Monitoring Power Transformer Performance, Usage

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Monitoring Power Transformer Performance, Usage and System Event Impacts – A Case Study Subrat Sahoo, Tord Bengtsson, Nilanga Abeywickrama, Robert Saers, Jonas Hedberg

Johanna Rosenlind Mälarenergi Elnät AB, Västerås, Sweden

ABB Corporate Research, Västerås, Sweden A number of online and offline monitoring techniques have historically been employed for monitoring different components within a transformer and also to understand the performance indicators on historical basis. A comprehensive review of different techniques is presented in [2,3]. The insulation serves as the weakest link for the transformer life. Optimized insulation models are developed by adopting expert systems that takes into account measurement data from traditional and modern techniques [4]. The non-invasive methods using terminal measurements, lumped parameter or 3D finite element model help understanding permanent shifts in winding properties [5,6], using frequency response analysis.

Abstract— This paper investigates the condition of a three phase, 132/30 kV, 50MVA transformer, in a Swedish Utility site. Transformer Explorer, as an online fault detection tool was used for this purpose by installing data acquisition accessories in the substation control room and tapping the signals from the metering and protection cabinets. The fundamental quantities of the transformer, such as turns ratio, impedance and power loss were calculated from the measured voltage and current phasors on either side. The transformer was further found to have generation sources on the 30 kV side. An increased power loss was observed during the period, when the secondary side generation existed. The paper presents a summary of status and operational condition of the transformer based on fundamental quantities and deviations thereof. The findings will be discussed and compared in accordance with the factory acceptance test results. The paper will also demonstrate the usage of Transformer Explorer during different scenarios, namelynormal monitoring, identifying special system conditions modes and post-event analysis and the possible conclusion made from this analysis.

On-line monitoring of traditional parameters (e.g., gas, moisture, temperature, etc.) and advanced methods like vibration analysis have become more popular and gained acceptance in the recent past [1,4] due to their ability to diagnose failure conditions in real time. Such techniques become more favorable, when the proposed concepts do not seek for additional sensor installation for understanding the behavior of the important characteristics of the transformer. Transformer Explorer concept is devised as one such online monitoring solution that relies on voltage and current signals available in a substation control room [7]. Furthermore, it is capable of detecting faults associated with active parts of the transformer, such as winding, core and tap changers, which attribute to more than half of the transformer failures worldwide, as reported in CIGRE [8]. The concept can be factory fitted and is also possible to retrofit on the existing transformers, without seeking an outage for the installation. A schematic illustration is given in Fig. 1.

Keywords— Transformer Explorer, Impedance, turns ratio, voltage drop, on-line monitoring, tap position, power loss, system events.

I. INTRODUCTION The power grids around the world is undergoing rapid modernization, to face the demand for increased reliability and availability, as well as stringent grid code compliance. The inclusion of distributed energy sources at different voltage levels poses challenges to grid owners, to be able to accommodate and operate stably both during normal operations and fault events. The latter requires an isolation with fault-ride through capability. Microgrids in isolated and grid connected mode are also dictating new norms in mature and emerging markets. Such operations need additional research, not only for protection functionality, but also for continuous monitoring and health assessment on a periodic and proactive manner.

II. FUNCTIONALITY OF TRANSFORMER EXPLORER Transformer Explorer performs estimation of turns ratio, impedance and power loss [9] by fitting measured data to a simple transformer model, represented by the equivalent circuit, shown in Fig. 2. With help of linear regression, the model estimation updates, whenever enough new data is available. The vector group transformation, data quality checks and zero sequence removal form the inherent tasks before the datasets on either side of the transformer are available for analysis. The data could be queried in real time from the acquisition card, or could be stored in a database for offline analysis. Provisions are also made to deal with phasors directly obtainable from the IEDs or digitized waveforms in the form of Comtrade files. The data needs to be further classified based on their tap position, power direction so that correct fits are

The advent of improved monitoring solutions and standardized communication technology is redefining the maintenance parameters of the power system assets. The transformer is however still the single most expensive equipment in a power system, thus requiring fair attention from operators for its healthy operation and upkeep [1]. Its unavailability during unplanned outages is best avoided by being knowledgeable about the condition of the transformer proactively and applying condition-based maintenance as desired.

