The effectiveness of oil analysis for failure prediction of power transformers

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1 The effectiveness of oil analysis for failure prediction of power transformers by Krige Visser and Yashin Brihmohan, University of Pretoria The availability of an electricity network is of utmost importance in industrialised regions since breakdowns cause huge disruption to factories, large buildings and traffic. The reliability of transformers affects the availability of a network and many techniques have been developed for preventive maintenance of transformers. Various condition-monitoring techniques are used to determine the condition of power transformers. This paper discusses the findings of a research project to determine the effectiveness of oil sampling techniques that are currently used in predicting the failure of a power transformer. The study was conducted on power transformers with the focus on the dissolved gas analysis technique as part of an oil sampling method for transformers. It was found that the effectiveness of the dissolved gas analysis on its own is much lower than perceived by management of the company. The cost effectiveness of the oil sampling and analysis technique was also evaluated from historical data as well as questionnaires and it was found that the dissolved gas analysis is not a financially viable method of preventive maintenance for transformers Introduction Background and context of the study Business enterprises rely on financial and technical viability, both of which provide an integral structure for business sustainability and customer satisfaction. The business sustainability is further supported by operations and maintenance. The maintenance activities should be investigated to determine its effectiveness, and hence its impact on the sustainability of the business. This research project was conducted within the Distribution sector of the electricity supply industry in South Africa. This sector of the business is in the process of been privatised, and hence effectiveness of the sector must be evaluated in the context of the future business. There is an increasing need for power generation and distribution installations, and in particular power transformers, as the South African economy continues to grow. This expansion of the economy has dramatically increased the demand for electricity. Power transformers are an expensive and critical subsystem of the electricity network and are an essential component required in addressing this demand. While new transformers address the new demand, it is vital that several old transformers in the network be attended to, either through replacement or maintenance activities. Oil sampling of transformers The practice of oil sampling of power transformers through the use of the dissolved gas analysis technique was investigated. This research focused on assessing the current practice of oil sampling within power transformers (capacity of 1 MVA to 120 MVA) within the Distribution sector in South Africa. The objective was to investigate the effectiveness and financial viability of the condition monitoring practice (dissolved gas analysis of oil) for failure prediction on power transformers. The specific objective was to determine whether, based on historical evidence, a failure has or could have been predicted prior to a power transformer failure based on the practice of oil sampling. Importance of the topic Power transformers within the Distribution industry are considered a major asset (in terms of monetary value, and service delivery), and it is therefore necessary to assess maintenance activities on the asset. Failures on power transformers can also be catastrophic and has an impact on human life as well as on the environment. Therefore, if failures can be predicted, the methods used (in this case the oil sampling practice) need to be investigated to determine its effectiveness in failure prediction. Literature Power transformers are crucial elements of an electricity network. Mainly step-down power transformers are used to convert voltages from higher to lower levels for purposes of electrical transmission to the customer. Heathcote [1] describes the fundamental theory of power transformers. Maintenance is conducted on power transformers in accordance with relevant distribution standards. The standard by Busch [2] was used within Eskom distribution for the period September 2001 until its revision date in September Day 2 Stream 4B: Presentation 1 Page 1

