On-line monitoring of winding insulation health using high frequency common mode voltage from PWM

Tsyokhla, Igor; Griffo, Antonio; Wang, Jiabin

doi:10.1109/iemdc.2015.7409250

Cited by 10 publications

(10 citation statements)

References 15 publications

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“…All stator samples continued to operate normally after their respective sharp drops of Ceq. The full model of insulation in [18] is required to analyze observed anomalies and unexpected movement in capacitance and dissipation data and will be presented in further publications. Figs.…”

Section: B Capacitance Resultsmentioning

confidence: 99%

“…1. It was shown in [18] that the model used in off-line testing is only applicable at a single test frequency. In order to use data measured at multiple frequencies and return a result comparable with offline measurement practice, a novel way to interpret multifrequency data is presented.…”

Section: Ground-wall Impedancementioning

confidence: 99%

“…Here, we extend the method presented in [18]. In this method, insulation impedance is measured at inverter drive switching frequencies and multiple harmonics are used.…”

Section: Ground-wall Impedancementioning

confidence: 99%

“…The extrapolated values allow presentation of both the capacitance and dissipation factor at 50Hz to present results in a way more familiar to standard insulation testing. Alternate methods of data presentation and interpretation will be explored by fitting the harmonics to the model in [18] to obtain further information in other publications.…”

Section: A Multi-frequency Impedance Datamentioning

confidence: 99%

“…However, these tests can only be performed offline, requiring temporary interruption of machine operation with consequent loss of availability. The observation that changes in dielectric properties occur due to ageing has led to the development of a number of insulation health monitoring methods suitable for online application [12]- [18]. These methods, however, have not been practically demonstrated yet during long-term ageing in inverter-driven machines.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Online Condition Monitoring for Diagnosis and Prognosis of Insulation Degradation of Inverter-Fed Machines

Tsyokhla

Griffo

Wang

2019

IEEE Trans. Ind. Electron.

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Real-time winding insulation condition monitoring is becoming an important research topic in response to increase in high availability and reliability demands in modern drives. In this paper, a novel method is proposed to monitor ground-wall insulation of low voltage inverter-fed machines based on a multi-frequency measurement of equivalent insulation ground-wall capacitance and dissipation factor. The monitoring is applied to four machine stator samples subjected to accelerated aging until failure. Insulation degradation is continuously tracked via the capacitance and dissipation parameters. A link between ground-wall insulation capacitance and final lifetime is established. The relationship between capacitance progression and its value at final machine failure is used to develop a method for prognosis of the final failure time.

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Section: B Capacitance Resultsmentioning

confidence: 99%

Section: Ground-wall Impedancementioning

confidence: 99%

“…Here, we extend the method presented in [18]. In this method, insulation impedance is measured at inverter drive switching frequencies and multiple harmonics are used.…”

Section: Ground-wall Impedancementioning

confidence: 99%

Section: A Multi-frequency Impedance Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Online Condition Monitoring for Diagnosis and Prognosis of Insulation Degradation of Inverter-Fed Machines

Tsyokhla

Griffo

Wang

2019

IEEE Trans. Ind. Electron.

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Detection of humidity ingress using online common‐mode insulation impedance‐monitoring system

Tsyokhla

Griffo

Wang

2019

J. eng.

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Analytical Analysis and Design of an Advanced Differential-Mode Current Sensor for Insulation Monitoring for Industrial Electrical Assets

Zhang

2020

IEEE Access

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Some industrial incidents are attributed to insulation degradation of electrical assets. The leakage current can be a crucial indicator for online insulation monitoring of asset. To realize per-phase monitoring, the measurement accuracy of milliampere-level leakage current is limited by the strong load current noises. This paper proposes an advanced current sensor with a dual-core topology based on differential-mode (DM) measurement. An analytical model of magnetic field of the dual-core sensor is originally presented. It helps clarify the significances of the inner core in filtering noises and reducing the influence of system operation and cable positioning. Then, a comprehensive design procedure is fully investigated by selecting the optimal position and approach for magnetic field detection, and a high-speed and low-noise signal conditioning circuit is presented. The proposed current sensor is tested in the laboratory. Experimental results indicate that measurement errors can remain within 0.1mA in the testing scope, where the load current is up to 100A and the DM current varies from 0-7mA. The proposed sensor is further validated with superior performance compared with two commercial industrial sensors. The contribution of this paper lies in providing the analytical analysis and design of a novel DM current sensor for leakage current measurement, which achieves online per-phase insulation monitoring for industrial electrical assets.

show abstract

On-line monitoring of winding insulation health using high frequency common mode voltage from PWM

Cited by 10 publications

References 15 publications

Online Condition Monitoring for Diagnosis and Prognosis of Insulation Degradation of Inverter-Fed Machines

Online Condition Monitoring for Diagnosis and Prognosis of Insulation Degradation of Inverter-Fed Machines

Detection of humidity ingress using online common‐mode insulation impedance‐monitoring system

Analytical Analysis and Design of an Advanced Differential-Mode Current Sensor for Insulation Monitoring for Industrial Electrical Assets

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