Figure - available from: IET Science, Measurement & Technology
This content is subject to copyright. Terms and conditions apply.
Remaining life probability density curve at different monitoring time without during parameter updating
Source publication
The service life of a transformer depends on the ageing degree of the internal oil–paper insulation. In order to reliably predict the remaining service life of oil–paper insulation for a single transformer, this study proposes a method based on monitoring the performance degradation data of oil–paper insulation. In this study, the remaining life pr...
Similar publications
We confirm a long-standing conjecture concerning shear-induced chaos in stochastically perturbed systems exhibiting a Hopf bifurcation. The method of showing the main chaotic property, a positive Lyapunov exponent, is a computer-assisted proof. Using the recently developed theory of conditioned Lyapunov exponents on bounded domains and the modified...
At present, a single-valued deterministic simulation method is preferred choice for numerical wind speed forecasts. However, it remains difficult to meet the actual needs of both wind farms and grid systems, mainly owing to unavoidable uncertainties. The development of skilled numerical forecasting methods has become a critical issue and major chal...
The problem of the Taylor-Ito and Taylor-Stratonovich expansions of the Ito stochastic processes in a neighborhood of a fixed moment of time is considered. The classical forms of the Taylor-Ito and Taylor-Stratonovich expansions are transformed to the four new representations, which includes the minimal sets of different types of iterated Ito and S...
Recursive prediction error method is one of the main tools for analysis of controlled auto-regressive systems with quantised output. In this study, a recursive identification algorithm is proposed based on the auxiliary model principle by modifying the standard stochastic gradient algorithm. To improve the convergence performance of the algorithm,...
Many multiagent dynamics, including various collective dynamics occurring on networks, can be modeled as a stochastic process in which the agents in the system change their state over time in interaction with each other. The Gillespie algorithms are popular algorithms that exactly simulate such stochastic multiagent dynamics when each state change...
Citations
... In recent decades, the paper-oil insulation system has been a common key component in the development of monitoring techniques used to diagnose the health and estimate the useful life of power transformers [8][9][10]. ...
Starting from the current need for the safety of energy systems, in which power transformers play a key role, the study of the health of power transformers in service is a difficult and complex task, since the assessment consists of identifying indicators that can provide accurate data on the extent of degradation of transformer components and subcomponents, in order to establish a model for predicting the remaining life of transformers. Therefore, this paper proposes a model for assessing the remaining service life by diagnosing the condition of the transformer based on the health index (HI) obtained from a multi-parameter analysis. To determine the condition of power transformers, a number of methods are presented based on the combination of the combined Duval pentagon (PDC) method and ethylene concentration (C2H4) to determine the fault condition, the combination of the degree of polymerisation (DP) and moisture to determine the condition of the cellulose insulation and the use of the oil quality index (OQIN) to determine the condition of the oil. For each of the classification methods presented, applications based on machine learning (ML), in particular support vector machine (SVM), have been implemented for automatic classification using the Matlab development environment. The global algorithmic approach presented in this paper subscribes to the idea of event-based maintenance. Two case studies are also presented to validate SVM-based classification methods and algorithms.
... It is reported that the transformer lifespan decreases with the increase in water content in paper [34] but this also depends on the temperature and oxygen content in the liquid insulation [34]. There are many approaches reported in the literature to estimate the lifespan of the transformer, considering the aforementioned factors [35,36]. ...
... To remove moisture from paper, several methods have been developed and accepted by the industry [28]. The most popular techniques include hot oil circulation, which consists of circulating the hot oil with many passes; vacuum drying; and hot oil spray [28,35,36]. There are also low-frequency drying and stationary molecular sieve methods for the drying of oil-paper insulation [28]. ...
