A Hybrid Modelling And Evidence-based Fault Diagnosis Approach To Power Transformer Winding Deformation Detection

电力变压器绕组变形检测的混合建模和基于证据的故障诊断方法

• 批准号: EP/G049459/1
• 负责人: Wenhu Tang
• 金额: $ 20.15万
• 依托单位: University of Liverpool
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2010
• 资助国家: 英国
• 起止时间: 2010 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FG049459%2F1
• 关键词: Hybrid; Modelling; Evidence; based; Fault

Power transformers are designed to withstand the mechanical forces arising from various in-service events, such as over-voltage and lightning, which may cause deformation or displacement of winding. Among various techniques applied to power transformer fault diagnosis, frequency response analysis (FRA) can give an indication of winding deformation faults without expensive and interruptive operations of opening a transformer tank, which can minimise the impact on system operation and loss of supply to customers and consequently save millions of pounds in timely maintenance. However, in industrial practice, FRA is always used as a comparative method, by comparing a test frequency response with a reference set, which cannot provide an insight understanding of transformer internal faults. A range of research activities have been undertaken to utilise FRA in the development winding models but with limitations, such as too complicated models, large computation time and inaccurate responses in the high frequency range between 1MHz and 10MHz. The proposed research is to build on the experience already gained at Liverpool and to develop an accurate winding model and a reliable fault diagnosis approach. A new hybrid winding model will be developed by modifying the analytical approach and results of transformer winding analysis obtained by Rudenberg for each disc, and subsequently connecting the travelling wave equation of each disc in a form of Multi-conductor Transmission Line (MTL) model. This can significantly reduce the order of the model yet with good modelling accuracy in the high frequency range, which allows access to the current and voltage at any desired turns of a winding. The electrical parameters of the hybrid model will be estimated with the finite element method (FEM), and further identified with evolutionary algorithms based on actual FRA measurements. The characteristic signatures between particular winding faults and winding parameters will be derived, which can be employed to detect and distinguish winding deformation faults. Then, the simulation of the hybrid model will be used to extract high frequency fault fingerprints of FRA for improving the detection of small winding changes, which will be further examined and verified through laboratory studies. For typical winding fault diagnosis, both the quantitative and qualitative judgements are generally considered, which can be treated as evidence and are often incomplete and imprecise. The Evidential Reasoning (ER) algorithm is very suitable for combining such evidence with a firm mathematical foundation. In this project, an evidence-based fault diagnosis system will be constructed to aggregate diagnosis information and deal with uncertainties for reliable winding fault diagnosis. The work is to be carried out as a collaborative project between the University of Liverpool, OMICRON and NG, bringing together academic and industrial expertise in the field of transformer test, modelling and fault diagnosis. The outcome of the proposed research will be the new hybrid winding model and the evidence-based winding fault diagnosis system. The new approach aims to improve the fundamental understanding of multi-frequency signal propagation across a winding, which will allow extracting fault fingerprints in both the low and high frequency ranges and provide new diagnostic rules for early fault detection and location. The extracted high frequency fault fingerprints will provide a feasible solution for early fault detection, which can assist a FRA test kit manufacturer, e.g. OMICRON, in fully understanding FRA and improving test kit precision. The developed evidence-based system for winding fault diagnosis can be a useful decision support tool for utility companies, e.g. NG, for reliable fault diagnosis yet with high efficiency, when processing numerous FRA records.

电力变压器的设计能承受各种运行中的事故所产生的机械力，例如过电压和闪电，这些事故可能导致绕组变形或移位。在应用于电力Transformer故障诊断的各种技术中，频率响应分析（FRA）可以给出绕组变形故障的指示，而无需打开Transformer箱的昂贵且中断的操作，这可以最小化对系统操作的影响和对客户的供电损失，从而在及时维护中节省数百万英镑。然而，在工业实践中，FRA总是被用作比较方法，通过将测试频率响应与参考集合进行比较，这不能提供对Transformer内部故障的深入理解。已经开展了一系列研究活动，以利用FRA开发绕组模型，但存在局限性，例如模型过于复杂，计算时间长以及在1MHz和10MHz之间的高频范围内响应不准确。拟议的研究是建立在利物浦已经取得的经验，并制定一个准确的绕组模型和可靠的故障诊断方法。通过修改Rudenberg对每个圆盘的Transformer绕组分析的分析方法和结果，并随后将每个圆盘的行波方程以多导体传输线（MTL）模型的形式连接起来，将开发出一种新的混合绕组模型。这可以显着降低模型的阶数，但在高频范围内具有良好的建模准确性，从而允许获取绕组任何所需匝数处的电流和电压。混合模型的电气参数将估计与有限元法（FEM），并进一步确定与进化算法的基础上实际FRA测量。将推导出特定绕组故障与绕组参数之间的特征量，用于检测和区分绕组变形故障。然后，混合模型的仿真将被用来提取高频故障指纹的FRA提高检测小绕组变化，这将进一步检查和验证，通过实验室研究。对于典型的绕组故障诊断，通常既考虑定量判断，又考虑定性判断，这些判断可以作为证据，但往往是不完整和不精确的。证据推理（ER）算法非常适合将此类证据与坚实的数学基础相结合。本计画将建构一个以证据为基础的故障诊断系统，以整合诊断资讯并处理不确定性，以提供可靠的绕组故障诊断。这项工作将作为利物浦大学、OMICRON和NG之间的合作项目进行，汇集了Transformer测试、建模和故障诊断领域的学术和工业专业知识。本研究的成果将是新的混合绕组模型和基于证据的绕组故障诊断系统。新方法旨在提高对绕组中多频信号传播的基本理解，这将允许在低频和高频范围内提取故障指纹，并为早期故障检测和定位提供新的诊断规则。提取的高频故障指纹将为早期故障检测提供可行的解决方案，这可以帮助FRA测试套件制造商，例如OMICRON，充分理解FRA并提高测试套件精度。开发的基于证据的系统绕组故障诊断可以是一个有用的决策支持工具，为公用事业公司，如NG，可靠的故障诊断，但具有高效率，当处理大量的FRA记录。

项目成果

Partial discharge location using a hybrid transformer winding model with morphology-based noise removal

• DOI: 10.1016/j.epsr.2013.03.003
• 发表时间: 2013-08
• 期刊: Electric Power Systems Research
• 影响因子: 3.9
• 作者: T. Ji;W. Tang;Qinghua Wu