust method for the solution of the power system SE with equality constraints.…”