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.

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In this paper it is described a geometrical approach 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 for (CMEN) is used. The measurement magnitude corrections otherwise are performed using the composed normalized measurement error (CNE). To give support to the detection and identification of the measurements containing gross errors, a version of the largest normalized composed error test is provided. The IEEE-14 bus system as well as the reduced 45-bus Power System of the Brazil South is used to test the multiple gross error detection, identification and correction efficiency.

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