Health Index: the TERNA s Practical Approach for Transformers Fleet Management

Transcription

1 213 Electrical Insulation Conference, Ottowa, Onterio, Canada, 2 to 5 June 213 Health Index: the TERNA s Practical Approach for Transformers Fleet Management Fabio Scatiggio TERNA RETE ITALIA SpA Via delle Querce, VENEZIA Italy fabio.scatiggio@terna.it Massimo Pompili UNIVERSITA SAPIENZA Via Eudossiana, ROMA - Italy Abstract - The asset management of any Transmission System Operator (TSO) cannot ignore the evaluation of the power transformers fleet. Even in the absence, to date, of any specific international guide or standard, every big electrical utility is adopting a home-made Health Index. An is developed to summarize in numerical form the transformers reliability for the purpose of evaluation, ranking and comparison. In the present paper the author will elucidate a new model that merge the evidences of periodic tests (DGA, furans, acidity, inductance, FDS, etc.) with the keraunic properties of a substation. Keywords: Health Index, Asset Management, Transformer I. INTRODUCTION Power transformers have indisputably the highest value of all equipment installed in transmission substations, comprising up to 6% of the total investment. To afford a reliable but at same time simple tool for evaluating the whole transformer fleet status, a so-called Health Index () was developed, to support the technical and economical justification for engineering decisions and capital replacement plans [1]. is an asset management tool that combines: site specificity where the substation is located on-site and the laboratory testing. The first parameter is basically static, since it doesn t change over time and is independent of the transformer itself. It depends on the keraunic level (lightning frequency) of the site, the substation lay-out, the recurrence of catastrophic events on that site, etc. The second parameter is strictly dependent on the transformer and should be considered dynamic, since it changes as the transformer ages. It gives information on: dielectric and thermal condition - from DGA and furans analysis. mechanical condition - from on-site electrical tests such as sweep frequency response analysis (SFRA), frequency domain spectroscopy (FDS). insulating oil condition - from water content, acidity, breakdown voltage (BDV) and dielectric dissipation factor (DDF). The mathematical combination of the two parameters generates the, which is of critical importance for addressing the management of large transformer fleets. Other aspects such as system events and conditions and evidence from periodic field inspections should be considered for evaluating the transformer status (number of energisations, load, oil and winding temperature, defects and breakdowns, external condition issues, oil leaks, etc). Although these parameters are of fundamental importance they are often partially unknown, frequently difficult to collect and hard to incorporate automatically into computer programs. II. SCENARIO It is well known that the traditional concept of transformer life must be reconsidered in light of new electrical market requirements. Increase of load, smart grids, competition and reduction of investment are elements that force the owner to reconsider operating and maintenance strategies [2]. Transformer manufacturers used to define the transformer s life in the range of 25-4 years depending on application, after this period the original transformer reliability is not assured or at least corrective actions must be taken. Basically the maintenance policies are time-based and therefore the utilities used to schedule over the years the oil sampling for testing, the on-field electrical & thermographic tests, the OLTC periodic check, the oil degassing or reclaiming, etc. All these operations are costly and time consuming and some require that the transformer is switched-off at additional cost. Nevertheless, many transformers are still in service with an acceptable failure rate even once they have reached or exceeded 5-6 years old, as may be seen in figure 1. So the traditional age oriented maintenance is no longer acceptable and must be replaced by another strategy such as conditionbased maintenance /13/$ IEEE 178

2 % Failure Rate % 18 16, , ,3 4 2, age (years),1 Terna CIGRE Figure 1 Terna s transformers age grouping III. BENCHMARKING Recently a CIGRE WG has presented an interim report on transformer failures [3] as an update of the previous CIGRE report [4] published in It summarized an international survey on transformer reliability in terms of failure rates and classification into failure location. The definition of failure was limited to major failure and described as any situation which required the transformer to be removed from service for a period longer than 7 days for investigation, remedial work, usually requiring the transformer to be removed form its installation site and returned to the factory. Failure rate (λ) is expressed as: n i i 1 % N Where n is the number of transformers failed in the i th year, and N is the number of transformers in service during the i th year. The following table reports the failure rate for substation transformers grouped by voltage. TABLE I FAILURE RATE OF SUBSTATION TRANSFORMERS Highest System Voltage (kv) all > Failures Transformers - Years Failure Rate % In figure 2 is shown the failure rate that affected the Terna transformers fleet over the last decade, compared with CIGRE target for 2-5 kv transformers. Terna s trend is comparable with the CIGRE survey results and demonstrates the excellence of the Terna strategies. i i Year Figure 2 TERNA vs. CIGRE failure rate In figure 3 the failure locations for transformers (all applications) from the CIGRE survey are given, and it can be easily seen how the ancillary components (bushings and OLTC) are responsible for more than 4% of the breakdowns. It is evident that particular attention should be taken in planning periodic checks for this kind of equipment. Core and magnetic circuits 3% OLTC 26% Bushings 17% Other 1% Insulation 1% HV connections 7% Windings 45% Figure 3 fault location grouping, from CIGRE IV. DEFINITION AND CALCULATION For keraunic level evaluation the typical station layout is shown in Figure 4, it takes into account the real incidence of direct lighting on transformer (typically estimated at 1 in 274 years), direct lighting on HHV busbars (3 every 1 years) and on HV busbars (2 every 274 years) and finally direct lighting on overhead lines (1 every 8 years and 1 every 11 years respectively for HHV and HV). 179