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between the source and the load side. The voltages are referred to the same side of the ideal transformer. The voltage drop, ΔV , represented as below in Eq. (2)

grouped together. The linear regression fit is applied to the fundamental quantities, governed by the straight line equation: y=mx+c, where x and y are obtained from measured data, m and c are computed as slope and intercept respectively. These parameters, also obtainable from the nameplate data, decide the change in values to classify as impending failure situations.

(2) ΔV = V1 − nV2 ΔV , can be fitted against the load current using the

transformer model, as presented in the following equation.

ΔV = Z W I 1 + Z 1 I 0

(3)

Where V1 and V2 are the respective complex quantity terminal voltages which are fitted with the load current, I1. Zw is the summed winding impedance of both windings, seen from one side, Z1, the primary winding impedance, I0, the magnetizing current. Zw is the slope of the expression (3) in this fit, whose imaginary part represents the reactance of the winding that can also be obtained from the nameplate as a percentage value. The real part of Zw represents the winding resistance and contact resistance. Z1Io is the intercept of Eq. (3), which does not carry so much of relevance and will not be discussed in this paper.

Fig. 1. Schematic illustration of Transformer Explorer adoptability: A semipermanent Transformer Explorer installation (shown to the left), other popular monitoring methods requiring direct access to the transformer are indicated.

C. Power loss The power loss in the transformer can be calculated as the difference of power in and out of the transformer. In its simplest form, this complex quantity can be represented by Eq. (4), taking cue from the simple model in Fig. 2.

S = V1 I1 * −V2 I 2 *

(4)

where * indicates complex conjugation. Fig. 2. Simple two winding representation of a transformer model with turns ratio n, measured quantities: V1, V2, I1, I2.

A similar estimation fit as Eq. (1) and (3) can also be made for the power loss but it provides the same information as obtained from the ratio and impedance estimates, albeit with more noise, due to involvement of both current and voltage sensors. Nevertheless, studies of the power losses can visualize important information as will be apparent below.

Transformer Explorer uses the load current to separate the magnetic circuit from the power flow. Thus the fit is decided after enough load variations have occurred. The least square regression is applied for the complex x and y parameters to estimate m and c. The respective fundamental quantities estimation is elaborated in the subsequent sections below.

III. TRANSFORMER EXPLORER FIELD INSTALLATION The installation procedure of Transformer Explorer does not require the transformer to be taken out of operation. Therefore, it requires high level of safety observation to ensure the safety of the installation personnel against direct contact of live low voltage signals. A risk assessment was carried out for the installation procedure, signal acquisition methods, power and operation of the acquisition system and sustainability of the data collection process.

A. Turns ratio and magnetization current To find the transformation ratio, the current relation from Fig. 2 is used, as the voltage ratio (standard offline method) is dependent on the winding impedance .

I1 = I 2 / n + I 0

(1)

The source side current is fitted as a linear function of load side current while taking into cognizance of power direction. Accordingly, the magnetizing current I0 is determined. Eq. (1) is a linear equation, where both the slope (1/n) and intercept (I0) are complex numbers. The complex value of the slope could have a small imaginary part that can be attributed to sensor inaccuracies between the current transformers (CTs). The no-load component of the current, contributing to the core magnetization or any other stationary losses is represented by the intercept, termed as the magnetization current.

The installation of Transformer Explorer at the site implies a semi-permanent installation of the data acquisition system that collects the voltage and current signals from the instrument transformers installed both on primary and secondary side. The data acquisition system is installed inside the control room at a substation facility, where the analog signals arrive from the primary equipment to the relays and intelligent electronic devices (IEDs). The setup consists of the mechanism of collecting current and voltage signals through suitable current and voltage division, a data acquisition system that facilitates consolidation of the signal to a compatible software and finally a computer that processes the data and runs a set of algorithms to decide the status of the transformer and store notable events.