2 This standard describes the various maintenance activities that are required for power transformers, which include transformer oil sampling. Huang [3] proposes a novel model for condition monitoring of power transformers using wavelet networks. Han and Song [4] says that the dissolved gas analysis (DGA) method, although widely used by utilities, is "not a completely objective and accurate method". Wood et al [5] describes a new approach to monitor transformer oil using light absorbance. Wood remarks "samples of failed transformers showed a general increase in absorbance during the experiment". Models and Methods Discussion of models Systems engineering theory can be used as a basic framework or model to evaluate the maintenance practice for power transformers. In the systems approach, the maintenance system comprises the maintenance personnel, training and training support, supply support, support equipment, computer resources, packaging, handling, storage, transportation, maintenance facilities, technical data, information systems, and database structures. Maintenance personnel are required for the installation, checkout, and sustaining maintenance throughout the planned life cycle. Training and training support is required for all personnel throughout the planned system life cycle. This is to ensure appropriate management of operation, and maintenance activities. Supply support includes all spares and repair parts that are required to support the maintenance of the asset. This also includes maintenance to software, support equipment, transportation and handling equipment, training equipment, and facilities. Support equipment includes all tools, specialised test equipment, and condition-monitoring equipment required to support operation and maintenance activities. Computer resources are all computer resources which support operations and maintenance of the asset throughout its planned life cycle. Packaging, handling, storage, and transportation include all equipment and storage space required for packaging, handling, storage, and transportation. Maintenance facilities are facilities required to support maintenance activities. Technical data, information systems, and database structures include all data and information systems necessary to support operations and maintenance. This provides a framework for the assessment of the maintenance practice. The power transformer can also be viewed as a component of the electrical system. The power transformer is a component of a substation and has sub-components which include the tank, paper insulation, oil insulation, tap changer, bushings and surge arrestors. In order for these components to provide their function, maintenance intervention is necessary to manage the failure modes and associated risks imposed onto the electrical system. This has been investigated by Eskom through the failure mode, effects and criticality analysis (FMECA) for the transformer components. A typical hardware breakdown of a transformer is illustrated in Fig. 1. Substation Busbar Transformer Bay Incoming Feeder bay Outgoing Feeder bay 1 Outgoing Feeder bay 2 Outgoing Feeder bay 3 Power Switchgear CT's VT's SA's Protection transformer Main tank Tap changer Bushings Windings Oil insulation Paper insulation Fig. 1: System breakdown for substation. Day 2 Stream 4B: Presentation 1 Page 2

3 Each component of the power transformer has a specific function to perform. Failure to perform this function would result in the transformer not operating to a specified requirement. Each component failure has failure modes, which describe how the component fails, as well as failure effects which describe the consequences of the failure mode. The root causes for each failure mode is also determined. The monitoring methods illustrated in this study are methods chosen such that it can determine a failure appropriately. Oil sampling, more specifically dissolved gas analysis, provides a condition monitoring method to determine failure prediction of the various components of the power transformer. The framework used illustrates the purpose of the dissolved gas analysis and are highlighted in the study. International models for oil analysis The international models listed below provided the framework for the research project. The California model: This oil diagnostic model was developed by the California State University. This model analyses the dissolved gases within the transformer oil and predicts the most likely fault that would exist within a transformer for a particular gas level. The model is summarised in a tabular format in Appendix A, and illustrates the normal, elevated, and abnormal quantities of gases where the normal level indicates that the transformer is operating normally, an elevated level indicates a developing fault, and an abnormal level indicates that the fault indicated is present. The Total Combustible Gas IEEE method: This method was developed by the institute of electrical and electronic engineers incorporated. Combustible gases include acetylene, carbon monoxide, ethane, ethylene, hydrogen, and methane. The total dissolved combustible gas method is the sum of the values of the above gases. This sum indicates the condition of the transformer, and is tabulated in Appendix A. Ratio methods (Rodger and IEC methods): Rodger s ratio is a commonly used method as a diagnostic tool for oil samples. This method calculates four ratios, and allocates codes to these ratios. The combination of the ratios with the relevant codes predicts the transformer condition, and hence typical fault conditions that exist within the unit. However, the limitations include that the value of some gases need to be greater