3 LINES 38 Kv - HHV BUSBARS 38 kv TRAFO 38 / 15 kv BUSBARS 15 Kv - HV LINES 15 Figure 4 Terna s substation typical kv layout For every single substation the geometrical area of transformers, busbars and lines, the annual lighting density based on standard CEI 81-3 [5], ground orography, presence of other interfering buildings, towers, etc is considered. The full explanation of site specificity (keraunicity, substation design, etc.) is out of scope of the present paper and will be fully treated in a separate detailed study. The transformer status is monitored at regular intervals by off-line tests and/or continuously by on-line tests. The information coming from both kinds of checks are conventionally grouped into four different families: Parameters about the dielectric and thermal condition derived from DGA. They include electrical faults (like partial discharges, low energy discharges, arcing) and thermal faults (hot spots). Parameters about the purely thermal condition of paper derived from the CO2 & CO and furans. Parameters about the mechanical condition derived from on-site electrical tests (inductance, SFRA, PDC/FDS). Parameters about the insulating oil condition derived from water, acidity, BDV and DDF. Some other parameters (see IEC 6422 [6]) were not taken into consideration as it was considered that there is a substantial overlap with the parameters described above. In the present work, the risk due to PCB contamination was not taken in consideration because all of TERNA s oils are PCB free. Also the risks related to DBDS and corrosive sulfur presence were disregarded as consequence of the mitigating actions adopted for tackling them. Since the risk relevance of the 4 conditions is largely different, the scoring system is designed so that every individual measure is weighted. The relative weight applied is inevitably subjective as it is largely dependent on field experience of every single utility. V. THRESHOLD LIMITS The reference targets are obviously deduced from international guidelines or regulations (IEC, IEEE, CIGRE, etc.) or, in the absence of a suitable standard, deduced from statistical computations based on utility s database (furans, SFRA, etc.). The most commonly used statistical approach is the calculation of 9-95th percentile as a maximum limit for acceptability. For example in the following tables and figures are shown and compared the DGA criteria of the Normalizations Bodies and from TERNA s analytical database. TABLE II IEC AND IEEE VALUES IN PPM. IEC 6599 IEEE Std C Typical Level Level Level 1 (9%) 2 3 Level 4 H >18 CH >1 CO >14 CO >1 C2H >2 C2H >15 C2H >8 TABLE III CIGRE VALUES IN PPM. PF= PRE-FAILURE CIGRE Brochure 443 [7] Level Level Typical Level 4 PF 2 3 H CH CO CO C2H C2H C2H Pre-failure (PF) concept was first defined and employed in CIGRE Brochure 296 [8], it defines the dissolved gases concentrations detected just before a failure event (trippingout, Buchholz, tank rupture, fire or explosion). TABLE IV PERCENTILES VALUES IN PPM FROM TERNA S DATABASE TERNA last analysis (434 cases) 9th 95th 97th 99th H CH CO CO C2H C2H C2H TERNA all analysis (598 cases) 9th 95th 97th 99th H CH CO CO C2H C2H C2H In figure 5 the evidences coming from TERNA s database are compared with CIGRE limits, a general good match was observed since the curves substantially overlap. Regardless 18

