Diagnosis of Stator Winding Insulation of Large Generator Based on Partial Discharge Measurement

Yang, Bo; Chen, Xiaolin; Cheng, Yonghong; Song, Jiancheng; Xie, Hui

doi:10.1109/tec.2006.874223

Cited by 26 publications

(11 citation statements)

References 12 publications

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“…The shapes of max ( ), ( ), ( ), and three-dimensional distribution ( , ) changed with aging time. These distributions have characteristic shapes, which vary with the type of the defects [19]. Experience has shown that the shape belonging to the positive half of the HV sine wave differs much from that of the negative half, so that their characteristic shapes are available for recognition purposes.…”

Section: Phase Resolved Pd Analysismentioning

confidence: 99%

Condition Evaluation of Large Generator Stator Insulation Based on Partial Discharge Measurement

Pan

Chen

et al. 2013

Advances in Mechanical Engineering

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The large generator faults are mostly from the stator insulation, which is aged by exposure to a combination of thermal, electrical, mechanical, and harsh environment stresses. Condition evaluation of stator insulation is an important measure of ensuring the safe operation and extending the remaining life of large generator. In this paper, a diagnosis method based on the partial discharge (PD) measurement was used for the condition evaluation of the stator insulation. The statistical parameters of partial discharge phase resolved distribution were proposed to assess the aging condition of stator insulation. A partial least square (PLS) approach was used to explore and extract relationships between the statistical parameters of partial discharge distribution with physical properties and performance such as electrical breakdown strength. Results of the PD testing and statistical analysis show that the statistical parameter Sk extent of partial discharge distribution changes much more remarkably with the aging time than do the other parameters. The statistical parameter Sk can be considered as a potential indicator of stator insulation aging. The PLS approach can effectively assess the stator insulation aging condition and can provide a predictive capability for the stator insulation diagnosis.

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Section: Phase Resolved Pd Analysismentioning

confidence: 99%

Condition Evaluation of Large Generator Stator Insulation Based on Partial Discharge Measurement

Pan

Chen

et al. 2013

Advances in Mechanical Engineering

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“…Stone et al [17] reviewed the mechanisms of slot discharge and vibration sparking, and the methods to detect and repair them. Yue et al proposed two health measure of the skewness of PD phase resolved distribution and the high-frequency crest of PD impulse based upon a multistress accelerating aging test of stator windings [18]. Fernando et al found that the frequency-domain spectroscopy and estimated parameters are informative to assess the generator insulation condition with a laboratory generator bar experiment [19].…”

Section: Introductionmentioning

confidence: 99%

Statistical Health Reasoning of Water-Cooled Power Generator Stator Bars Against Moisture Absorption

Youn

Park

et al. 2015

IEEE Trans. Energy Convers.

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The power generator is typically maintained with a time-or usage-based strategy, which could result in a substantial waste of remaining useful life, high maintenance cost, and low plant availability. Recently, the field of prognostics and health management offers diagnostic and prognostic techniques to precisely assess the health condition and robustly predict the remaining useful life (RUL) of an engineered system, with an aim to address the aforementioned deficiencies. This paper explores a smart health reasoning system to assess the health condition of power generator stator bars against moisture absorption based on the statistical analysis of the capacitance measurements on bar insulators. In particular, a relative health measure, namely the directional Mahalanobis distance, is proposed to quantify the health condition of a stator bar. The smart health reasoning system is validated using five years' field data from seven generators, each of which contains 42 turns. . His current research interests include system risk-based design, prognostics and health management, and energy harvester design. Dr. Youn's research and educational dedication has led him to many notable awards, including the winner of the international PHM competitions in 2014 hosted by the IEEE PHM and PHM Society, and the ISSMO/Springer Prize for the Best Young Scientist in 2005.

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“…Compared to other methods for insulation defect diagnosis, PDD has several advantages, including more sensitive, non-destructive & non intrusive, and able to be performed on-site or even online [16]. As a result, PDD has been popularly used for monitoring insulation systems, especially high voltage insulation systems, such as generators [30], transformers [2] [3], and HV cables [19]. Since PD signals are generally low signal-to-noise ratio in nature, feature extraction and effective feature dimensionality reduction are even more important in the design of PDD systems.…”

Section: Introductionmentioning

confidence: 99%

On reducing feature dimensionality for partial discharge diagnosis applications

Yan

2012

Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing)

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Feature dimensionality reduction is a critical task in various machine learning applications including prognostics and health management (PHM) applications. Linear transformations, most popularly principal component analysis (PCA) and linear discriminant analysis (LDA), are the most widely-used methods for feature dimensionality reduction. For classification problems, LDA, being a supervised linear transformation that aims at maximally retaining class discriminant information, is generally considered to be a better method than PCA, an unsupervised method. However, LDA suffers from the singularity or small sample size problem. Attempting to address this problem, in this paper we propose a cluster-based LDA (cLDA) for feature dimensionality reduction. It first partitions features in distinct clusters and then performs cluster-wise LDA transformation. We demonstrate the effectiveness of the proposed cLDA on reducing the number of features by using a real-world PHM applicationpartial discharge diagnosis.

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Diagnosis of Stator Winding Insulation of Large Generator Based on Partial Discharge Measurement

Cited by 26 publications

References 12 publications

Condition Evaluation of Large Generator Stator Insulation Based on Partial Discharge Measurement

Condition Evaluation of Large Generator Stator Insulation Based on Partial Discharge Measurement

Statistical Health Reasoning of Water-Cooled Power Generator Stator Bars Against Moisture Absorption

On reducing feature dimensionality for partial discharge diagnosis applications

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