• Energy Research
• natural sciences close

Delivery of the Smart Grid is a topic of considerable interest within the power industry in general, and the IEEE specifically. This paper presents the smart grid landscape as seen by the IEEE Dielectrics and Electrical Insulation Society (DEIS) Technical Committee on Smart Grids. We define the various facets of smart grid technology, and present an examination of the impacts on dielectrics within power assets. Based on the trajectory of current research in the field, we identify the implications for asset owners and operators at both the device level and the systems level. The paper concludes by identifying areas of dielectrics and insulation research required to fully realize the smart grid concept. The work of the DEIS is fundamental to achieving the goals of a more active, self-managing grid.

Interest has risen among utilities in using natural ester (NE) insulating oils in transformers as a substitute for conventional mineral oil. However, present understanding on aging behaviour of NE-paper composite insulation system and knowledge on application of existing condition monitoring tools for NE-based insulation are inadequate. This limits the cost effective and reliable field applications of NE insulating oil. To pave the way the application of NE-based insulation in transformers, a systematic study has been performed to compare the aging behaviour of transformer grade pressboard (PB) impregnated in NE and conventional mineral oil. Applicability of a number of chemical and physical parameters, including acidity value, dielectric dissipation factor (DDF), viscosity, and colour for assessing the quality of NE insulating oil is also discussed in this paper. Comparisons are made based on the limiting values provided in the related IEEE Standard and properties of mineral oil under similar aging conditions.

This paper describes the investigation of the insulation condition of a number of power and distribution type transformers of different manufacturing dates and with different operating histories. Return voltage measurements were conducted for these transformers. Effects of moisture and ageing on the oilpaper insulation of these transformers were investigated and results are presented in this paper. Moisture in oil samples at known temperature was measured for these transformers. Previously accelerated ageing experiments were performed on paper wrapped insulated conductors in different environments over a temperature range of 115 to 145/spl deg/C. Results from the measurements on transformers are compared with those of the accelerated aged samples and are described in this paper.

Condition monitoring and diagnosis have become an essential part of power transformer asset management. A variety of online and offline measurements have been performed in utilities for evaluating different aspects of transformers' conditions. However, properly processing measurement data and explicitly correlating these data to transformer condition is not a trivial task. This paper proposes an intelligent framework for condition monitoring and assessment of power transformer. Within this framework, various signal processing and pattern recognition techniques are applied for automatically denoising sensor acquired signals, extracting representative characteristics from raw data, and identifying types of faults in transformers. This paper provides case studies to demonstrate the effectiveness of the proposed framework and techniques for power transformer asset management. The hardware and software platform for implementing the proposed intelligent framework will also be presented in this paper.