Transformer Monitoring System Research Articles

Rich surface oxygen vacancies SmFeO3 for acetylene gas sensor with ppb-level detection

The concentration of acetylene (C2H2) is a key parameter to monitor the status of oil-immersed transformers. When the transformer is operated at 330 kV, the transformer oil dissolving 1 ppm of C2H2 is considered as red flag. Therefore, low detection limit of C2H2 is very necessary for transformer monitoring systems. In this work, a freeze-dry assisted sol-gel method is proposed to synthesize rich surface oxygen vacancies SmFeO3. Compared to the traditional sol-gel method, the freeze-dry synthesized SmFeO3 exhibits the increased oxygen vacancies and a higher proportion of larger pores (>100 nm). The response of the sensor based on above SmFeO3 (SFO-F) to C2H2 is higher than that of the sensor based on SmFeO3 synthesized by traditionally sol-gel method (SFO-S), and the SFO-F exhibits the low detection limit of 200 ppb C2H2. In addition, the response time for 200 ppb C2H2 of the SFO-F is relatively smaller than that of the SFO-S. Moreover, the sensors exhibit a high selectivity towards C2H2 in comparison to other gases (C2H4, CH4, NO, CO, H2), and five consecutive test cycles demonstrate the high stability of the sensors. The SmFeO3 prepared through freeze-drying may provide a viable approach to enhance the performance of C2H2 sensors and presenting a fresh perspective to fabricating oxygen-rich vacancy materials.

Design of a Load Monitoring System for a Distribution Transformer

Design of a Load Monitoring System for a Distribution Transformer

IoT Based Transformer Monitoring System using ESP-32

Abstract: The integration of Internet of Things (IoT) technology with power transformer monitoring systems presents a significant advancement in electrical grid management. This paper explores the development of an IoT-based transformer monitoring system utilizing the ESP-32 microcontroller. The primary objective is to enhance the reliability and efficiency of transformers by providing real-time data on their operational status and health. The system leverages the ESP-32's capabilities in wireless communication and data processing to monitor critical parameters such as temperature, load current, voltage, and oil levels. Data is transmitted to a centralized cloud platform for analysis and visualization, enabling predictive maintenance and rapid response to potential failures. The proposed system promises to reduce downtime and maintenance costs while ensuring the longevity and optimal performance of transformers. This research highlights the design, implementation, and testing phases of the monitoring system, demonstrating its effectiveness through various case studies and field trials.

Breakdown Voltage Estimation in Transformer Oils with Low-Cost Humidity Sensor

The relative humidity and temperature of the oil used for insulation purposes in transformers directly affect the breakdown voltage of the oil and accordingly the life of the transformer. Continuous monitoring of the moisture content and breakdown voltage of the transformer oil provides the basis for predictive maintenance practices. This study aims to develop a sensor to continuously monitor the moisture content of the insulating oil and to calculate the breakdown voltage approximately. In this context, firstly, measurements were taken with an industrial oil monitoring sensor which calculates the breakdown voltage in transformer oils with high accuracy and was verified with laboratory measurements. At the same time, EE364 by E+E™ and SHT10 by Sensirion™ also used for temperature and humidity measurements. By comparing the measured data with each other, a relationship was formulated between the humidity and temperature values and the breakdown voltage. As a result, an approximate breakdown voltage calculation method that can be used considering the characteristic parameters of transformer oil has been introduced. Thus, a cost-effective function has been developed that can be widely used for transformer monitoring systems.

Transformer Monitoring with Electromagnetic Energy Transmission and Wireless Sensing.

