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TRANSFORMER RELIABILITY AND DISSOLVED-GAS ANALYSIS
Abstract
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.
... Statistical survival analysis performed with DGA data including failure cases provides an objective way to estimate the risk associated with fault gas production by a transformer. It is shown in [6] that the risk of near-term failure is not necessarily higher for higher gas concentrations. ...
... Applying reliability statistics to NEI-HC and NEI-CO, one can derive a hazard rate curve as illustrated in Fig. 2 of [6]. The hazard rate curve represents the the percent of failures per additional unit of NEI, which can be interpreted as the risk of short-term failure associated with active gassing. ...
This paper provides an example of how to improve DGA fault severity interpretation using screening test statistics and optimization curves with transformer failure data. Initial comparison of ‘cookbook’ fault severity methods in Draper & Dukarm 2021 [1] showed that IEEE C57.104-2019 had fallen short of its potential by not having a clearly defined status code higher than a 3. The IEEE guidelines provide for an undefined ‘Extreme DGA’ category which we attempt to define in this paper using failure data. We found that the overall performance of IEEE 2019 as a predictor of near-term transformer failure can be considerably improved by just multiplying the status code 3 limits by a factor of seven. Nevertheless, this simple modification to IEEE 2019 is not able to achieve as high of a positive predictive value or diagnostic odds ratio as PFS (IEC 60599–2015 / CIGRE TB 771) or Reliability-based DGA. Further algorithmic changes using population data with actual failure cases will be required to improve the performance of future DGA fault severity interpretation methods.
This is a feature article in the Fall 2019 issue of NETA World Journal. By permission of NETA World, a PDF of the article is downloadable here.
Abstract: Building on five decades of industry experience and data collection, plus the wide availability of computers, a re-examination of transformer DGA from the point of view of physical chemistry and advanced statistics is breathing new life into the subject. This article describes important advances. An example shows how they can improve fault detection and provide new risk assessment information.
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
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.
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.
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