than zero to ensure appropriate mathematical division. IEC 599 method: This method uses the same principle as the Rodger s ratio method, but has adopted three of the ratios. These methods are given in Appendix A. Daily production rate method: This method analyses more than one transformer sample and calculates the daily production rate of the gases. The expert needs to determine a maintenance time when it is believed that the transformer is operating abnormally. Transformer size will be considered in the diagnosis. The normal and abnormal rates are illustrated in Appendix A. Key gas method: The IEEE guide defines key gases as "gases generated in oil-filled transformers that can be used for qualitative determination of fault types, based on which gases are typically or predominantly at various temperatures". The key gases are summarised in Appendix A, based on the data from IEEE PC D11d. Graphs are generally used to illustrate the typical relative proportion of gases that show a typical fault and the test sample is compared to the generic graphs. Effectiveness of monitoring methods The process used to determine the effectiveness of the dissolved gas analysis (DGA) methods to predict failure of a transformer comprises the following steps. Identify failed power transformers Identify the oil samples taken from the transformer before failure occurred Determine DGA diagnostic methods Apply each diagnostic method to the oil sample taken before failure Tabulate prediction against failure for each diagnostic method Determine whether the diagnostic method could have predicted a failure for a specific transformer A process to test the financial viability of the maintenance practice is summarised in table 1. Day 2 Stream 4B: Presentation 1 Page 3

4 Step No. Description of Task 1 Determine repair cost of transformer without prediction 2 Determine repair cost of transformer without prediction 3 Determine repair profit of predictive maintenance = Repair cost of transformer without prediction - Repair cost of transformer with prediction 4 Determine cost of predictive maintenance = Cost of maintenance personnel + Cost of training and training Support + supply support + support equipment + computer resources + packaging, handling storage, and transportation + maintenance facilities + technical data, information systems, and database structures 5 Determine Financial Viability = Repair Profit of Predictive Maintenance + Cost of energy lost due to an unplanned transformer replacement - Cost of energy lost due to a planned transformer replacement + Cost of consequential damages due to unplanned failure - Cost of consequential damage due to a planned failure - Cost of Predictive Maintenance Table 1: Process to determine financial viability. Research Methodology Existing strategies for oil sampling of transformers were identified in the Eskom Distribution environment in South Africa. A framework for data collection was then established. Data was gathered for all existing power transformers managed by the Distribution division of Eskom. Failure information on the transformers as well as oil sample information was obtained for the analysis. Each failure from the sample chosen was evaluated against the routine sample information to determine if a developing fault could have been detected. This involved the use of internationally accepted models for the analysis of the oil samples. Financial analysis has also been done to quantify potential cost savings if an imminent failure is determined. The findings are presented to highlight the shortcomings in the existing practice, and recommendations are made to address the shortcomings if necessary. In addition to the oil sampling data, questionnaires were also developed and interviews were conducted with relevant maintenance personnel and specialists. The specific individuals were chosen for the survey based on their current area of responsibility and related area of specialist experience and knowledge. The results of the questionnaires were analysed to determine shortcomings of the existing practice, as well as to capture vital information which was further analysed to determine the effectiveness of the oil sampling maintenance practice. Additional information was also sourced from the specialists to evaluate the effectiveness of the current practice. This was done through determining the number of transformers removed from service in the past ten years due the analysis of the oil samples taken from a power transformer. This number was then compared to the total number of failures to determine the percentage of failed transformers removed from service due to the analysis of oil samples. Results Data was collected from the questionnaires distributed to the maintenance specialists as well as extracted from the oil database of Eskom. Day 2 Stream 4B: Presentation 1 Page 4