To ensure stable and normal transformer operation, light gas protection of the transformer Buchholz relay is essential. However, false alarms related to light gas protection are common, and troubleshooting them often requires on-site gas sampling by personnel. During this time, the transformer's operating state may rapidly deteriorate, posing a safety threat to field staff. To tackle these challenges, this work presents the near-field, thin-sliced transformer monitoring system that uses Electromagnetic Energy Transmission and Wireless Sensing Device (ETWSD). The system leverages external wireless energy input to power gas monitoring sensors. Simultaneously, it employs Near-Field Communication to obtain real-time concentrations of light gases, along with the electrified state and temperature. In field testing conducted on transformer relays' gas collection chambers, the ETWSD effortlessly monitors parameters within warning ranges, encompassing methane gas concentrations around 1000 ppm, leakage voltage ranging from 0-100 V, and relay working temperatures up to 90 °C. Additionally, to facilitate real-time diagnosis for electrical workers, we have developed an Android-based APP software that displays current light gas concentrations, leakage voltage collection values, and temperature, while also enabling threshold judgment, alarms, and data storage. The developed ETWSD is expected to aid on-site personnel in promptly and accurately evaluating transformer light gas protection error alarm faults. It provides a method for simultaneous, contactless, and rapid monitoring of multiple indicators.

Instruments for On-line Monitoring of Transformers

The main objective of the paper is to describe the experience with newly developed monitoring system for transformers with oil-paper insulation system. From the view of operation reliability, it is necessary for operators to provide a monitoring of transformer’s failure free operation. The application of monitoring system enables to warn timely of the rising failure and also decrease of degradation of single subsystems of machine. When failure is detected, it is important to support this detection by other measurements that would lead to exact determination of the failure. SFRA analysis (Sweep Frequency Response Analysis) is such additional method in our monitoring.

Transformer protection, automation and monitoring systems

This paper presents the results of research on improving the protection, automation and monitoring systems of power transformers (PT) in 6-35 kV distribution networks. The authors have developed improved algorithms for differential and distance protection of a transformer based on measuring voltage and current synchrophasors, proposed additional criteria for blocking protection in the event of a magnetization inrush current (MIC), and considered the functions of the transformer monitoring and automation system based on synchrophasor technology.

Partial discharges in power transformers

Tansformers are among the most responsible and expensive elements of the power system, therefore monitoring the technical condition of the transformer is an important and urgent task. One of the signs of aging of insulation in a transformer may be partial discharges (PD), leading to its further failure. The use of monitoring systems based on PD analysis makes it possible to prevent the severe consequences of short circuits in transformers and, as a result, achieve significant economic effects during the operation of controlled high-voltage equipment. The transformer monitoring system based on the partial discharge method is complex and not always effective, because fixed discharges in transformer insulation can be observed for years and do not lead to breakdown, and among the PD it is not always possible to identify the leader's transition to the through phase in time, which creates sufficient conditions for breakdown. In practice, the discharge test is performed using chromatographic analysis of dissolved gases in oil (CADG) or by measuring discharge activity. The article analyzes the effectiveness of PD control methods and the need to develop mathematical models of partial discharges that allow studying the dynamics of the streamer-leader transition in controlled isolation.

Connected system for monitoring electrical power transformers using thermal imaging

The stable supply of electricity is essential for the industrial activity and economic development as well as for human welfare. For this reason, electrical system devices are equipped with monitoring systems that facilitate their management and ensure an uninterrupted operation. This is the case of electrical power transformers, which usually have monitoring systems that allow early detection of anomalies in order to prevent potential malfunctions. These monitoring systems typically make use of sensors that are in physical contact with the transformer devices and can therefore be affected by transformer problems. In this work we demonstrate a monitoring system for electrical power transformers based on temperature measurements obtained by means of thermal cameras. Properly positioned, the cameras provide thermal data of the transformer, the incoming and outgoing lines and their surroundings. Subsequently, by appropriate image processing, it is possible to obtain temperature series to monitor the transformer operation. In addition, the system stores and processes thermal data in external equipment (placed in locations other than the transformers) and is equipped with a communications module that allows secure data transmission independent of the power grid. This aspect, along with the fact that there is no need to have physical contact with the transformer, make this approach safer and more reliable than standard approaches based on sensors. The proposed system has been evaluated in 14 stations belonging to the Spanish power grid, obtaining accurate and reliable temperature time series.

