Dissolved gas analysis is an effective method for the early detection of incipient fault in power transformers. To improve the capability of interpreting the result of dissolved gas analysis, an artificial immune network classification algorithm (AINC), inspired by the natural immune system that is able to respond to an almost unlimited multitude of foreign pathogens, is proposed in this paper. The immune network system describes the complex interaction of antibodies and antigens in virtue of some immune mechanisms, such as immune learning, immune memory, etc. AINC mimics these adaptive learning and defense mechanisms to respond to fault samples of power transformers. Consequently, AINC can find a limited number of antibodies representing all fault samples distributing structures and features, which helps to realize dynamic classification. This proposed AINC algorithm has been tested by many real fault samples, and its results are compared with those of IEC ratio codes and artificial neural networks, which indicates that the proposed approach has remarkable diagnosis accuracy, and with it multiple incipient faults can be classified effectively

Artificial Immune Network Classification Algorithm for Fault Diagnosis of Power Transformer

This paper presents a new technique based on vibration measurement to diagnose power transformers, which is called the onload current method (OLCM). It can acquire the fundamental frequency component of the core vibration signal without running the transformer at the open-circuit condition. The diagnostic method adopted and the experimental test results are reported. Tests have been performed at normal operating conditions in both the manufactory and the laboratory. With the tests performed in the manufactory, the vibration characteristics of transformer windings and core are described, and then the principle of OLCM is introduced. To verify the validity of OLCM, the laboratory tests are conducted which relates the transformer vibrations to the simulative fault. But the presented method was verified on a 5-kVA transformer which is a very far cry from an actual power transformer. It needs to be developed further before OLCM can be used effectively on the transformer in the field

Research on extraction technique of transformer core fundamental frequency vibration based on OLCM

A different approach to the study of surface tracking reveals a new view of the oil-pressboard interface and suggests a link between the electric double layer and the boundary layer.

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Tracking and surface discharge at the oilߝpressboard interface

This paper presents experimental research of creepage discharge on insulation barriers in power transformers. Using point-to-plate electrode configurations under AC voltages creepage discharge is studied for both single flashover breakdown and partial discharge induced failure modes. It is confirmed that the dielectric strength of oil gap will not be reduced with introducing dry new pressboard surface into the oil gap, indeed the flashover breakdown voltage is hardly compromised by introducing aged pressboard with up to 3% moisture. However, increased moisture content in pressboard reduces partial discharge inception voltage (PDIV) significantly, i.e. ~30% PDIV reduction for pressboard of ~3% moisture as compared with dry pressboard. More importantly, high moisture contents in pressboard increase PD activities in oil pores which allow gasses to be trapped inside to develop gaseous channels which eventually lead creepage discharge to breakdown.

Creepage discharge on insulation barriers in aged power transformers

Power transformers are a central component in the field of energy distribution and transmission. The early recognition of incipient faults in operating transformers is substantially cost effective by lessening impromptu blackouts. A standout amongst the most responsive and dependable strategies utilized for assessing the health of oil filled electrical equipment is dissolved gas analysis (DGA). Nowadays, there is an expanding requirement for better nonintrusive diagnostic and online monitoring tools to survey the internal state of the transformers. Chemical sensors are viewed as a key innovation for condition monitoring of transformer health, coordinating the non-invasiveness with typical sensor features, such as cost, usability, portability, and the integration with the data networks. Low-cost chemical sensors-based DGA techniques are expected to drastically augment the diagnostic abilities empowering the deployment on a broader range of oil filled power assets. The recent development involves both specific sensors designed to detect individual dissolved gas in transformer oil and non-specific sensors, operated in near ambient conditions, with the potential to be applied in a DGA system. In this paper, general background and operating guidelines of DGA are presented to address the origin of the gas formation, methods for their detection and the interpretation of the results by data analytics. The recent significant interest and advancements in chemical sensors to DGA applications are reviewed. Future research perspectives and challenges for the development of novel DGA chemical sensors are also discussed.

Chemical Sensing Strategies for Real-Time Monitoring of Transformer Oil: A Review

Condition-based monitoring of power transformer insulation should center on the prediction of a substantial drop in the dielectric safety margin under the impact of moisture, oil by-products, contaminating particles, paper insulation aging and partial discharge activity. A functional failure model of power transformer insulation and possible effective methods of the insulation condition assessment are discussed based on practical experience.

V. Sokolov

Papers

Effective methods of assessment of insulation system conditions in power transformers: a view based on practical experience

Physical experiment in problems of building aerodynamics

Traction electric drive based on a valve motor with microprocessor control