Low-Cost Online Contact Resistance Measurement of Power Connectors to Ease Predictive Maintenance

Kadechkar, Akash; Moreno-Eguilaz, Manuel; Riba, Jordi-Roger; Capelli, Francesca

doi:10.1109/tim.2019.2899481

Cited by 29 publications

(25 citation statements)

References 32 publications

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“…Connectors are supplied at power frequency, so by measuring on-line the voltage drop between the connector terminals Δ V Connector and the AC current I passing through them, the impedance of the connector Z Connector is determined instead of the resistance R Connector [ 4 ],

φ being the phase angle between the voltage drop across the connector and the current waveforms.…”

Section: Resistance-based Degradation Modelsmentioning

confidence: 99%

“…These electromechanical devices aim to transmit electrical power with minimum power losses [ 2 , 3 ], thus with minimum voltage drop. The most common materials for medium voltage connectors are copper and aluminum [ 4 ] and they are often of compressed type, since compression provides a reduced contact resistance and a reliable electrical connection.…”

Section: Introductionmentioning

confidence: 99%

“…Any increase in the resistance implies a rise in the temperature of the connector, which further raises the resistance, thus tending to deteriorate the connector performance and reducing the RUL [ 13 ]. Next, from a mathematical RUL model, the right moment to replace the connector is calculated, i.e., when the connector will show poor or degraded electrical performance [ 4 ]. The RUL model proposed in this work is based on the oxidation multi-spot equation which describes the rise of the electrical contact resistance with time, due to the growth of oxide films at the contact interface [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

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On-Line Remaining Useful Life Estimation of Power Connectors Focused on Predictive Maintenance

Riba

Gómez-Pau

Martinez

et al. 2021

Sensors

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Connections are critical elements in power systems, exhibiting higher failure probability. Power connectors are considered secondary simple devices in power systems despite their key role, since a failure in one such element can lead to major issues. Thus, it is of vital interest to develop predictive maintenance approaches to minimize these issues. This paper proposes an on-line method to determine the remaining useful life (RUL) of power connectors. It is based on a simple and accurate model of the degradation with time of the electrical resistance of the connector, which only has two parameters, whose values are identified from on-line acquired data (voltage drop across the connector, electric current and temperature). The accuracy of the model presented in this paper is compared with the widely applied autoregressive integrated moving average model (ARIMA), showing enhanced performance. Next, a criterion to determine the RUL is proposed, which is based on the inflection point of the expression describing the electrical resistance degradation. This strategy allows determination of when the connector must be replaced, thus easing predictive maintenance tasks. Experimental results from seven connectors show the potential and viability of the suggested method, which can be applied to many other devices.

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φ being the phase angle between the voltage drop across the connector and the current waveforms.…”

Section: Resistance-based Degradation Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On-Line Remaining Useful Life Estimation of Power Connectors Focused on Predictive Maintenance

Riba

Gómez-Pau

Martinez

et al. 2021

Sensors

Self Cite

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“…Many switchgear failures are caused by gradual degradation [1,2] of the devices, such as the insulators [3][4][5][6][7], switches [8][9][10][11][12][13], and connectors [14][15][16][17][18][19][20]. At the preliminary stage, these electrical faults, as shown in Figure 1, can produce noises that are detectable in the frequency range (20 kilohertz (kHz) to 100 kHz) by an ultrasonic detection system [21,22].…”

Section: Introductionmentioning

confidence: 99%

Fault Classification System for Switchgear CBM from an Ultrasound Analysis Technique Using Extreme Learning Machine

et al. 2021

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Currently, the existing condition-based maintenance (CBM) diagnostic test practices for ultrasound require the tester to interpret test results manually. Different testers may give different opinions or interpretations of the detected ultrasound. It leads to wrong interpretation due to depending on tester experience. Furthermore, there is no commercially available product to standardize the interpretation of the ultrasound data. Therefore, the objective is the correct interpretation of an ultrasound, which is one of the CBM methods for medium switchgears, by using an artificial neural network (ANN), to give more accurate results when assessing their condition. Information and test results from various switchgears were gathered in order to develop the classification and severity of the corona, surface discharge, and arcing inside of the switchgear. The ultrasound data were segregated based on their defects found during maintenance. In total, 314 cases of normal, 160 cases of the corona, 149 cases of tracking, and 203 cases of arcing were collected. Noise from ultrasound data was removed before uploading it as a training process to the ANN engine, which used the extreme learning machine (ELM) model. The developed AI-based switchgear faults classification system was designed and incorporated with the feature of scalability and can be tested and replicated for other switchgear conditions. A customized graphical user interface (GUI), Ultrasound Analyzer System (UAS), was also developed, to enable users to obtain the switchgear condition or classification output via a graphical interface screen. Hence, accurate decision-making based on this analysis can be made to prioritize the urgency for the remedial works.

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“…Medium voltage connectors are predominantly made of aluminum or copper [6], usually of compressed type, and designed to guarantee steady and reliable electrical connections between two bus bars or conductors with reduced voltage drop and power loss [7,8].…”

Section: Introductionmentioning

confidence: 99%

Time Series RUL Estimation of Medium Voltage Connectors to Ease Predictive Maintenance Plans

2020

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The ageing process of medium voltage power connectors can lead to important power system faults. An on-line prediction of the remaining useful life (RUL) is a convenient strategy to prevent such failures, thus easing the application of predictive maintenance plans. The electrical resistance of the connector is the most widely used health indicator for condition monitoring and RUL prediction, even though its measurement is a challenging task because of its low value, which typically falls in the range of a few micro-ohms. At the present time, the RUL of power connectors is not estimated, since their electrical parameters are not monitored because medium voltage connectors are considered cheap and secondary devices in power systems, despite they play a critical role, so their failure can lead to important power flow interruptions with the consequent safety risks and economic losses. Therefore, there is an imperious need to develop on-line RUL prediction strategies. This paper develops an on-line method to solve this issue, by predicting the RUL of medium voltage connectors based on the degradation trajectory of the electrical resistance, which is characterized by analyzing the electrical resistance time series data by means of the autoregressive integrated moving average (ARIMA) method. The approach proposed in this paper allows applying predictive maintenance plans, since the RUL enables determining when the power connector must be replaced by a new one. Experimental results obtained from several connectors illustrate the feasibility and accuracy of the proposed approach for an on-line RUL prediction of power connectors.

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Low-Cost Online Contact Resistance Measurement of Power Connectors to Ease Predictive Maintenance

Cited by 29 publications

References 32 publications

On-Line Remaining Useful Life Estimation of Power Connectors Focused on Predictive Maintenance

On-Line Remaining Useful Life Estimation of Power Connectors Focused on Predictive Maintenance

Fault Classification System for Switchgear CBM from an Ultrasound Analysis Technique Using Extreme Learning Machine

Time Series RUL Estimation of Medium Voltage Connectors to Ease Predictive Maintenance Plans

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