Low-cost real-time internet of things-based monitoring system for power grid transformers

<p><span lang="EN-US">One of the most common causes of blackouts is unexpected failures at power system transformer levels. The purpose of this project is to create a low-cost Internet of things (IoT)-based monitoring system for power grid transformers in order to investigate their working status in real-time. Our monitoring system’s key functions are the gathering and display of many metrics measured at the transformer level (temperature, humidity, oil level, voltage, vibration, and pressure). The data will be collected using various sensors connected to a microcontroller with an embedded Wi-Fi module (DOIT Esp32 DevKit v1), and then supplied to a cloud environment interface with a full display of all the ongoing changes. This technology will provide the power grid maintenance center with a clear image of the transformers’ health, allowing them to intervene at the right time to prevent system breakdown. The method described above would considerably improve the efficiency of a power transformer in a smart grid system by detecting abnormalities before they become critical.</span></p>

Integrating IoT Technology for Improved Distribution Transformer Monitoring and Protection

Abstract This research study focuses on developing and implementing an IoT-based distribution transformer monitoring and protection system. The traditional methods of transformer protection and monitoring have proven to be inefficient and time-consuming, leading to the need for a more modern and effective solution. In this study, a low-cost prototype system is proposed to handle and control the main functions and problems of the distribution transformer through the internet. The proposed system allows for easy monitoring and protection of the transformer, enabling electric companies to improve efficiency and reduce labour and tool costs. The system is validated using Proteus software to simulate and obtain results from the hardware. The system results are displayed in multiple ways, including LCD, system bar, and internet, making it easier for electric companies and consumers in developing countries like Pakistan to monitor and control distribution transformers efficiently. This research study aims to provide valuable insights into the effectiveness of IoT-based distribution transformer monitoring and protection systems and their potential benefits in enhancing transformer performance and efficiency.

A Microcontroller-based Transformer Monitoring System for Physical Protection: A case for Zimbabwe

A Microcontroller-based Transformer Monitoring System for Physical Protection: A case for Zimbabwe

Retraction Note: Design of Online Monitoring System for Distribution Transformer Based on Cloud Side End Collaboration of Internet of Things

Retraction Note: Design of Online Monitoring System for Distribution Transformer Based on Cloud Side End Collaboration of Internet of Things

Lightning caused overvoltages on power transformers recorded by on-line transient overvoltage monitoring system

Transient overvoltages generated by lightning strikes or switching operations represent a significant risk to bushings and windings of power transformers. They cause stress on the insulation system and can, over time, cause dielectric failure and damage to power transformers. Many transformer failures are reported as dielectric failures and they are not necessarily linked to any particular event when they occur but may be the result of prior damage from transient overvoltage events. Lightning and switching overvoltage waveforms appearing at transformer terminals in real operating conditions may significantly differ from standard impulse voltage waveforms used during laboratory testing. The number and amplitudes of overvoltages which stress the insulation depend on various parameters such as the lightning strike density in the considered area, since it determines how often the transformer is stressed by lightning overvoltages. Since the overvoltage amplitudes at transformer terminals are usually unknown, an on-line overvoltage transient recorder can be used with the ability to sample, analyse and store transients in real-time. In this paper, an on-line transient overvoltage monitoring system (TOMS) for power transformers is presented that is capable to continuously record in real-time various kinds of transient overvoltages such as lightning or switching overvoltages. Special attention is paid to lightning caused transient overvoltages recorded at the terminals of 150 MVA power transformer. Recorded waveforms originating from lightning strikes to overhead lines are correlated with data from the lightning location system (LLS) and supervisory control and data acquisition (SCADA) system. Collected data about overvoltage stresses can be used as the basis for the assessment of the transformer insulation condition, estimation of health index and for analysis of various kinds of events such as faults or equipment failures.

Design and Implementation of a GSM-based Monitoring System for a Distribution Transformer

Maintaining the distribution power substation's health and reliability has been an issue for many years. A typical transformer measurement device detects only one transformer characteristic, such as power, current, voltage, or phase. While some methods can detect several factors, the acquisition time and operation parameters are too long, and the testing speed is insufficient. However, a monitoring system that can check the temperature level of the transformer continuously and predict faults such as overheating, overcurrent, after which the fault diagnosis is relayed to the base station through a GSM modem is developed in this paper. After receiving the message of any abnormality remedial action can be taken immediately to prevent any catastrophic failures of distribution transformers. Key components used in this development are the ATmega328 microcontroller, the GSM modem, the LM35 temperature sensor, ACS712 current sensor, and so on. The GSM-based monitoring of the distribution transformer is preferable to manual monitoring since it is not always possible to monitor the ambient temperature rise, load current manually.

