Top PDF Improved transformer temperature monitoring

Improved transformer temperature monitoring

Improved transformer temperature monitoring

3. Maintenance 3.1 Limitations of traditional OTI and WTI For many decades, it has been a standard practice to install OTIs and WTIs on new transformers. These devices are typically comprised of a temperature sensing bulb inserted into a dry well in the top layer of the insulating fluid, as shown in Figure 1. In addition to this, the WTI incorporates a heater element to which a sample of the load current carried by the transformer is applied. This current causes the tempera- ture bulb to read the oil temperature plus a temperature increment that is intended to be the same as the winding hottest- spot temperature rise above top oil tem- perature. The fluid in the bulb expands through a capillary tube connected to a dial gauge equipped with switches that can be adjusted to any temperature within the operating range. These mechanical devices provide an accuracy of 3 °C to 5 °C if the transformer designer has pro- perly evaluated the winding hottest-spot temperature. These devices are typically used for cooling control and temperature alarms. They are sufficiently rugged to be used for protection purposes if the recom-
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Transformer Parameter Monitoring Using GSM Module

Transformer Parameter Monitoring Using GSM Module

According to the define of Transformer, it is a fixed device which transformers electrical energy as of single path to an additional path. To guard the transformer from full of atmosphere dirt and muck, it is completely together with this and oil immersed. As transformer has no revolving part, the probabilities of fault occuring in them are very rare.[2] so rare burden may be present extremely dangerous except transformer is hastily disengaged from system. This adequate automatic fortification for transformer adjacent to possible faults. Small capacity transformers used for distribution function are providing with series fuses for fortification in opposition to overloading and earth fault.[5] In this project we will be in domination of constraint like tenderness and current, voltage. Monitor is over and done with LM35 temperature feeler, GSM & microcontroller component. Therefore, it be mandatory in the direction of stay put on scrutiny the condition while make bigger in tenderness, force as well as environmental condition. It be
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Monitoring of Distribution Transformer Parameters Using Plc

Monitoring of Distribution Transformer Parameters Using Plc

Not only over load current may not result in damage to the transformer but also the absolute temperature of the windings and transformer oil remains within specified limits. The ratings of transformer are based on a 24-hour average ambient temperature of 30°C (86°F). Due to over voltage and over current, temperature of oil increases which causes failure of insulation of transformer winding. When the temperature of transformer increases to upper limit of temperature rating, the over temperature fault will occur. This fault can be detected by temperature sensor like thermistor.
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Transformer monitoring and controlling with GSM based system

Transformer monitoring and controlling with GSM based system

The controller consists of a sensing unit which collects the essential parameters such as current, voltage and the oil temperature within the distribution transformer. The digital display connected to the processing unit displays corresponding parameter values at the substation for any technical operations. The controller also senses the overload and high current flow conditions in the internal windings that may lead to breakdown of the corresponding unit. The Arduino controller is programmed in such a manner so as to continuously scan the transformer and update the parameters at a particular time interval. The parameter values sensed by the Arduino-controller are transmitted through the ADC transmitter connected to the Arduino controller unit.
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II. DISTRIBUTION TRANSFORMER MONITORING TECHNOLOGIES-A SURVEY APPROACH A. Online Monitoring by using SCADA

II. DISTRIBUTION TRANSFORMER MONITORING TECHNOLOGIES-A SURVEY APPROACH A. Online Monitoring by using SCADA

Viswanath [10] presented a paper uses a temperature sensor , pic microcontroller, LCD display ,GSM board and Xbee which is used for send the message to the electricity board. This system is capable of detecting multiple faults in the three phase transmission lines. Mohamed Ahmed Eltayeb Elmustafa Hayatiet [11] have designed decision support system to grid operation engineers with information helps to estimate the loads, fix problems and identify week points in the grid. Distribution transformer monitoring is very important in the grid in fact its abnormality adversely affects the smooth functioning of the smart grid. In this paper they suggested and implemented a method to remotely monitor a group of distribution transformers. Here the microcontroller is used for data acquisition and transmission.
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Monitoring System for Power Transformer Windings Hot Spot Temperature Using Fiber Optic Sensors, Kalman Filter and Integration in SCADA System

Monitoring System for Power Transformer Windings Hot Spot Temperature Using Fiber Optic Sensors, Kalman Filter and Integration in SCADA System

