Assessment of moisture content in power transformer based on traditional techniques and Adaptive neuro-fuzzy interference system

P. Sekatane, J. Jordaan, P. Bokoro
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引用次数: 0

Abstract

The use of traditional measurement techniques for condition monitoring of power transformer is still a common practice in the power industry. These techniques have proven to be unreliable as a result of sampling and analysis errors. Given the unequal moisture distribution between cellulose and mineral oil in power transformers, the dryness correlation between the two liquid insulators is not always accurate. The aim of this work is to advice the manufacturer of power transformers to continue use the Dew point measurement or move to the modern methods, like frequency domain spectroscopy (FDS). Dew point measurements have been used to estimate the dryness of power transformers, model the data by adaptive neuro-fuzzy inference system (ANFIS) as is proven to solve complex data and validate the results by Frequency Domain spectroscopy (FDS).
基于传统方法和自适应神经模糊干扰系统的电力变压器含水率评估
在电力工业中,使用传统的测量技术对电力变压器进行状态监测仍然是一种普遍的做法。由于采样和分析错误,这些技术已被证明是不可靠的。由于电力变压器中纤维素和矿物油的水分分布不均匀,两种液体绝缘子的干度相关性并不总是准确的。这项工作的目的是建议电力变压器制造商继续使用露点测量或转向现代方法,如频域光谱(FDS)。露点测量被用来估计电力变压器的干度,用自适应神经模糊推理系统(ANFIS)对数据建模,并被证明可以解决复杂的数据,用频域谱(FDS)验证结果。
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