Health Monitoring System for Transformer by using Internet of Things (IoT)

Transformer is an important device in electrical field that used to transfer electricity from one circuit to another with changing the voltage level. The transformer will be having a problem such as increasing in temperature and make a lot of noise. Typical transformer measurement system will take time for testing and less accurate. The aim of this project is to design the IoT system for monitoring and evaluate the performance of the transformer current, sound and temperature. The monitoring system using three types of sensors to sense the current, sound and temperature. ESP32 is being used to keep and process the data before sending to the Blynk application to show the value by using internet. Two different conditions are being tested to the transformer which are transformer without load and with load. Ammeter and ACS712 current sensor are used to get the current value. Besides, for temperature value used two equipment which are using thermometer and MLX90614 temperature sensor. The transformer sound obtained from the sound sensor. The data will be display in Blynk application. The result obtained show the health monitoring system for transformer by using Internet of Things is acceptable because has low error and save time to measure the parameters.

Design and Research of Power Information Acquisition System for Smart Grid

In order to realize the of all electric power users “full coverage, full acquisition”, this article in view of the situation of electric power information collection launches the research, in the same power load management system at home and abroad, substation meter reading system, residents centralized meter reading system such as electric field data acquisition system on the basis of comparative analysis, combining with the characteristics of the power of the county area. A new information acquisition system for power users is designed. By adopting the technology of layered design and multi-channel access, the concrete solution ideas and methods of supporting multi-terminal, multi-protocol, multi-channel and multi-application services are put forward. Results show that the system of collection scope covers the existing centralized meter reading system, distribution transformer monitoring system and load management system, the system conforms to the national modernization development direction and the requirement of electric power marketing information, to be advanced and practical, compared with the similar systems in a dominant position, have achieved satisfactory results in practical operation, the application prospect is very broad.

RETRACTED ARTICLE: Design of Online Monitoring System for Distribution Transformer Based on Cloud Side End Collaboration of Internet of Things

RETRACTED ARTICLE: Design of Online Monitoring System for Distribution Transformer Based on Cloud Side End Collaboration of Internet of Things

Power Electric Transformer Fault Diagnosis Based on Infrared Thermal Images Using Wasserstein Generative Adversarial Networks and Deep Learning Classifier

The safety of electric power networks depends on the health of the transformer. However, once a variety of transformer failure occurs, it will not only reduce the reliability of the power system but also cause major accidents and huge economic losses. Until now, many diagnosis methods have been proposed to monitor the operation of the transformer. Most of these methods cannot be detected and diagnosed online and are prone to noise interference and high maintenance cost that will cause obstacles to the real-time monitoring system of the transformer. This paper presents a full-time online fault monitoring system for cast-resin transformer and proposes an overheating fault diagnosis method based on infrared thermography (IRT) images. First, the normal and fault IRT images of the cast-resin transformer are collected by the proposed thermal camera monitoring system. Next is the model training for the Wasserstein Autoencoder Reconstruction (WAR) model and the Differential Image Classification (DIC) model. The differential image can be acquired by the calculation of pixel-wise absolute difference between real images and regenerated images. Finally, in the test phase, the well-trained WAR and DIC models are connected in series to form a module for fault diagnosis. Compared with the existing deep learning algorithms, the experimental results demonstrate the great advantages of the proposed model, which can obtain the comprehensive performance with lightweight, small storage size, rapid inference time and adequate diagnostic accuracy.

Design of sound monitoring and fault diagnosis system for dry-type transformers

This paper focuses on several common fault phenomena and possible causes of dry-type transformer. MATLAB programming realizes the processing of acoustic signal. Through the encapsulation of GUI interface program and the processing and analysis of a large number of fault signals, fault sets are formed. Through the actual transformer fault acoustic signal processing, the main fault frequency of the acoustic signal is extracted and used as an important criterion for transformer fault detection.