Detection and Localization of Partial Discharge in Connectors of Air Power Lines by Means of Ultrasonic Measurements and Artificial Intelligence Models

Samaitis, Vykintas; Mažeika, Liudas; Jankauskas, Audrius; Rekuvienė, Regina

doi:10.3390/s21010020

Cited by 13 publications

(4 citation statements)

References 33 publications

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“…[29] provides an overview of the evolution of smart algorithms for estimating the transformer state. Papers [23,[30][31][32] provide an overview of and consider prospects for the development of real-time RUL estimation techniques. Currently, two basic approaches are used to estimate and forecast the equipment state-based on the health index (HI) and the RUL.…”

Section: The Relevance Of Improving the Rul Forecasting Techniquesmentioning

confidence: 99%

Method for Forecasting the Remaining Useful Life of a Furnace Transformer Based on Online Monitoring Data

et al. 2023

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Implementing the concept of a “smart furnace transformer” should stipulate its information support throughout its life cycle. This requires improving techniques for estimating the transformer’s health and forecasting its remaining useful life (RUL). A brief review of the problem being solved has shown that the known RUL estimation techniques include processing the results of measuring the facility state parameters using various mathematical methods. Data processing techniques (deep learning, SOLA, etc.) are used, but there is no information on their application in online monitoring systems. Herewith, fast (shock) changes in the resource caused by the failures and subsequent recoveries of the facility’s health have not been considered. This reduces the RUL forecasting accuracy for the repairable equipment, including transformers. It is especially relevant to consider the impact of sudden state changes when it comes to furnace transformers due to a cumulative wear effect determined by their frequent connections to the grid (up to 100 times a day). The proposed approach is based on calculating the RUL by analytical dependencies, considering the failures and recoveries of the facility state. For the first time, an engineering RUL forecasting technique has been developed, based on the online diagnostic monitoring data results provided in the form of time series. The equipment’s relative failure tolerance index, calculated with analytical dependencies, has first been used in RUL forecasting. As a generalized indicator, a relative failure tolerance index considering the facility’s state change dynamics has been proposed. The application of the RUL forecasting technique based on the results of dissolved gas analysis of a ladle furnace unit’s transformer is demonstrated. The changes in the transformer state during the operation period from 2014 to 2022 have been studied. The RUL was calculated in the intensive aging interval; the winding dismantling results were demonstrated, which confirmed developing destructive processes in the insulation. The key practical result of the study is reducing accidents and increasing the service life of the arc and ladle furnace transformers. The techno-economic effect aims to ensure process continuity and increase the metallurgical enterprise’s output (we cannot quantify this effect since it depends on the performance of a particular enterprise). It is recommended to use the technique to forecast the RUL of repairable facilities equipped with online monitoring systems.

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Section: The Relevance Of Improving the Rul Forecasting Techniquesmentioning

confidence: 99%

Method for Forecasting the Remaining Useful Life of a Furnace Transformer Based on Online Monitoring Data

et al. 2023

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“…The best solution is to use ultrasonic discharge counters in association with humidity and temperature sensors. New studies relate to the replacement of O 3 sensors with ultrasonic discharge counter [22]. For example, in [22] the 40 kHz ultrasonic transducer MA40S4R from Murata Manufacturing [23], with 80 • directivity, −63 dB sensitivity and 5 kHz bandwidth at −6 dB level is used for analyzing PD.…”

Section: Ozone Measurements In Pd (Partial Discharge) and Literature ...mentioning

confidence: 99%

“…New studies relate to the replacement of O 3 sensors with ultrasonic discharge counter [22]. For example, in [22] the 40 kHz ultrasonic transducer MA40S4R from Murata Manufacturing [23], with 80 • directivity, −63 dB sensitivity and 5 kHz bandwidth at −6 dB level is used for analyzing PD. Additional machine learning techniques were used to separate the parasitic surrounding noise from the PD signals.…”

Section: Ozone Measurements In Pd (Partial Discharge) and Literature ...mentioning

confidence: 99%

Partial Discharge and Internet of Things: A Switchgear Cell Maintenance Application Using Microclimate Sensors

Fechet

Petrariu

Graur

2021

Sensors

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This paper proposes a solution for the development of microclimate monitoring for Low Voltage/High Voltage switchgear using the PRTG Internet of Things (IoT) platform. This IoT-based real time monitoring system can enable predictive maintenance to reduce the risk of electrical station malfunctions due to unfavorable environmental conditions. The combination of humidity and dust can lead to unplanned electrical discharges along the isolators inside a low or medium voltage electric table. If no predictive measures are taken, the situation may deteriorate and lead to significant damage inside and outside the switchgear cell. Thus, the mentioned situation can lead to unprogrammed maintenance interventions that can conduct to the change of the entire affected switchgear cell. Using a low-cost and efficient system, the climate conditions inside and outside the switchgear are monitored and transmitted remotely to a monitoring center. From the results obtained using a 365-day time interval, we can conclude that the proposed system is integrated successfully in the switchgear maintaining process, having as result the reduction of maintenance costs.

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“…Based on dissolved gas analysis (DGA), more scholars adopted the artificial neural network (ANN), the polynomial neural network (PNN), etc., [ 19 , 20 , 21 ]. Moreover, some studies have optimized the algorithms to achieve better results [ 22 , 23 , 24 ]. Compared to the three-ratio method, the adoption of machine learning significantly improved the accuracy of diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Partial Discharge Based on the Air Components for the 10 kV Air-Insulated Switchgear

Tan

Zhang

et al. 2022

Sensors

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Partial discharge (PD) is a common phenomenon of insulation aging in air-insulated switchgear and will change the gas composition in the equipment. However, it is still a challenge to diagnose and identify the defect types of PD. This paper conducts enclosed experiments based on gas sensors to obtain the concentration data of the characteristic gases CO, NO2, and O3 under four typical defects. The random forest algorithm with grid search optimization is used for fault identification to explore a method of identifying defect types through gas concentration. The results show that the gases concentration variations do have statistical characteristics, and the RF algorithm can achieve high accuracy in prediction. The combination of a sensor and a machine learning algorithm provides the gas component analysis method a way to diagnose PD in an air-insulated switchgear.

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Detection and Localization of Partial Discharge in Connectors of Air Power Lines by Means of Ultrasonic Measurements and Artificial Intelligence Models

Cited by 13 publications

References 33 publications

Method for Forecasting the Remaining Useful Life of a Furnace Transformer Based on Online Monitoring Data

Method for Forecasting the Remaining Useful Life of a Furnace Transformer Based on Online Monitoring Data

Partial Discharge and Internet of Things: A Switchgear Cell Maintenance Application Using Microclimate Sensors

Diagnosis of Partial Discharge Based on the Air Components for the 10 kV Air-Insulated Switchgear

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