B. Impedance The winding reactance and resistance can be calculated from the voltage drop with appropriation of the impedance



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Fig. 3 shows a photograph of the connections made for current and voltage acquisition in one of the protection unit.

A. Estimation of turns ratio For the above mentioned loading condition, the transformer mainly operated in three tap positions, namely Tap 8, 7 and 6, for which reference fits were obtained. In addition to the analysis for each tap position, the system also keeps track of the number of operations seen and the accumulated time spent on each position as this may be valuable information. As a ratio fit example, the results for n and I0 at tap 8 is presented in Table I. TABLE I. RATIO FIT RESULTS FOR TAP 8 Phase

Fig. 3. Location of (left) voltage and (right) current sensors in different cabinets inside the control room.

IV. DATA ANALYSIS OBTAINED THROUGH ACQUISITION Fig. 4 below represents the normalized value of current in both windings superposed together in per unit (pu) scale. Some observations from the load profile during the entire observed period, Fig. 4, worth mentioning are:

No load Current (A) Absolute

Real

Imag

Phase-A

4.139

0.0057

0.12

Phase-B

4.136

0.0083

0.12

Phase-C

4.150

0.0043

0.12

Average

4.142

0.0061

0.12

Nominal Ratio = 4.134

Table I represents the obtained values of slope and intercept for the “Ratio” (referred in Eq. (1)) that contains n and I0 respectively. The values are complex numbers and represented for the three phases, with the nominal value for the ratio given in an additional row (as 4.134). The real parts carry the actual value of the ratio, whereas the imaginary part is a manifestation of sensor inaccuracies and is therefore an accuracy indicator. It can thus be concluded that the estimated ratios are consistent with the nominal one within the sensor accuracy limits. A ratio change would most likely indicate a turn-to-turn fault which is a very serious condition.

Fig. 4. load profile of both HV and MV winding current during the studied period.

B. Estimation of magnetization current The intercept of the ratio for Tap 8 describes the absolute value of the magnetization current and is obtained in Table I for the different phases. The thumb rule suggests the magnetizing component should be below 1% of the nominal current. The magnetization current estimated by the Transformer Explorer is in the range of the values obtained through the factory acceptance test (FAT), which is around 0.15A. A change of the magnetizing or no-load current can indicate, changes in the core or result from eddy currents anywhere in the transformer tank.

• A drop in overall load was observed between 11th July to 10th August, almost for a month and again from start to mid of September, which could be attributed to lower domestic and industrial consumption during summer. • Between 20th to 25th July, the current dropped to a significantly low level, even a small reverse power flow, upstream, is recorded (marked as 1 in Fig. 4). Similar drop in current was also observed between 30th August and 13th September (marked as 2). This part will be discussed in more detail later on.

C. Estimation of winding impedance The real and imaginary part of the winding impedance as narrated in Section II.B are computed for Tap position 8 for the three phases and is compared against the nominal imaginary amplitude (reactance) in Table II. Any cognizable difference can be reported as change in the winding reactance due to buckling, axial displacement, etc. In this case, no significant deviations are detected. In particular the three phases have almost identical values, which is less likely, in case a deformed winding is involved.

Typical waveform spectra is presented in Fig. 5 and it can be seen that the hamonic distortion is moderate, around 1%. In addition to the power frequency and its harmonics, frequencies arising from the Swedish railway system, running at 16.7 Hz, are also visible. Secondary voltage

Ratio

- Phase-1 - Phase-2 - Phase-3

Fig. 5. Representative voltage spectra at moderate load of ~0.4 pu.



