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This paper describes a cluster assessment (CA) method for automatic detection, assessment, and logging of significant fault gas production events by analysis of multi-gas online monitor data. When large numbers of transformers are monitored, automatic screening interpretation of the data is necessary. The automated interpretation of dissolved-gas data must be quite skilled at discriminating between exceptional and unexceptional patterns to provide high sensitivity and high specificity, i.e., to detect incipient problems reliably while generating very few false alarms. The CA method is applied to a moving time window
of the most recent 30 to 90 days of multi-gas monitor data for each transformer. It relies upon three innovative elements:
1. Statistical estimation of measurement uncertainty from in-service data. A paper deriving the statistical estimators and evaluating their performance is in preparation.
2. A fault energy index calculated from the gas concentrations. The index is the normalized energy intensity (NEI) described in "Thermodynamic Estimation of Transformer Fault Severity."
3. Cluster analysis applied to the NEI data.
An uncertainty estimate derived from the data is used for adaptive tuning of the cluster analysis and for discriminating between insignificant and significant increases. Clusters and transitions between clusters are subjected to diagnostic assessment. Any that appears to represent the production of a significant amount of new fault gas is reported as an event with a severity score and an apparent fault type. The severity score can be based on the amount of change in survival probability as illustrated in this paper.

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... where mu and sigma are as stated above and capital phi is the cumulative distribution function of the standard normal distribution. A similar Kaplan-Meier curve, based on early exploratory work on the same data, was shown in [8] Figure 1. It was limited to the portion corresponding to survival probability between 0.95 and 1.0. ...

... Our original intention in pursuing DGA survival analysis was, following the example of the PFS method, to use the NEI survival curve to locate NEI limits for condition assessment. Following that idea, Table Iof [8] uses NEI values corresponding to survival probabilities of 0.99, 0.97, and 0.95 as DGA condition assessment limits. Surprisingly, however, work subsequent to the publication of [8] demonstrated that condition codes based on gas concentration or NEI limits do not represent degrees of deterioration of the transformer. ...

... Following that idea, Table Iof [8] uses NEI values corresponding to survival probabilities of 0.99, 0.97, and 0.95 as DGA condition assessment limits. Surprisingly, however, work subsequent to the publication of [8] demonstrated that condition codes based on gas concentration or NEI limits do not represent degrees of deterioration of the transformer. That conclusion, hinted at by the question at the end of the Survival Analysis Concepts section above, is based on inspection of the graph (Figure 2) of the lognormal failure rate function h( ...

Dissolved-gas analysis (DGA) is widely used for transformer condition screening and assessment. Conventional DGA practice is to employ statistically derived limits for combustible gas concentrations and their increments or rates of increase for the purpose of classifying a transformer's condition as acceptable, suspicious, or abnormal. The DGA condition assessment, in the form of numeric "condition codes," is sometimes used as part of a transformer health index for prioritizing of testing and maintenance or for asset management functions such as replacement planning. The basis of this condition classification scheme is the seemingly reasonable assumption that higher fault gas levels must represent progressively worsened states of the transformer, with correspondingly reduced reliability. We show that this assumption is not supported by the data. Methods of reliability engineering statistics were applied to a large DGA database augmented with transformer failure data to obtain nonparametric and parametric models of survival probability as a function of a thermodynamic index of fault energy-the Normalized Energy Intensity (NEI), which is based on concentrations of hydrocarbon gases dissolved in the transformer oil. Key concepts from the Probability of Failure in Service (PFS) method devised by Duval and others were adapted for the application of reliability statistics to DGA. The best-fitting parametric model of a pre-failure values distribution for NEI and also for fault gas concentrations is lognormal. From the shape of the associated failure rate curves it follows that-except at low gas concentrations-the instantaneous failure rate relative to NEI actually decreases as NEI increases, implying that higher NEI levels do not imply more impaired transformer reliability. Therefore, "condition codes" based on NEI are not a good basis for classifying transformer health. This conclusion was verified for hydrocarbon gas concentrations as well. While levels of NEI or fault gas concentrations in themselves cannot say whether a transformer's suitability for continued service has been compromised, the survival models show that fault severity associated with a gassing event (producing an increase in NEI over two or more consecutive samples) can be quantified in terms of failure probability, optionally multiplied by a criticality or cost factor. DGA case histories are presented to show how this approach works in practice. KEYWORDS Transformer-DGA-Severity assessment-NEI-Reliability-Risk exposure 1 j.dukarm@ieee.org CIGRÉ-807 2016 CIGRÉ Canada Conference, Vancouver, Canada, October 2016.

