Monitoring Of Electrical Equipment Research Articles

Condition Assessment of Electrical Power Devices using Infrared Thermography: A Review

Departures from the stable operation of electrical equipment usually appear as changes in parameters such as vibrations, pressure, temperature, power, and flow. Excessive changes in these parameters will indicate significant damage, leading to total or partial breakdown of equipment. This paper reviews the use of infrared thermography (IRT) for the operating condition temperature monitoring of electrical equipment. Infrared thermography is a contactless method that is based on thermal radiation principles and used for determining the temperature distribution on the surface of objects by measuring the intensity of radiation emitted by the object. The basic principles underlying IRT are presented in this review, showing its pros and cons, the various IRT methods, processes involved in IRT image acquisition, intelligent techniques for IR image analysis, and factors that affect the accuracy of results, including technical, environmental, and procedural factors. Also presented is an overview of common applications of IRT condition monitoring to solar energy, medicine, civil infrastructure, and aerospace equipment. Common faults in electrical equipment, including those in transformers, circuit breakers, feeder pillars and arrestors, are discussed with illustrations from recent research studies. These studies have demonstrated the effectiveness of IRT in the predictive maintenance of electrical installations. Hence, the replication of the techniques reviewed in this work is recommended so as to reduce current costs incurred in maintaining and replacing damaged equipment, and the associated time losses.

Application of industrial robot based on infrared sensor technology in monitoring of electrical equipment

Abstract In this paper, infrared sensor technology is applied to the monitoring of electrical equipment, and a mathematical model of infrared image processing is built. In addition, the infrared image recognition model of electrical equipment is combined with the improved CenterNet, and ResNeXt50, which has stronger feature extraction capability, is used as the backbone network of CenterNet to improve detection accuracy. An infrared dataset of electrical equipment is used to verify the effectiveness of the improved CenterNet model and the other two models. Finally, the faults in substation equipment are analyzed and studied for real cases applied in practical work as an example. The results show that the algorithm in this paper has an mAP value of 93.7% and a frame rate of 40/S, which can balance the detection accuracy and detection speed in the electrical equipment infrared scene. Compared to traditional electrical equipment detection methods, the detection method of infrared sensor imaging is more reliable, with a 90% accuracy rate, and the highest has reached 100%. The article analyzes the characteristics and relationship between the local heating and temperature increase of the fault point of the equipment concerned, summarizes the fault judgment method, and verifies the effectiveness of infrared sensor imaging technology.

Research on Electrical Equipment Monitoring and Early Warning System Based on Internet of Things Technology

With the rapid increase of global population, the power load increases rapidly, and the electrical equipment such as generator, transformer, and power line in the power system is an important basis for production and life. Its safe operation is of great significance because it is related to people’s economic development and life stability. In recent years, with the rapid development of Internet of Things technology, the Internet of Things and electrical equipment safety early warning are combined to use their respective advantages to provide a new way for electrical equipment monitoring and early warning system. Through a variety of key technologies of the Internet of Things, such as sensor technology, network communication technology, and cloud computing technology, data, information exchange, and communication between electrical equipment and Internet technology are carried out according to the agreed protocol, and the safety of the operation status of electrical equipment is monitored in real time, so as to prevent power equipment failure and other problems. Based on this background, this paper studies the Internet of Things technology and electrical equipment monitoring and early warning system and analyzes its three-tier network architecture mode from the Internet of Things technology, namely, perception layer, network layer, and application layer. The combination of cloud computing and edge computing is studied and analyzed to provide theoretical support for the research of electrical equipment monitoring and safety early warning system. The wireless sensor network equipment is also installed on the electrical equipment through the Internet of Things technology to transmit data to the base station, so as to monitor whether the equipment operates safely. The monitoring and early warning system of wireless sensor system based on Internet of Things is given through case experiment. This system realizes relevant intelligent application services, which can not only ensure the stability of information transportation but also real-time monitoring of electrical equipment, early warning, shorten troubleshooting time, reduce the workload of power station staff, and achieve the functions of safety early warning, emergency command, and control. It is of great significance to the monitoring and early warning system of electrical equipment.

Online dissolved gas analysis used for transformers\xa0\u2013 possibilities, experiences, and limitations

At a time when the optimal utilization of resources is becoming ever more important, the condition monitoring of electrical equipment is also becoming increasingly essential. The dissolved gas analysis (DGA), an internationally accepted method for the condition assessment of transformers, plays a key role in this respect. By continuously monitoring the key gases in a transformer, incipient faults can be detected at an early stage and suitable maintenance measures can be initiated. This ability to plan maintenance measures may provide the key to cutting costs and increasing the overall system reliability. In free-breathing transformers, degassing or gas dissolving can occur over time, which may influence the interpretation basis of the DGA. The reason for this is that molecules always strive for equal distribution in a chemical-physical system. Various theories, which are based on the determination of physical and chemical material properties, can be used to describe these processes. Here, the Henry constants describe the concentration equilibrium at the phase interfaces and the diffusion coefficients describe the movement of the gas molecules dissolved in mineral oil. This paper presents mathematically or experimentally determined values and compares them with the literature. Furthermore, it describes the influence of the key gases investigated and the temperature dependence.