UG,QWHUQDWLRQDO&RQIHUHQFHRQ&RQGLWLRQ$VVHVVPHQW7HFKQLTXHVLQ(OHFWULFDO6\VWHPV&$7&21 TABLE II. IMPEDANCE FIT RESULTS FOR TAP 8 Phase

Impedance (Ÿ) Resistance

Reactance

Phase-A

1.253

38.15

Phase-B

1.361

38.25

Phase-C

1.35

38.07

Average

1.321

38.15

Nominal Impedance = 37.59 j Ÿ

Fig. 7. Real part of power loss plotted against secondary winding current in per-unit scale.

D. Estimation of power loss The power loss is plotted in Fig. 6 for the studied time span to demonstrate the higher losses during specific time intervals, as encircled. These higher losses (4 times higher than the magnitude at other times) could not be attributed to higher load currents, as can be seen in Fig. 7.

Fig. 8. Real part of the voltage drop plotted against the secondary winding current, presented in per-unit scale.

Fig. 6. Real part of power loss in each phase during the studied period.

An unusual behavior is noticed, when the current was relatively low (say below 0.13 pu) in Fig. 7. Strangely enough, the power loss component still increases and this defies the normal norms of increased loss against increasing current, such as for currents above 0.15 pu. This statement is not equally true for all the three phases either, since the green phase still sees a lower power loss with increased current (above 0.25 pu). Such anomalies can be attributed to sensor balance errors less than 0.1 %. Such errors can however not explain the fact that there are two occasions (20th – 24th July and 4th –15th September), where power loss in excess of 15 – 20 kW is observed while lower losses are seen at the same load at other occasions.

Fig. 9. Real part of current difference between primary and secondary winding plotted in pu scale. The data points belonging to the higher voltage drop cluster are marked in blue.

This set of data needs further investigation before concluding on the reason for this abnormal power loss increase. It is however interesting to note that the highlighted periods in Fig. 9 corresponds within the hour to the times when hydro power was produced on the secondary side as shown in Fig. 10.

E. Estimation of voltage drop The voltage drop analysis attracts attention since, as can be seen in Fig. 8, the real part of the drop clearly consist of two clusters. The cluster of voltage drops with higher values can be associated with the higher power loss, characterized in Fig. 6. This set of data was indexed and highlighted. The real part of the current difference between the primary and secondary winding current after normalization is plotted in Fig. 9 with the data points belonging to the higher voltage drop cluster marked. Thus it is confirmed that the higher voltage drops are associated with the higher loss occasions.

V. DIAGNOSIS OF SYSTEM RELATED EVENTS Some exceptional system events were recorded from the transformer at three different occasions, lasting between 0.35s (roughly 18 cycles) and 1.7 s (~ 90 cycles) during the observation period. Three phases of the transformer experienced power frequency overcurrent of one order higher magnitude than nominal. However, it was seen that the three phases did not participate at the same time that might cause a momentary unbalance leading to a huge circulating current and hence temperature rise. The waveforms were captured roughly for 10s duration with a 10 kHz sampling frequency, following a trigger based on the magnitude variation, which was way above the standard deviation of the normal operation. One of these three incidents, occurred on 17th August is reported below in Fig. 11.



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Fig. 10. Secondary side hydro power production in the studied period. Fig. 12. Reactance estimates for tap 7 around the overcurrent event 17th August 2016, 12.52, marked with yellow line. No significant change of fits before and after can be detected.

The event is registered between point A and C in Fig. 11, where the current suddenly jumps by 10x magnitude for ph-A and ph-C at 2.9s in the time axis and the third phase joins them after some 18 cycles (point B). The entire phenomenon lasted for 1.7s. When the fault was cleared, the current magnitudes then dropped to a lower value than prior to the fault (600A to 400A) at 4.6s in the time axis.

Fig. 13. Difference of measurements to ratio (bottom) reference fit predictions around the event 2016-08-17, 12.52. Fig. 11. Current magnitude of secondary winding, during the over current in all the three phases recorded on 17th August 2016, 12.52.