The fiber optic temperature monitoring technology is based on the dependence between the temperature and the GaAs crystal property of light absorption. The GaAs has a band deviation of certain energy, which is a fundamental characteristic of all semiconductor materials. This energy offset acts as a barrier for the absorption of lower energy wavelengths. The GaAs absorbs shorter wavelengths instead of a certain threshold while wider wavelengths are transmitted. This absorption transition is situated close to the infrared region and reaches wider wavelengths with increasing temperature. The operating principle is based on the GaAs semiconductor absorption spectrum variation in relation to the temperature. The inside of the fiber provides white light to the semiconductor crystal. Depending on the temperature, part of the light is more or less absorbed and the rest is reflected [17, 18].
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Transformer Health Monitoring Using IOT and LABVIEW

Transformer Health Monitoring Using IOT and LABVIEW

Abstract—In this paper real time online health monitoring of transformer is done. The transformer temperature, voltage level and load current is monitored on LABVIEW software and also on IOT platform ThingSpeak. The hardware framework mainly constitutes LCD, WiFi Module ESP8266, Arduino ATMEGA328 Microcontroller and sensors. For transformer protection relays are used. The IOT stage and LABVIEW contain data about the variation from the norm as per some predefined guidelines modified in the microcontroller for the protection of the transformer.
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Online conditional anomaly detection in multivariate data for transformer monitoring

Online conditional anomaly detection in multivariate data for transformer monitoring

However, an anomaly would not be flagged if, say, a heat- wave caused the ambient temperature to be so unusually high that there is a low probability of being in any of the four modes of operation. Intuitively, in such a case we may expect top oil temperature to be unusually high as well, and so low probability indicator values will not be defined as anomalous. Considering this case in terms of the probabilistic models, if the training data is very unlike the current environmental conditions, then the coverage of the models does not extend to the current situation, and we can make no firm judgement about how likely or not the indicator values are. It is better to label it as neither normal nor abnormal, than to flag something as anomalous when the coverage of the model does not provide information to support this. This approach will reduce the number of false anomalies reported to engineers.
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POWER TRANSFORMER INCIPIENT FAULTS MONITORING

POWER TRANSFORMER INCIPIENT FAULTS MONITORING

Abstract: Power transformers are important and expensive components in the electric power system. The knowledge of the actual status of the transformer insulation behavior, load tap changer performance, temperature, and load condition is necessary in order to evaluate the service performance concerning reliability, availability and safety. Systems abnormalities, loading, switching and ambient condition normally contribute towards accelerated aging and sudden failure. The paper presents the causes which lead to the internal faults appearance in the power transformer. The production mechanisms of the faults and the on-line monitoring are also analyzed. A monitoring procedure is proposed for the diagnosis and forecasting strategy of the functioning state of the power transformer.
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Transformer Status Monitoring and Diagnosis Using IOT

Transformer Status Monitoring and Diagnosis Using IOT

In this system, we used three sensors for monitoring, that is voltage sensor, a current sensor, and temperature sensor. We used a power supply to operate microcontroller PIC16F877A and IOT Module. Sensors sense the data and display it on LCD display, at the same time IOT module sends the data to the user on given IP address as per program. If we get an unsecured data about the system we can avoid failure. This proposes a model for real-time transformer monitoring using IOT. This is classified into four parts- Power supply (230V step down transformer, bridge rectifier and regulator), controlling, data processing and data uploading.
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Distribution Transformer Monitoring and Controlling Using GSM Module

Distribution Transformer Monitoring and Controlling Using GSM Module

low-voltage users directly, and its operation condition is an important component of the entire distribution network operation. Operation of distribution transformer under rated condition( as per specification in their nameplate) guarantees their long life .However, their life is significantly reduced if they are subjected to overloading, resulting in unexpected failures and loss of supply to a large number of customers thus effecting system reliability. Overloading and ineffective cooling of transformers are the major causes of failure in distribution transformers. We have designed a module which constantly monitors the parameters such as over voltage, over current, temperature, oil level of distribution transformer. If any difficulty arises message will goes directly to the monitoring centre with the help of GSM module.
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ZIGBEE Wireless Transformer Monitoring, Protection and Control System

