Compensating the Impact of Residual Energy on Time Domain Dielectric Response Using Time-Varying Model

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Chandra Madhab Banerjee;Deepak Mishra;Arijit Baral;Sivaji Chakravorti
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Abstract

Analysis of polarization and depolarization current (PDC) is a widely accepted method for diagnosing power transformer insulation. The accuracy of such techniques depends significantly on the premise that measurement of insulation response has been done correctly. During field measurement, equipment sometimes fails to record proper current, even after applying dc charging voltage. In such cases, the polarization current profile gets affected by residual energy. Recently, a conventional Debye model (CDM) based approach has been reported to solve the issue. The CDM-based approach relies on identifying the correct time-invariant branch parameters, by minimizing the deviation between measured and estimated value of several performance parameters, through an iterative technique. This coupled with the presence of multiple branches in CDM makes the overall method time consuming and computationally intensive. This article proposes a non-iterative methodology, based on a model with time-varying parameters that is capable of achieving the same result. This not only saves time but also reduces overall data post processing and computation burden required for diagnosis. Performance of the proposed method is tested on data obtained from the oil-paper sample and several real-life power transformers. The proposed method is observed to be capable of estimating paper-moisture (using affected data) with more than 95% accuracy for in-service units. The time required for achieving this is found to be approximately 1/third of that required by CDM-based technique (which could provide results with maximum 90% accuracy).
利用时变模型补偿残余能量对时域介电响应的影响
极化和去极化电流分析(PDC)是一种被广泛接受的电力变压器绝缘诊断方法。这种技术的准确性在很大程度上取决于绝缘响应测量是否正确。在现场测量中,即使施加直流充电电压,设备有时也不能记录正确的电流。在这种情况下,极化电流分布受到剩余能量的影响。最近,一种传统的基于Debye模型(CDM)的方法被报道来解决这个问题。基于cdm的方法依赖于通过迭代技术,通过最小化几个性能参数的测量值和估计值之间的偏差,来识别正确的时不变分支参数。这与CDM中多个分支的存在相结合,使得整个方法既耗时又需要大量计算。本文提出了一种基于具有时变参数的模型的非迭代方法,该方法能够实现相同的结果。这不仅节省了时间,而且减少了诊断所需的整体数据后处理和计算负担。用油纸样品和实际电力变压器的数据对该方法的性能进行了测试。所提出的方法被观察到能够估计纸张湿度(使用受影响的数据),在服役单位的准确度超过95%。实现这一目标所需的时间大约是基于cdm技术所需时间的1/ 3 (cdm技术可以提供最高90%的准确度)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
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