VI. DISCUSSION

An excessive stress like this could impact the integrity of the winding and it is worth investigating if it has affected the transformer performance. A winding inductance change is the most probable effect following such an event. Fig. 12 demonstrates the individual inductance estimates from one week before and one week after the August 17th event for tap 7, which is the tap mainly used during this period. No significant deviation of the inductance estimates before and after the event can be seen in this plot. In particular, all three phases deviate from the reference value in a similar way, which would not be the case if one of the phase windings had been deformed. The ratio and no-load current estimates were also evaluated without any noticeable deviation from the estimate prior to the event.

This section discusses two important aspects while monitoring the transformer parameters with help of Transformer Explorer, namely the sensitivity and accuracy of the sensors used as well as the ability of Transformer explorer to aggregate the usable data from different sources. The unphysical dependence of power loss on load in Fig. 7 can serve as a motivation for a detailed discussion on measurement accuracy. While using the existing primary CTs and VTs is convenient, it has the disadvantage of over-relying on their accuracy. Typically the protection CTs used have an accuracy class of 1 % or worse while VTs often are specified with an accuracy of 0.1 %. Ratings of CTs and VTs and their accuracy classes have been standardized by IEEE [11] and IEC [12, 13].

Another tool available in Transformer Explorer is the difference of every incoming measurement to the predicted value by the reference fit. If the difference after some specific time consistently attains a higher value, this indicates a change in transformer properties. While the impedance fit difference remains largely unchanged, a slight change is observed in the ratio fit difference as shown in Fig. 13, which is about 0.2A corresponding to 0.1 % of nominal HV current or 20 % of the no-load current.

With only 1 % accuracy it would seem impossible to obtain the results depicted in Fig. 7 where the largest loss values are only 0.15 %. The reason why this is apparently possible is because sensor errors are a combination of systematic and stochastic deviations from the true value and as per the authors’ experience, the systematic errors in CTs and VTs are usually one order of magnitude larger than their stochastic counterpart. Thus, while the absolute value of a measured or deduced quantity will have an uncertainty dependent on the accuracy of the involved sensors, much smaller changes may be significant and dependable. Therefore adequate attention should be paid to distinguish between absolute accuracy and change detection sensitivity, while dealing with sensitive measurements like the ones presented here.

The ratio fit difference shows no correlation with load and can thus be attributed to the no-load current; presumably small changes have occurred in the core or eddy current loops have been created elsewhere. This change is so small that it is not visible in the power loss and should therefore not imply any immediate threat to the transformer. Thus it can be concluded that only minute changes of the transformer properties can be observed due to this event.



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d. Some momentary overcurrents were observed without tripping the transformer. The incidents reported in Section V are in accordance with the operations expected by the relay protection to disconnect the faulty feeder. However, the power frequency overcurrent transients of such high magnitude could be detrimental to the transformer integrity. These events did not generally seem to alter the crucial transformer properties as was checked by inspecting the estimates before and after the event. A minor change of the no-load current was however observed after one event. The detected events were confirmed by the utility.

Absolute accuracy is how well the values confirm to the true one and can never be better than the accuracy of the worst sensor involved. Change sensitivity is rather a measure of how the values from the same sensor set varies over time due to noise, disturbances or other circumstances. The change sensitivity is substantially better than the absolute accuracy. This is exemplified by the distribution of individual measurements as shown in Fig. 7 and Fig. 8, which have a width of only 0.025 % despite the 1 % accuracy class of the current sensors. Similarly Fig. 12 shows that current differences as small as 0.2 A can be detected while the nominal load current is 220 A, thus the sensitivity is 0.1 %. The systematic sensor errors can even cause negative transformer losses to appear, such as in Fig.s 6 and 7. This is an effect of sensors on primary and secondary side having errors that are particularily ill matched. Thus the losses may even appear to decrease with increase in load. The change in loss is more accurate than the absolute values.