5 Analysis of the current practice of oil sampling The maintenance practice of oil sampling for dissolved gas analysis testing was generally found to be conducted annually, although in some cases samples were taken at six monthly intervals. It was found that there is no formalised tool used for the planning and diagnosis of the oil samples, and hence the decision to remove a power transformer from service due the analysis of the oil samples is not consistent. It was found that although international models were applied on the sample analysis, experience is a critical factor to determine whether a power transformer should be removed from service. A total of 78% of the specialists interviewed believe that the maintenance practice of oil sampling is effective in determining a power transformer failure, and 55% of the specialists could not clearly indicate whether the practice is financially viable. No formalised study could be provided by the regions under study to determine whether the practice is effective or financially viable. This information was derived from the data from questionnaires received from the specialists. Limitations of the current maintenance practice of oil sampling are illustrated in table 2. No. Description of Limitation 1 International benchmarks are not conducive to the South African environment 2 Dissolved gas analysis is just one variable to assess plant health 3 Taking a sample annually is not as effective as online sampling for 24 hours, as it takes only a snapshot of the current situation 4 It is doubtful that an annual sample will be sufficient to alarm of an abnormal situation 5 The quality of the results is directly related to the quality of the oil sample. The sample results are extremely dependant on handling, transportation, and the competence of the technician taking the sample 6 Due to long distances from the laboratory; it takes a long time to transport the sample for emergency testing, which effects the restoration time of the customer 7 Experience is a necessity to make an assessment of the monitoring results 8 DGA is not 100% accurate and open to interpretation, and a wrong diagnosis is possible 9 There is a lack of a planning tool for dissolved gas analysis Table 2: Limitations of the oil monitoring methods. Analysis of results of oil samples of failed transformers The oil sample database was investigated to determine whether oil sample data was available for each of the power transformers that failed in the year 2005 for Eskom Distribution Central Region. The database was searched per serial number which is a unique identifier of power transformers per manufacturer. There was a 100% strike rate, which means that there was oil sample data available for all of the power transformers that failed in the year There are a total of 993 power transformers within the electrical distribution network in the Central Region. Of these, 24 transformers failed in 2005, which represents 2,4% of the total. From the analysis, it was found that 28,6% of the power transformers that failed had an oil sample for analysis in the year of the failure, and 19% of the power transformers had the last sample taken before the year This means that a significant number of power transformers were not sampled at least annually. This has an impact on the prediction of failure, as the prediction is directly dependant on the sampling interval. Analysis of the oil samples of the failed transformers revealed that the total combustible gas method (IEEE Method), the IEC ratio method, the Rodger s ratio method, and the daily production rate method did not illustrate significant indication of a failure. The Key Gas method did indicate that there was overheated cellulose in the majority of the failed power transformers, but this does not indicate an imminent failure. The California method indicated that there were abnormal conditions in 33% of the samples analysed. This was based on abnormally high levels of at least one of the gases present in the oil. However, the levels of abnormality were not significant enough to predict a transformer failure. It can therefore be concluded that prediction of failure prior to the actual failure of the transformers could not have been determined using the existing practices and methods of maintenance and analysis. Day 2 Stream 4B: Presentation 1 Page 5

6 The results in table 3 indicate that for all regions about 1,7% of the transformer failures in 2005 were predicted by the dissolved gas method of oil analysis. The actual number of transformer failures in 2005 are given as well as the number of failures that were predicted, averaged over 10 years. The Western region had the highest success rate of 7,1% and the North West region the lowest of 0%. The range of 0 7,1% indicates a large difference in the success rate for failure prediction. The reasons for this difference is unknown but it could be due to the different analysis models and experience applied in predicting a power transformer failure The reasons for this large discrepancy should be investigated further. Region in South Africa Total number of transformers installed Actual number of failures in 2005 Average number of predicted failures/year Average number of failures predicted with oil sampling analysis (%) Eastern ,4 3,6 Southern ,1 3,3 Western ,5 7,1 Northern ,3 1,2 North West Central ,1 