The degradation of the insulation system in liquid-filled power transformers is a serious concern for electric power utilities. The insulation system's ageing is accelerated by moisture, acids, oxidation products, and other decay particles (soluble and colloidal). The presence of these ageing by-products is detrimental to the insulation system and may further lead to premature ageing and serious consequences. The ageing mechanisms of oil-paper insulation are complex, highly interrelated , and strongly temperature-dependent. The operating temperature of the transformer insulating system has a direct relationship with the loading profile. The major aspect that is witnessed with the fluctuating temperatures is moisture migration and subsequent bubble evolution. In other words, gas bubbles evolve from the release of water vapor from the cellulosic insulation wrapped around the transformer windings. The models presented in the existing standards, such as the IEC Std. 60076-7:2018 and the IEEE Std. C57.91:2011, are mainly based on the insulation temperature, which acts as a key parameter. Several studies have investigated the moisture dynamics and bubbling phenomenon as a function of the water content in the paper and the state of the insulation system. Some studies have reported different prototypes for the estimation of the bubble inception temperatures under selected conditions. However, there are various attributes of the insulation system that are to be considered, especially when expanding the models for the alternative liquids. This paper reviews various evaluation models reported in the literature that help understand the bubbling phenomenon in transformer insulation. The discussions also keep us in the loop on the estimation of bubbling behavior in alternative dielectric liquids and key attributable factors for use in transformers. In addition, useful tutorial elements focusing on the bubbling issue in transformers as well as some critical analyses are addressed for future research on this topic.
... The possibility of failure and the complexity of fault types is, therefore, an increasing concern among manufacturers. Sudden failure of equipment may lead to the interruption of the entire production process and cause significant economic losses [1][2][3]. Therefore, real-time residual life prediction of key components in mechanical equipment is of great practical significance. The existing residual life prediction methods include physical, expert knowledge, and data-driven prediction models. ...
The existence of abrupt change points in mechanical equipment degradation leads to inaccuracies in the prediction of its residual life. We propose a real-time residual life prediction method based on kernel density estimation (KDE) considering the influence of abrupt change points. First, a non-parametric cumulative sum method is used to detect abrupt change points in the degradation process. Then, integral mean square error is used to determine the abrupt change in the sample number that affects the accuracy of KDE. The weight coefficient is adaptively allocated according to the change in sample numbers relative to the minimum sample number before and after the abrupt change point in real-time monitoring. This method considers abrupt change states in the degradation process and uses KDE, which does not make model structure assumptions or parameter estimations for the degradation process. Finally, the effectiveness of this method is verified using the gear wear degradation data and compared with the real-time residual life prediction method based on KDE without considering the impact of the abrupt change point. The results show that this method can track the dynamic changes of a system more quickly and improve the accuracy of real-time residual life prediction.
Accurate assessment of Remaining Useful Lifetime (RUL) of power transformers plays an essential role in improving the reliability of power grid while reducing operating costs. A dynamical RUL prediction technique is proposed in this article through analyzing the individual difference by multi-dimensional fusion of condition monitoring data. Firstly, the on-site Dissolved Gas Analysis (DGA) measurements and the calculated Degree of Polymerization (DP) are fused with the method of Analytic Hierarchy Process (AHP) and Variable Weight Principle (VWP) to construct the power transformer Life Correlation Index (LCI). Secondly, the parameters of Wiener model are updated iteratively by Bayesian rule and Expectation Maximization (EM) algorithm, to obtain the distribution and prediction of residual transformer lifetime. The residual lifetimes of four 500 kV power transformers in operation are calculated, and the predicted remaining lifetimes are 24.73 years, 30.66 years, 2.79 years, and 0.07 years respectively. The effectiveness and reliability are demonstrated by the worst transformer case with the predicted RUL of 0.07 years, in which a short circuit fault of that transformer happened immediately after power-off maintenance. In particular, this method is able to predict the residual lifetime of transformers in a dynamic way, which provides significant practical implications for the assessment management of power transformers.
The relationship between the permittivity of a two-phase porous composite material and its structure forms the basis for the adjustment and analysis of the permittivity of the porous material. However, existing calculation models exhibit significant errors at low frequencies. A wide-frequency equivalent model is proposed in this paper based on the geometrical structural characteristics of porous materials, considering the effects of interface polarization and dielectric relaxation process. The relationship between the two-phase porosity material and the dielectric response of its framework and filling medium in a wide-frequency range can be calculated. Furthermore, the modified Cole–Cole model is adopted to analyze the dielectric spectrum characteristics of cellulose and aramid, thereby exhibiting the effects of polar bonds in the molecular structure and impurity ions in the material on the polarization strength and polarization process. The results obtained from this study can provide key parameters and a basis for the analysis of the polarization process of two-phase combined porous materials and the design of low-permittivity insulating paper.
