A review on status monitoring techniques of transformer and a case study on loss of life calculation of distribution transformers

Chandran, Lekshmi R.; Babu, G.S. Ajith; Nair, Manjula G.; Ilango, K.

doi:10.1016/j.matpr.2020.10.290

Cited by 12 publications

(9 citation statements)

References 17 publications

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“…Many strategies are effective for classifying the various sorts of failures stated, as seen in Figure 5 and established by Lekshmi in the study of monitoring techniques [27]; for example, for thermal analysis, which needs a thermal camera and a data acquisition device to record the power transformer's image data, deep learning algorithms are used to categorize failures, primarily internal winding problems.…”

Section: Power Transformer Monitoring Techniquesmentioning

confidence: 99%

“…Transformer life expectancy mostly depends on the insulation that is put in place to protect it from heat losses. Throughout the lifespan of the transformer, a number of abnormal circumstances, such as insulation defects, overloading, and winding shorting, will happen, which alters the transformer's typical life span [27]. The life loss is a very informative indicator of the power transformer since it gives an obvious view of the power transformer state.…”

Section: Life Lossmentioning

confidence: 99%

“…Due to the melting of insulation winding into the transformer's cooling oil, the presence of various gasses can be seen in the oil tank of the transformer at an early stage. Different types of faults happening in the transformer can be represented by the ratio of these various gasses present [27] because of the conversion of winding material into winding oil determines the sort of gas recovered and its nature. The faults could be determined by the conventional ratio methods or by applying the artificial intelligence algorithms, where the necessity to monitor and acquire DGA exists.…”

Section: Dga Graphmentioning

confidence: 99%

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An AI-Layered with Multi-Agent Systems Architecture for Prognostics Health Management of Smart Transformers: A Novel Approach for Smart Grid-Ready Energy Management Systems

et al. 2022

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After the massive integration of distributed energy resources, energy storage systems and the charging stations of electric vehicles, it has become very difficult to implement an efficient grid energy management system regarding the unmanageable behavior of the power flow within the grid, which can cause many critical problems in different grid stages, typically in the substations, such as failures, blackouts, and power transformer explosions. However, the current digital transition toward Energy 4.0 in Smart Grids allows the integration of smart solutions to substations by integrating smart sensors and implementing new control and monitoring techniques. This paper is proposing a hybrid artificial intelligence multilayer for power transformers, integrating different diagnostic algorithms, Health Index, and life-loss estimation approaches. After gathering different datasets, this paper presents an exhaustive algorithm comparative study to select the best fit models. This developed architecture for prognostic (PHM) health management is a hybrid interaction between evolutionary support vector machine, random forest, k-nearest neighbor, and linear regression-based models connected to an online monitoring system of the power transformer; these interactions are calculating the important key performance indicators which are related to alarms and a smart energy management system that gives decisions on the load management, the power factor control, and the maintenance schedule planning.

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Section: Power Transformer Monitoring Techniquesmentioning

confidence: 99%

Section: Life Lossmentioning

confidence: 99%

Section: Dga Graphmentioning

confidence: 99%

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An AI-Layered with Multi-Agent Systems Architecture for Prognostics Health Management of Smart Transformers: A Novel Approach for Smart Grid-Ready Energy Management Systems

et al. 2022

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“…where: FEQA=aging factor n=time interval index N=total time duration 𝛥𝑡 𝑛 =time interval And, the percentage of LOL can be determined as [26], [27]:…”

Section: The Loss Of Life Of the Transformermentioning

confidence: 99%

Analysis of thermal models to determine the loss of life of mineral oil immersed transformers

et al. 2021

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Hot spot as well as top oil temperatures have played the most effective parameters on the life of the electrical transformers. The prognostication of these factors is very vital for determining the residual life of the electrical transformers in the transmission and distribution systems. Thus, an accurate mathematical method is required to calculate the critical temperature such as hot spot and top oil temperature based on the different types of thermal models. In this study calculates the service life of the transformers based on an accurate top oil temperature. Accordingly, An approach solution is given for calculating the thermal model. Also, findings are validated with true temperatures. Finally, this method is implemented on 2500 KVA electrical transformer.

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“…Mechanical damage can also occur when the cooling mechanism fails to function properly. It may gradually turn into a serious fault, such as overheating, winding failure, and oil contamination (Chandran et al, 2021;Murugan & Ramasamy, 2019). Most of these faults can be prevented through testing and maintenance.…”

Section: Introductionmentioning

confidence: 99%

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2022

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In Malaysia, on-site technical personnel manually inspect power transformers. Some vital condition indicators, such as oil and winding temperatures, are not monitored in real-time. This condition can be hazardous if the transformer gets overheated. Overheating can cause mechanical deformation and insulation degradation if not monitored regularly. Thus, an online monitoring system that meets industry standards is needed to enhance power transformer monitoring and troubleshooting. In this research, the Internet of Things (IoT) based data acquisition (DAQ) system was deployed for real-time oil temperature monitoring and inspection to detect incipient faults in power transformers early. This IoT-based DAQ system was connected to the substation remote terminal unit (RTU) to update real-time data on each power transformer. The long-range (LoRa) technology is proposed for the system to transmit temperature, current, and voltage from the power transformers. The data transmission from the oil temperature indicator (OTI), network server, and database was monitored and compared. It is observed that the temperature data was transferred from the network server to the database without any transmission delay. The average deviation from the two experiments was 0.006 and 0.003, respectively, compared to the manual reading from the OTI scale meter with a digital reading by the proposed DAQ system. For testing purposes, the alert

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A review on status monitoring techniques of transformer and a case study on loss of life calculation of distribution transformers

Cited by 12 publications

References 17 publications

An AI-Layered with Multi-Agent Systems Architecture for Prognostics Health Management of Smart Transformers: A Novel Approach for Smart Grid-Ready Energy Management Systems

An AI-Layered with Multi-Agent Systems Architecture for Prognostics Health Management of Smart Transformers: A Novel Approach for Smart Grid-Ready Energy Management Systems

Analysis of thermal models to determine the loss of life of mineral oil immersed transformers

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