4 PFO1 PFO2 PFO3 PFO1 PFO2 PFO3 the present work it was decided to prioritize the CIGRE thresholds for prudential purposes. 5 4 DIE TERM,5 3 2 MEC OLIO> 1, Figure 6: (max 14) on the left side and unitary (p.u.) on the right side, of 3 transformers In figure 6 are shown the of 3 different transmission transformers, on the left side is segregated into the 4 parameters and on left the unitary is displayed. PF3 is the unit in the wrong status with the higher relative risky condition. Figure 5 Terna vs. CIGRE limits. VI. EXAMPLE OF CALCULATION Since the test parameters are expressed in different units (kv, ppm, %, etc.) they must be converted into a nondimensional number (rank). As an example, starting from water content in oil of 18 mg/kg (ppm) will be generated a rank of.15. VII. APPLICATION The scoring system, ordered by decreasing, for the entire population (> 7 units) of TERNA s power transformers is displayed in figure 7. Transformers with low that fall in right side of the graph can be considered as in safe condition and on the contrary transformers with the higher are located on the left side of the graph and should be classified as in risky condition. TABLE V MATRIX FOR WATER CONVERSION Test IEC 6422, for True or Weight >17 kv False Rank Good < 15 No Water = Fair Yes mg/kg Poor > 2.3 No Weighted rank for water content =.15 The same approach was applied for all the considered parameters for oil evaluation. oil... test Weighted rank test1 n Weighted rank test 2 And then also for all the other categories the same approach was adopted. dielectric thermal max mechanical Where max=14, so will be finally expressed in per units (p.u.) and at the end of day the higher will be associated with transformers with a low level of reliability. oil,5, Trafo # Figure 7 TERNA s transformers ordered by decreasing. In order to verify the long term maintenance and replacement plans, figure 8 shows as function of time in service. It is evident how is completely independent of transformer age as demonstrated by the extremely low Pearson s correlation coefficient (R2=.3). As a matter of fact some young transformers present high and on the contrary some other, very old transformers have a diametrically opposed. 181

5 n of cases,5 R 2 =,3 VIII. CONCLUSION The composite that merges electrical, thermal, and mechanical and oil deficits is very useful for representing the real condition of every single transformer and creates a pragmatic tool for the maintenance and replacement strategies based on condition., Age (years) Figure 8 TERNA s transformers ordered by age. This demonstrates how the transformers service age is not at all the key parameter for planning the maintenance and replacement activities for a large transformer fleet. Finally, each transformer is classified in four ranges of which represents the failure probability. The four classes express the different level of remaining strength in terms of electrical, thermal, mechanical and oil condition, as displayed in the following table and figure. TABLE VI S RANGES Condition Colour.1 Very Good Green.1.2 Good Pale Blue.2.3 Fair Yellow >.3 Poor Red Transformers classified in very good and good condition can be managed following the common and standard maintenance practices, transformers classified as fair need an increase of analysis frequency or a deeper investigation. Finally transformers with >.3 ( poor ) should be considered in critical condition and they require a replacement priority. Of course some maintenance actions such as oil reclamation, dehumidification, etc. can partially reverse the value and move the transformer into a lower class of risk. ACKNOWLEDGMENT The authors gratefully thank Gordon Wilson of National Grid UK for his valuable support in the paper revision. REFERENCES [1] A. Nadeiran Jahromi, R. Piercy, S. Cress, J.R.R. Service, W. Fan: An Approach to Power Transformer Asset Management Using Health Index - IEEE Electrical - Insulation Magazine March-April 29 Vol.25, No.2.-. [2] EPRI Report # 11938: Extending the useful life of power transformers is the single most important strategy for increasing life power transmission and distribution infrastructures, starting with generator step-up transformers (GSU) at power plant itself. [3] CIGRE A2.37 Transformer Reliability Survey: Interim Report Electra N April 212. [4] A. Bossi et al. An international Survey on Failures in Large Power Transformers in Service Final Report Electra N 88, pp , [5] Italian Standard CEI 81-3 Average values of the number of lightning strikes per year on ground and per square kilometer of Italian Municipalities, in alphabetical order - Valori medi del numero dei fulmini a terra per anno e per chilometro quadrato dei Comuni d'italia, in ordine alfabetico. [6] IEC 6422: Supervision and maintenance guide for mineral insulating oils in electrical equipment. [7] CIGRE Brochure 443: DGA in Non-Mineral Oils and Load Tap Changers and Improved DGA Diagnosis Criteria. [8] CIGRE Brochure 296 Recent Developments in DGA Interpretation <,5,5 -,1 12,1 -,15 42,15 -,2 31,2-13 -, ,3 -,35,35 -,4 >,4 Figure 9 TERNA s transformers grouped by risk classes. 182