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.
The obtained result is confirmed by the 25MVA/220 kV transformer dismantling at the manufacturing plant, when untwisting the regulating winding (RW) of transformer was discovered. Therefore, the proposed method is more reliable and can be recommending for the introduction on other short-circuit testing laboratories and in the operation in the power systems during the measurement of short-circuit inductance or impedance [by 3–4].
In addition to examined method, which makes it possible to obtain significant deviations of ΔХs-c with the aid of the correct calculation of Δconf, it follows to add that in the case of obtaining the insignificant deviations (as in Figure 6) from the first short-circuit shot to the second short-circuit shot and from second to the third short-circuit shot it is possible to consider significant deviation ΔХs-c = +0,17 % (0,22 % – 0,05 % = 0,17 %) from first to the third final short-circuit shot.
In addition to this, in the case of the intersection of the zones of confidence intervals Δconf between the first (ΔХs-c = +0,05 %) and the second short-circuit shot (ΔХs-c = +0,16 %) at point +0,11 % it is possible to consider this as one significant deviation ΔХs-c = +0,11 % with the confidence interval Δconf =
From Figure 5 follow that zones of confidence interval Δconf of measurement short-circuit inductance Хs-c of the adjacent on the time short-circuit shot (for example, 2-nd short-circuit shot and 3-d short-circuit shot) can intersect between themselves: ΔХs-c2 = +0,16 % (Δconf2 =
This “imposition” of measurement confidence interval is inadmissible, since in certain cases this hampers the estimation of winding condition state of transformer: if this deviation ΔХs-c insignificantly, i.e. it is connected with a measurement error, then of changes in the windings does not occur; but if it significantly, i.e. it corresponds to the development of residual deformations in the windings, then it must be considered for evaluating the winding condition state in order not to bring it to the destruction [5–14].
Example. During the short-circuit testing of two accordingly switch reactors of the type ROST-700 in the course of measurements by ADC of short-circuit inductance there was identified “Chapeau” type distribution.
The value of resulting of measurement root-mean-square deviation comprised:
The obtained value Δconf = 0,052 % is lower than stipulated level of error +-0,1 %, which confirms the high accuracy of the determination of short-circuit inductance deviation in the proposed device – Smart Grid Monitoring System [by 1–9, 15–26].
1.9. Conclusion
The most important element of “intellectual of networks” (Smart Grid) are the systems of monitoring the parameters of electrical of equipment.
Smart Grid Monitoring System, which described in this chapter, were proposed to use together with quick-working protection against short-circuit regimes in transformer windings.
At the beginning of winding deformations, and also in the case of winding turn-to-turn internal short-circuit the value of inductance L is developed to increase, or to decrease.
Smart Grid Monitoring System and connected with it protection block were stopped the process of winding destruction.
Short-circuit inductance measurements by ADC there was identified “Chapeau” type distribution of random error.
The determination of confidence interval of measurement random error of short-circuit inductance deviation ΔХs-c by algorithm (1.12–1.26) makes to increase the accuracy of the conducted measurements and the reliability of Smart Grid Monitoring System’s work for the control of short-circuit inductance of power transformers and the reactors.
Smart Grid Monitoring System makes it possible to continuously control the state of power transformer windings without their turning off from the network, to achieve their protection in the case of the appearance of the winding deformations or their damage, and it ensures the high accuracy of the inductance measurement with the confidence interval value of the random error less than +0,1 %. This increases the reliability of operation and the continuity of the power supply of the electrical energy users.
Active Vocabulary of electrotechnical terms and verbs:
Information-measuring system – информационно-измерительная система,
monitoring system – система мониторинга,
Smart Grid – интеллектуальная («умная») сеть,
inductance – индуктивность,
autotransformer – автотрансформатор,
short-circuit – короткое замыкание,
short-circuit testing – испытание на стойкость при коротком замыкании,
accuracy – точность,
accuracy of diagnostic parameters – точность (измерений) диагностических параметров,
confidence interval of measurement – доверительный интервал измерений,
Frequency Response Analysis – (метод) частотного анализа,
transformer winding fault diagnostic – диагностика повреждений обмоток трансформатора,
Low Voltage Impulse method – метод низковольтных импульсов,
short-circuit inductive reactance measurement – измерение индуктивного сопротивления короткого замыкания,
voltage transformer (TV) – трансформатор напряжения,
current transformer (CT) – трансформатор тока;
high-voltage circuit breaker (HVCB) – высокольтный выключатель,
dissolved gas analysis (DGA) –