0,4 Total ,4 1,7 Table 3: Effectiveness of the maintenance practice to predict failures. The overall success rate of only about 1,7% is a clear indication that the dissolved gas analysis is not currently successful and should probably be reviewed to improve the success rate. Financial viability of oil sampling Although failure data was not readily available from all the regions within Eskom, assumptions were made regarding the analysis, where it was assumed that the failure rate per year calculated based on a single year of study is valid for a period of ten years. The financial viability of the maintenance practice could not be calculated based on the model developed in this research due to data not being readily available. However, alternative analysis was done to determine the financial viability. The cost of labour, transport, and oil sample analysis was used as the total maintenance cost, which equated to an annual maintenance cost of R14,5 million for the population of 4197 power transformers. Based on specialist feedback, it was assumed that on average the cost of a predicted failure is 6,5 times less than the cost of an unpredicted failure. Given the number of power transformers removed from service, a cost difference between a predicted and unpredicted failure of R12,9 million was estimated. This amount is R1,53 million less than the maintenance cost of implementing the condition monitoring technique, therefore indicating that the practice is not financially viable. More accurate actual cost data would be necessary to verify this result for more regions in South Africa. Conclusions The effectiveness of the current practice of oil sampling to predict the failure of power transformers was established through this research project. It was found that the current method of oil sampling using dissolved gas analysis alone is not as effective as perceived by management. An average of only 1,7% of transformer failures were actually predicted by this method and management should review alternative mitigating strategies to manage the risk of transformer failure. The financial viability of the maintenance practice of oil sampling in predicting power transformer failures was also assessed. Although data was limited, it can be concluded that the cost of the condition monitoring using dissolved gas analysis is too high for the potential benefit of predicting failures. The research has also highlighted limitations in the current practice of oil sampling. Further research, involving data analysis of more transformers and for longer periods is needed to determine means of improving the current methods of oil analysis, or to discard the current practice of oil sampling. A financial analysis, based on credible actual cost data, should also be done to verify the financial viability of the technique. A break-even point for success rate could also be determined from the cost analysis. Day 2 Stream 4B: Presentation 1 Page 6

7 APPENDIX A Gas Normal Elevated Abnormal Interpretation Acetylene <15 >15 and <70 <70 Arcing Carbon Dioxide < >10000 and > Severe Overloading <15000 Carbon Monoxide < 500 >500 and < 1000 >1000 Severe Overloading Ethane <10 >10 and < 35 > 35 Local Overheating Ethylene <20 >20 and <100 >100 Severe Overheating Hydrogen <150 >150 and < 1000 >1000 Arcing, Corona Methane <25 >25 and <80 >80 Sparking Nitrogen 1-10% Normal ageing Oxygen % Normal Ageing Total Combustible Gases < 720 >720 and <5000 >5000 Total Combustible Gas Limit Table A1: California Model: Guideline for Combustible Gases Sum of Dissolved Combustible Gases () Diagnosed Condition Within Transformer > 4630 Continued operation could result in unit failure Fault/s are probably present Fault/s may be present < 720 Operation satisfactory Table A2: Total combustible gas method Chemical Ratio Ratio Abbreviation < > 3 CH 4 /H 2 R C 2 H 6 /CH 4 R C 2 H 4 /C 2 H 6 R C 2 H 2 /C 2 H 4 R Table A3: Rodger s Ratio Coding R1 R2 R3 R4 Diagnosed Condition Within Transformer Normal Partial Discharge or 2 Partial Discharge with Tracking Continuous Sparking to floating potential or 2 1 or 2 Arc with power follow-through Flashover without power follow-through General Conductor Overheating 1 or Overheating 150ºC to 200 ºC 1 or Overheating <150 ºC Overheated Joints Winding Circulating Currents Core and Tank Circulating Currents Table A4: Rodger Ratio Diagnostic table on transformer condition Day 2 Stream 4B: Presentation 1 Page 7

8 References [1] Heathcote, M.J. J & P Transformer Book. 12th Ed Oxford, UK : Elsevier Science Ltd. [2] Eskom. Standard for Sampling and Testing of Mineral Insulating Oil for Power Transformers, Reactors and Switchgear [3] Huang, Y-C. Evolving Wavelet Networks for Power Transformer Condition Monitoring. IEEE Transactions on Power Delivery,17, No [4] Han, Y. and Song, Y.H. Condition Monitoring techniques for electrical equipment A literature study. IEEE Transactions on Power Delivery, 18, No [5] Wood, R., Shoureshi, R. and Wang, X. Optical Sensor for Transformer Monitoring. Symposium on Diagnostics for Electric Machines, Atlanta, USA Day 2 Stream 4B: Presentation 1 Page 8