are determined.

13) The value of quantile coefficient for the concrete identified distribution is calculated:

Table 1.1.

14) The error in the determination of root-mean-square deviation (RMSD) of random error distribution is calculated:

(1.24)

where:

is the kurtosis of distribution;

n is the number of measurements.

15) The value of the confidence interval of a random error of measurement of short-circuit transformer inductance is determined:

(1.25)

where: t is the quantile coefficient;

is the measurement’s root-mean-square deviation (RMSD) of X50 value.

16) Obtained result of measuring the deviation of short-circuit transformer inductance is derived to the printing in the following form:

(1.26)

where: ΔX50 is the deviation of X50 value from base value of short-circuit transformer inductance Х0;

Δconf is the value of the confidence interval of a random error of measurement of short-circuit transformer inductance from (1.25).

In the case of the appearance of residual deformations in the windings of transformer-reactor electrical equipment (TREE) comes a gradual increase in the value of short-circuit transformer inductance.

The criterion of the evaluation of the threshold quantity of the deviation of short-circuit inductance, which corresponds to the beginning of the appearance of deformation, is value (ΔХs-c = +0,2–0,3 % with the confidence interval (accuracy) of the measurements (Δconf = 0,1 %). Value ΔХs-c = +1 % corresponds to the sufficiently serious deformations of the transformer windings [by 1–4].

The given procedure of the determination of the confidence interval Δconf (1.12–1.25) for the measurements of Хs-c can be used also in the case of calculation Δconf for the deviations ΔХs-c in the course of transformer testing for withstand to short-circuit current. The value of Δconf for the deviations ΔХs-c, determined on (1.26), does not exceed the value of Δconf for ΔХs-c, since utilized in (1.13–1.15) Xaverageand X0 are calculated from the samples n of the uniform the equal-point values xi, which have one and the same law of random error distribution in the type “Chapeau”.

Let us illustrate this based on the example of a change in the significance of a deviation of short-circuit inductance ΔХs-c from one shot to the next during the 25MVA/220 kV transformer testing for withstand to short-circuit currents (Figure 6).

Figure 6. Example of a change in short-circuit inductance and the estimation of the significance of deviations Хs-c with the aid of the confidence interval of measurements Δconf during the 25MVA/220 kV transformer testing.

Advantage of the proposed in this chapter method one can see well in the case of changing Хs-c in the third, and then in the fourth final shot from +0,22 % to 0,34 %, when the value of confidence interval with the normal distribution Δconf =

(no shaded rectangles in Figure 6) the significance of the obtained deviations does not give to estimate, since confidence intervals Δconf of third and fourth shots are overlapped. This can lead to the false conclusion that change ΔХs-c = +0,12 % from the third to the fourth shot insignificant and is connected only with the influence of measurement error.

The procedure of determination of Δconf, which presented in (1.13–1.26), allows to obtain the significant deviation of ΔХs-c with its change from the third short-circuit shot to the fourth short-circuit shot, having Δconf = 0,05 % for “Chapeau” distribution.