Thus the usage and performance of a working transformer was investigated under normal as well as under the influence of system events, the latter is generally not considered by traditional monitoring methods. The ability of Transformer Explorer as an online monitoring tool was demonstrated highlighting the limitations posed by the sensors for detection of changes occurring within the transformer parameters.

Distinguishing accuracy and sensitivity is thus an important means to obtain a reliable and informative monitoring system. This requires a careful selection of how the obtained quantities are studied and in this direction the paper shows how this can be done by supplementing some examples.

Such monitoring solutions are going to redefine the asset management strategies with the advancement of both communication technologies and computational power, suitably supported by the future trends of cloud computing and unprecedented amount of data/information sources.

Transformer Explorer retains the possibility of acquiring the phasors or waveforms from multiple sources, such as industrial data acquisition cards, protection relays from different vendors or even from the disturbance recorders which stores Comtrade files. The flexibility offered by Transformer Explorer makes it a versatile monitoring solution, usable across the board as a vendor agnostic platform. It helps gathering information not just about the transformer parameters but also the system related events that occur outside the transformer. A clear example was illustrated in Section V, where overcurrent events have been captured and later confirmed by the operator.

REFERENCES [1]

A. Jahromi, R. Piercy, S. Cress, J. Service, W. Fan, “An approach to power transformer asset management using health index,” IEEE Electr. Ins. Magazine, 2009, pp 20-34. [2] M. Wang, A.J. Vandermaar, K.D. Srivastava, “Review of condition assessment of power transformers in service,” IEEE Electr. Ins. Magazine, vol. 18, issue 6, pp 12-25. [3] X. Zhang, E. Gockenbach, “Asset-management of transformers based condition monitoring and standard diagnosis”, IEEE Electr. Ins. Magazine, 2008. [4] S. Sarkar, T. Sharma, A. Baral, B. Chatterjee, D. Dey, S. Chakravorti, “An expert system approach for transformer insulation diagnosis combining conventional diagnostic tests and PDC, RVM data,” IEEE Trans. on DEIS, vol. 21, issue 2, Apr. 2014, pp 882-891. [5] S.K. Sahoo, L. Satish, “Discriminating changes introduced in the model for the winding of a transformer based on measurements,” Electric Power Systems Research, vol. 77, Issue 7, May 2007, pp 851-858. [6] N. Hashemnia, A. Abu-Siada, S. Islam, “Improved power transformer winding fault detection using FRA diagnostics – part 2: radial deformation simulation,” IEEE Trans. on DEIS, Vol. 22, Issue 1, Feb. 2015, pp 564-570. [7] T. Bengtsson, N. Abeywickrama, “On-line monitoring of power transformer by fundamental frequency signals,” A2-110, CIGRE 2012. [8] Transformer reliability survey, CIGRE WG A2.37, 2013-14. [9] N. Abeywickrama, T. Bengtsson, R. Saers, “Transformer Explorermonitoring transformer staus by fundamental frequency signals,” condition monitoring and diagnostics (CMD) conference, 25-28th September, China. [10] IEEE Standard Requirements for Instrument Transformers, IEEE Std C57.13, 2008. [11] Instrument transformers –Part 1: Current transformers, IEC 60044-1, 2003. [12] Instrument transformers –Part 2:Inductive voltage transformers, IEC 60044-2, 2003.

VII. CONCLUSIONS The basic properties of an in-service power transformer were evaluated with help of Transformer Explorer. The installation procedure was performed while the substation was under normal service. The major findings from the analysis can be summarized as: a. The fundamental quantities of the transformer: ratio, impedance and loss, have not changed significantly since the factory acceptance test in 1967. b. High power losses were observed at specific instances that coincide with the hydro power generation fed to the system at 30 kV level. This also corroborates with lower power flow leading to a reverse power flow upstream. b. During the observation period, a total of 503 tap operations took place and recorded, mostly switching between three taps during three months. The majority of time, almost two months, was spent in Tap 7.