ZIGBEE Wireless Transformer Monitoring, Protection and Control System

On line Wireless monitoring and diagnostic of power transformers has attracted considerable attention for many years. The main objectives are to prevent forced outages, indicate acceptable overload, assess the remaining insulation-life and reduce maintenance costs. To achieve these goals, the monitoring system manufacturers must follow strategies, which are in line with the interests of transformer owners. Transformer is the key equipment in power system, to ensure its safe and stable operation is important. Transformers either raise a voltage to decrease losses, or decreases voltage to a safe level. "Monitoring" is here defined as on-line collection of data and includes sensor development, measurement techniques for on-line applications. It is very difficult and expensive to construct the communication wires to monitor and control each distribution transformer station. Here ZigBee is used for communicating the monitored parameters. The failures of transformers in service are broadly due to: Over Load condition temperature rise, low oil levels, over load, Earth grounding , and improper installation and maintenance. Out of these factors temperature rise, low oil levels and over load, need continuous monitoring to save transformer life. A distributed transformer networks remote monitoring system increases the reliability of distribution network, by monitoring critical information such as oil temperature, and oil level of transformer. Data are collected continuously. Monitoring the transformers for problems before they occur can prevent faults that are costly to fix and result in a loss of service life.
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Real Time Transformer Monitoring System Using GSM

Real Time Transformer Monitoring System Using GSM

ABSTRACT: Electricity plays an important role in our life.The most crucial equipment of transmission and distribution of electric Power is transformer. As a large number of transformers are distributed over a wide area in present electric systems, it is difficult to measure the condition manually of every single transformer. So automatic data acquisition and transformer condition measuring has been an important issue. This project presents design and implementation of a mobile embedded system to measure load currents, over voltage, transformer oil level and oil temperature.This is implemented by on-line measuring system using Wireless, with single chip Arduino microcontroller and sensors. The output values of sensors are processed and recorded in the system memory. System programmed with some predefined instructions to check abnormal conditions. If there is any abnormality on the system, details are automatically updated in the Mobile through Wireless communication. GSM will help to utilize transformers optimally and identify problems before any catastrophic failure occurs. Thus wireless-measuring system is used to collect and analyze temperature data over time,which has greater reliability and significant cost savings .
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Distribution Transformer Health Monitoring System Using IOT

Distribution Transformer Health Monitoring System Using IOT

ABSTRACT: Transformers are a vital part of the transmissions and distribution systems. Monitoring transformers for problem before they occurs prevent fault that are costly to repair and results in a loss of services. Current system provides information about the state of a transformers, but are either offline or very expensive to implement. Transformer is essential part of power transmission system, are costly, as is the cost of power interruption. The costs of scheduled and unscheduled maintenance, especially at remote site, the utility industry has begun investing in instrumentation& monitoring of transformers. This project objective is to develop low cost solution for monitoring health conditions of remotely located Distribution Transformer using IOT technology to prevent premature failures of distributions transformers and improving reliability of services to the customers. This is done by deploying sensors to monitor the transformer winding temperature ,oil temperature ,level of the transformer oil ,voltage and based on the load current monitored if it is any abnormalities happen will indicate to Website page of the above transformer IP Address so has to avoid failure cause of the transformer.
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IOT BASED ONLINE MONITORING OF TRANSFORMER MAINTENANCE SYSTEM

IOT BASED ONLINE MONITORING OF TRANSFORMER MAINTENANCE SYSTEM

This paper screens the state of transformer protection oil and keeps up the transformer in solid condition. Because of absence of legitimate work, the power transformers in power board and the circulation transformers utilized as a part of open and private area are not looked after appropriately. The protecting oil in the transformer may infrequently come into contact with air because of spillage in the oil tanks or in related pipe lines, the oxidation response in the transformer oil happens, which will be additionally quickened because of temperature and nearness of impetuses like iron, copper and broke down metallic mixes in the transformer oil because of maturing. The above condition talked about will change the qualities of transformer protecting oil, which truly influences the execution of transformer and may prompts blast of transformer on the off chance that it isn't observed appropriately. The proposed framework performs sharpness and blaze point test which decides the qualities of oil whether to utilize it further or to purge and educated it to the individual worried through GSM and IoT.
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Uncertainty-aware fusion of probabilistic classifiers for improved transformer diagnostics

Uncertainty-aware fusion of probabilistic classifiers for improved transformer diagnostics

From an engineering viewpoint, the disagreements among independent classifiers are the most important situations that need to be resolved effectively because conflicting diagnoses may imply very different maintenance actions. Therefore, it is critical to analyse and quantify the strength of classifiers in the presence of conflicting data. Uncertainty quantification is very important for condition monitoring systems [24]. For instance, assume that a model has been trained to classify certain faults. So long as the test data is comprised of faults which are similar to the trained model, it should return a prediction with high confidence. However, if the model is tested on an unseen class of fault, the model should be able to quantify this with uncertainty levels, which can convey information about the confidence of the diagnosis of the model. This information is completely lost with BB models. Conversely, white-box (WB) models capture expert knowledge either as a causal model or through first-principle models. They generate the uncertainty associated with the decision-making process by quantifying the PDF of the likelihood of different diagnostics states. This function represents the strength of the model’s diagnosis, i.e. the wider the variance, the lesser the confidence in the diagnostics outcome and vice-versa.
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Uncertainty-aware fusion of probabilistic classifiers for improved transformer diagnostics