Conventional practice for transformer dissolved gas analysis (DGA) is to use concentrations of several fault gases, with or without total dissolved combustible gas, for evaluating apparent fault severity. We suggest a simpler approach based on the normalized energy intensity (NEI), a quantity related directly to fault energy dissipated within the transformer. DGA fault severity scoring based on NEI is shown to be sensitive to all IEC fault types and to be more responsive to shifts in the relative concentrations of the fault gases than scoring based on fault gas concentrations. Instead of eight or more gas concentration limits, NEI scoring requires only two or three limits that can be empirically derived to suit local requirements for any population of mineral-oil-filled power transformers. OPEN ACCESS - FREE DOWNLOAD FROM HERE: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7065295

Methods are presented in this paper allowing individual networks or transformer users to calculate their own specific DGA gas limits and required sampling intervals as a function of gas concentrations and rates of gas increase in service. This calculation can be done on the entire transformer population or on more specific segments of it. The purpose of this paper is only to provide a tool for calculating gas levels requiring more frequent sampling intervals for DGA. The actions to be taken on the equipment at any of these gas levels and sampling intervals (e.g., removal or not from service, additional testing, installation of on-line gas monitors) are outside the scope of this paper. These actions depend on a large number of other parameters (e.g., experience and maintenance practices of individual users, strategic importance and type of equipment used, type and location of the fault, fault active or not). The actual sampling intervals to be used also remain the decision of maintenance personnel, based on their best operational practices.

In lifetesting, medical follow-up, and other fields the observation of the time of occurrence of the event of interest (called a death) may be prevented for some of the items of the sample by the previous occurrence of some other event (called a loss). Losses may be either accidental or controlled, the latter resulting from a decision to terminate certain observations. In either case it is usually assumed in this paper that the lifetime (age at death) is independent of the potential loss time; in practice this assumption deserves careful scrutiny. Despite the resulting incompleteness of the data, it is desired to estimate the proportion P(t) of items in the population whose lifetimes would exceed t (in the absence of such losses), without making any assumption about the form of the function P(t). The observation for each item of a suitable initial event, marking the beginning of its lifetime, is presupposed.
For random samples of size N the product-limit (PL) estimate can be defined as follows: List and label the N observed lifetimes (whether to death or loss) in order of increasing magnitude, so that one has \(0 \leqslant t_1^\prime \leqslant t_2^\prime \leqslant \cdots \leqslant t_N^\prime .\) Then \(\hat P\left( t \right) = \Pi r\left[ {\left( {N - r} \right)/\left( {N - r + 1} \right)} \right]\), where r assumes those values for which \(t_r^\prime \leqslant t\) and for which \(t_r^\prime\) measures the time to death. This estimate is the distribution, unrestricted as to form, which maximizes the likelihood of the observations.
Other estimates that are discussed are the actuarial estimates (which are also products, but with the number of factors usually reduced by grouping); and reduced-sample (RS) estimates, which require that losses not be accidental, so that the limits of observation (potential loss times) are known even for those items whose deaths are observed. When no losses occur at ages less than t the estimate of P(t) in all cases reduces to the usual binomial estimate, namely, the observed proportion of survivors.

A new method is presented to get an insight into univariate time series data. The problem addressed is how to identify patterns and trends on multiple time scales (days, weeks, seasons) simultaneously. The solution presented is to cluster similar daily data patterns, and to visualize the average patterns as graphs and the corresponding days on a calendar. This presentation provides a quick insight into both standard and exceptional patterns. Furthermore, it is well suited to interactive exploration. Two applications, numbers of employees present and energy consumption, are presented

IEC Publication 60599 provides a coded list of faults detectable by dissolved gas analysis (DGA): PD = partial discharges; D1 = discharges of low energy; D2 = discharges of high energy; T1 = thermal faults of temperature < 300°C; T2 = thermal faults of temperature 300°C < T < 700°C; T3 = thermal faults of temperature > 700°C. The IEC TC10 databases of DGA results corresponding to faults identified by visual inspection of faulty transformers in service have been presented in a previous paper (see IEEE Elec. Insulation Mag., vol. 17, no. 2, p.31-41, 2001). The present paper reviews these DGA results in a more user-friendly graphical form. It also reviews the DGA results of laboratory models attempting to simulate these faults, as published in the scientific literature or technical reports. The specific case of on-load tap changers (OLTC) is reviewed much more extensively, and separately, since DGA interpretation in this case must take into account the large background of residual gases resulting from the normal current-breaking operation of the OLTC. Particular attention is also given to DGA results related to PDs and low-temperature thermal faults.

The use of dissolved gas analysis (DGA) to monitor the in-service behavior of transformers is discussed. Sampling techniques are briefly considered, and two commercial hydrogen-in-oil detectors are described. The first allows the hydrogen concentration to be measured at intervals of a few hours by a portable gas collector that can be connected to semipermeable tubes. Continuous remote monitoring from the substation is possible with the second model, which uses a fuel-cell-type detector. The use of DGA for fault diagnosis is examined, and acceptable gas levels are indicated. The use of expert systems to facilitate decision making on the basis of DGA results is discussed, as is international cooperation in sharing data and experience and reaching agreement on methods of analysis and interpretation. Further applications of DGA are indicated.< >

DGA Survival Analysis for Power Transformers

- J J Dukarm

J. J. Dukarm, "DGA Survival Analysis for Power Transformers," TJ|H2b TechCon Canada
2012, Montreal, Canada, 28 Sep 2012.