Search

The useful life of a power transformer is linked to the integrity of its solid (cellulosic) insulation. The insulation deterioration is time dependent and is influenced by varying temperatures, moisture, oxygen, oil conditions and loading profiles. Accordingly, once the solid insulation state is compromised, its dielectric and mechanical properties are not recoverable. Thus, longevity in service of a power transformer can only be achieved by incessant monitoring and assessing the credibility of its insulation system. The tensile strength of the solid insulation can be indicated by determining the degree of polymerization of the paper. Consequently, the degree of polymerization is a key indicator of the insulation status that correlates well with the transformer remnant life. Since degree of polymerization measurement is an intrusive test involving transformer disassembling, utilities have opted to the use of furans concentration in the oil as a way of determining the degree of polymerization of transformer solid insulation. However, these furans and degree of polymerization correlations are based on mathematical models. This paper introduces an adaptive neuro fuzzy model to estimate the degree of polymerization of a mineral oil-immersed power transformer based on furan content and CO 2 /CO gas concentration ratio in the insulation system. Practical data from numerous power transformers of different lifespans subjected to different operating regimes have been used to validate the accuracy and credibility of the established adaptive neuro fuzzy model. Compared to the conventional models, the results show that the proposed model is effective in estimating the degree of polymerization of transformer solid insulation.

The performance and useful life expectancy of a power transformer generally depends on the condition of oil-impregnated paper insulation system that has a finite life span. End of technical life of a power transformer is reached when some of the insulation diagnostic characteristics drop to a level where further operation is unacceptable. Thus, recurrent evaluation of the transformer insulation system diagnostics enhances better estimation of transformer residual life. However, being a function of voluminous conflicting attributes, the design feature, changing operating conditions, and different maintenance strategies, a flawless affirmation of the criteria governing the transformer remnant life estimation is a very complex issue. Power transformer operational condition that can be mirrored by criticality of ageing and fault stress levels has substantial impact on its residual life estimation. This paper explores the possibility of developing a remnant life estimation and asset management decision model based on diagnostics of transformer insulation characteristics. The proposed model is developed upon the integration of the fuzzy logic diagnostic tool and the fuzzy logic remnant life mapping model. The diagnostic details are signified by fuzzy rules for an accurate assessment to reach a defined outcome in terms of fault stress level and energy of faulting, ageing stresses and transformer condition. A multicriteria analysis was used in developing the model whereby the outcome of the model is achieved by including the combined effect of individual sub outcomes using fuzzification of all the employed attributes. The developed remnant life model is validated using data collected from several in-service mineral oil immersed transformers. Results show that the proposed model provides an approximate but practical means of remnant life estimation and asset management decision.

The health index scheme can be the most fundamental tool that unifies all transformer condition status information into a singular outcome, thereby enhancing the power transformer asset management and life longevity strategies. This study aims at establishing a multiple parameter-dependent transformer health index estimation model cascaded with a fuzzy logic inference system. This strategy is centered on the effect of dynamic loading regime, varying hotspot temperatures and multiple attesting results of the insulation system. Furthermore, a nonintrusive degree of polymerization (DP) model based on furans and carbon oxide ratios as DP pointers is also factored in developing the health index model. The general outcome of the health index depends on entirely considered elements, but not on any isolated attribute. Data obtained from in-service transformers were used to validate the proposed model. The outcome of the model mirrors the practical condition of the evaluated transformers. Therefore, the proposed health index model can be a vital tool to asset managers and power utilities.

Decision making on transformer insulation condition based on the evaluated incipient faults and aging stresses has been the norm for many asset managers. Despite being the extensively applied methodology in power transformer incipient fault detection, solely dissolved gas analysis (DGA) techniques cannot quantify the detected fault severity. Fault severity is the core property in transformer maintenance rankings. This paper presents a fuzzy logic methodology in determining transformer faults and severity through use of energy of fault formation of the evolved gasses during transformer faulting event. Additionally, the energy of fault formation is a temperature-dependent factor for all the associated evolved gases. Instead of using the energy-weighted DGA, the calculated total energy of related incipient fault is used for severity determination. Severity of faults detected by fuzzy logic-based key gas method is evaluated through the use of collected data from several in-service and faulty transformers. DGA results of oil samples drawn from transformers of different specifications and age are used to validate the model. Model results show that correctly detecting fault type and its severity determination based on total energy released during faults can enhance decision-making in prioritizing maintenance of faulty transformers.