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首页> 外文期刊>Electrical Insulation Magazine, IEEE >Adaptive neuro-fuzzy inference system approach for simultaneous diagnosis of the type and location of faults in power transformers
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Adaptive neuro-fuzzy inference system approach for simultaneous diagnosis of the type and location of faults in power transformers

机译：自适应神经模糊推理系统方法，可同时诊断电力变压器故障的类型和位置

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Electrical, mechanical, and thermal stresses can degrade the quality of the insulation in power transformers, causing faults [1]. Several methods are used for fault diagnosis in transformers, e.g., dissolved gas analysis (DGA), measurement of breakdown voltage, and tan ??????, pollution, sludge, and interfacial tension tests [2]. Of these, DGA is the most frequently used. Thermal and electrical stresses result in fracture of the insulating materials and the release of several gases. Analysis of these gases may provide information on the type of fault. Various standards have been suggested for the identification of transformer faults based on the ratio of dissolved gases in the transformer oil, e.g., International Electrotechnical Commission (IEC) standards [3]?????????[7], and these standards has been quoted in many papers, e.g., [8]?????????[15]. However, they are incomplete in the sense that, in some cases, the fault cannot be diagnosed or located accurately. Intelligent algorithms, e.g., wavelet networks [16], neuro-fuzzy networks [17], [18], fuzzy logic [8], [12], and artificial neural networks (ANN) [2], [9], [10], [19], [20] have been used to improve the reliability of the diagnosis. In these algorithms, the type of fault is diagnosed first, and the fault is then located using the ratio of the concentrations of CO2 and CO dissolved in the transformer oil [21], [22]. The algorithms are not entirely satisfactory. The wavelet network has high efficiency but low convergence, the fuzzy logic method has a limited number of inputs and, in some cases, it is very difficult to derive the logic rules, and the ANN need reliable training patterns to improve their fault diagnosis performance. In this paper, we present a new method for simultaneous diagnosis of fault type and fault location. It uses an adaptive neuro- fuzzy inference system (ANFIS) [23]?????????[27], based on DGA. The ANFIS is first ?????????trained????????? in accordance with IEC 599 [3], so that it acquir- s some fault determination ability. The CO2/CO ratios are then considered additional input data, enabling simultaneous diagnosis of the type and location of the fault. The results obtained by applying it to six transformers are presented and compared with the corresponding results obtained using ANN and some other standards and methods.
机译：电气，机械和热应力会降低电力变压器的绝缘质量，从而引起故障[1]。有几种方法可用于变压器的故障诊断，例如，溶解气体分析（DGA），击穿电压和tan tan的测量，污染，污泥和界面张力测试[2]。其中，DGA是最常用的。热应力和电应力会导致绝缘材料破裂并释放出几种气体。对这些气体的分析可能会提供有关故障类型的信息。已经提出了各种基于变压器油中溶解气体的比例来识别变压器故障的标准，例如国际电工委员会（IEC）标准[3] ?????? [7]，以及这些标准。在许多论文中都引用了标准，例如[8] ????????? [15]。但是，从某种意义上说，它们是不完整的，在某些情况下，无法诊断或准确定位故障。智能算法，例如小波网络[16]，神经模糊网络[17]，[18]，模糊逻辑[8]，[12]和人工神经网络（ANN）[2]，[9]，[10 ]，[19]，[20]已被用于提高诊断的可靠性。在这些算法中，首先诊断故障的类型，然后使用溶解在变压器油中的CO2和CO的浓度之比来定位故障[21]，[22]。该算法并不完全令人满意。小波网络效率高，收敛性低，模糊逻辑方法输入数量有限，在某些情况下，很难推导出逻辑规则，人工神经网络需要可靠的训练模式来提高故障诊断性能。在本文中，我们提出了一种同时诊断故障类型和故障位置的新方法。它使用了基于DGA的自适应神经模糊推理系统（ANFIS）[23] ?????? [27]。 ANFIS是第一个经过培训的????????????符合IEC 599 [3]，因此具有一定的故障确定能力。然后，将CO2 / CO比率视为附加的输入数据，从而可以同时诊断故障的类型和位置。介绍了将其应用于六个变压器的结果，并将其与使用ANN和其他一些标准和方法获得的相应结果进行了比较。

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