Uncertainty-aware fusion of probabilistic classifiers for improved transformer diagnostics

Abstract—Transformers are critical assets for the reliable operation of the power grid. Transformers may fail in service if monitoring models do not identify degraded conditions in time. Dissolved gas analysis (DGA) focuses on the examination of dissolved gasses in transformer oil to diagnose the state of a transformer. Fusion of black-box classifiers, also known as an ensemble of diagnostics models, have been used to improve the accuracy of diagnostics models across many fields. When independent classifiers diagnose the same fault, this method can increase the veracity of the diagnostics. However, if these methods give conflicting results, it is not always clear which model is most accurate due to their black-box nature. In this context, the use of white-box models can help resolve conflicted samples effectively by incorporating uncertainty information and improve the classification accuracy. This paper presents an uncertainty-aware fusion method to combine black-box and white-box diagnostics methods. The effectiveness of the proposed approach is validated using two publicly available DGA datasets.
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Real Time Condition Monitoring of Transformer

Real Time Condition Monitoring of Transformer

By measuring the transformer parameters, for example, transformer temperature and power consumed vs power supplied by the system to its load along with handling, sending and receiving of data wirelessly on webserver, it is conceivable to successfully screen circulation transformers and furthermore to watch utilization rate per day. This prototype model shown in Fig. 4 contains four 110W bulbs as the output load. The farthest point esteem is set as 4Amps. At the point when every one of the bulbs are turned on the load current exceeds its set limit of 4Amp, the MCU senses a surpassing readings and strong state transfer is turned on which breaks the association. All of this information can be visualize on webserver in form of graphical charts and tables as shown in Fig. 5, Fig. 6 and Fig. 7 and Table I.
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Real Time Transformer Health Monitoring System Using IOT

Real Time Transformer Health Monitoring System Using IOT

Electricity plays an important role in our life. Every moment of our life depends upon electricity. Electricity has several components and equipment helping human to transfer and regulate the distribution according to usage. The most crucial equipment of transmission and distribution of electric power is transformer. In Power system, an electrical component transformer directly distributes power to the low-voltage users and its operation condition is a criteria of the entire network operation. The majority of the devices have been in service for many years in different (electrical, mechanical, environmental) conditions. They are the main components and constitute the large portion of capital investment. Operation of distribution transformer under rated condition (as per specification in their name plate) guarantees their long service life. However their life is significantly reduced if they are subjected to overloading, heating low or high voltage current resulting in unexpected failure and loss of supply to a large number of customers thus is affecting system reliability. Overloading, oil temperature load current and ineffective cooling of transformer are the major cause of failure in distribution transformer. As a large number of transformers are distributed over a wide area in present electric systems, it’s difficult to measure the condition manually of every single transformer. So we need a distribution transformer system to monitor all essential parameters operation, and send to the monitoring system in time. It provides the necessary information about the health of the transformer. This will help and guide the utilities to optimally use the transformer and keep this equipment in operation for a longer period.
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Improved power transformer condition monitoring under uncertainty through soft computing and probabilistic health index

Improved power transformer condition monitoring under uncertainty through soft computing and probabilistic health index

So as to adopt maintenance-related decisions, the engineer can make use of the transformer-level health index metric, or alternatively, the proposed independent subsystem health indices can be used so as to examine the health state of different subsystems. The propagation of the uncertainty estimates from the solid insulation health index (Figure 11) into the transformer-level health index (Figure 21) results in a worst case transformer health index variation of 0.01 units. This is informative to examine the effect of temperature and load measurement errors, as well as uncertain degradation processes on the solid insulation and transformer-level health state. If the maximum likelihood value was lower and the confidence bounds were wider, the decision-making process would have been more intricate. In contrast, the expert-based health index formulation in [27] generates deterministic health index values without uncertainty modelling and without including many of the different DGA and insulation paper factors integrated in this work. Accordingly, the proposed health index model assists the engineer in the transformer maintenance decision- making processes with more informative and uncertainty-aware subsystem and system-level health index metrics. Depending on the magnitude of the errors, the effect of uncertainty in decision-making can become critical.
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