Design of Motor Intelligent Monitoring and Fault Diagnosis System Based on LoRa

Aiming at the characteristics of low intelligence of the current motor cluster and superconducting electrical equipment monitoring system, high construction and maintenance costs of wired transmission methods. The wireless sensor network constructed by LoRa technology is applied to the motor operating state monitoring system, while the data transmission efficiency of the system is improved by optimizing the topology of the wireless sensor network (WSN) and processing the transmission data redundancy. The system uses STM32F407ZET6 as the main controller, builds an offline operating state library, and uses HMM model for training to achieve real-time acquisition and fault diagnosis of the status information of the cluster system motor's voltage, current, speed, and position. Practical application shows that the system is running well and can realize the acquisition of the motor's running status and fault diagnosis.

A Method for Segmentation of Transformer Oil Level Region Based on Infrared Image

With the rapid development of computer and artificial intelligence technology, infrared images are playing a higher and higher value in the state monitoring of electrical equipment in substations. Aiming at the detection of the oil level area of the oil conservator in the substation, in order to accurately obtain the oil level information, this paper proposes a transformer oil level detection method combining maximum entropy threshold segmentation algorithm and SLIC (simple linear iterativeclustering) segmentation algorithm. First, the original infrared image is pre-processed by image cutting technology to obtain the infrared image of the oil pillow area; the green channel image is extracted, and then the infrared image is segmented by the maximum entropy threshold segmentation algorithm to remove the background interference and obtain the target area of the electrical equipment. Finally, SLIC is used to super the pixel segmentation algorithm performs segmentation to obtain the oil level area. Experiments show that the algorithm can clearly segment the oil level area of the oil pillow, and it has certain practical value.

RETRACTED ARTICLE: Intelligent electrical equipment fiber Bragg grating temperature measurement system

Fiber Bragg gratings are widely used in electrical equipment monitoring, structural health monitoring, power system fault monitoring and other actual detection occasions due to the unique advantages of anti-electromagnetic interference, low fusion loss, high sensitivity, small size, and easy distributed measurement. Fiber grating has been widely used in actual detection occasions such as electrical equipment monitoring, structural health monitoring, and power system fault monitoring. The temperature measurement of the motor based on fiber grating sensing technology not only has strong anti-electromagnetic interference ability, but also can realize multi-point distributed temperature measurement. The research of fiber grating temperature measurement system for intelligent electrical equipment is of great significance. This article mainly studies the optical fiber grating temperature measurement system of intelligent electrical equipment. This paper designs the temperature measurement system architecture, including data acquisition layer, data monitoring layer and remote monitoring layer. Complete the system design from two aspects: hardware design and software design. In the hardware part, the functions of the temperature sensor module transmission system, signal demodulation system, PLC and host computer are analyzed. Use FTM3501 fiber temperature converter is used to realize the automatic electrical fiber grating. Experimental results show that the thermometer has alarm and display analysis functions. The measurement uncertainty is analyzed, and the uncertainty of the calibration result of the fiber Bragg grating temperature sensor is 0.0725 °C; the fault judgment time of the system in this paper does not exceed 4.73 s, while the fault judgment time of the control group is more than 4.73 s. Compared with the control group, it has a faster fault accuracy speed. The system has high measurement accuracy and good stability, can be applied to actual temperature measurement systems, and has certain practical value.

A Data-Driven Long Time-Series Electrical Line Trip Fault Prediction Method Using an Improved Stacked-Informer Network.

The monitoring of electrical equipment and power grid systems is very essential and important for power transmission and distribution. It has great significances for predicting faults based on monitoring a long sequence in advance, so as to ensure the safe operation of the power system. Many studies such as recurrent neural network (RNN) and long short-term memory (LSTM) network have shown an outstanding ability in increasing the prediction accuracy. However, there still exist some limitations preventing those methods from predicting long time-series sequences in real-world applications. To address these issues, a data-driven method using an improved stacked-Informer network is proposed, and it is used for electrical line trip faults sequence prediction in this paper. This method constructs a stacked-Informer network to extract underlying features of long sequence time-series data well, and combines the gradient centralized (GC) technology with the optimizer to replace the previously used Adam optimizer in the original Informer network. It has a superior generalization ability and faster training efficiency. Data sequences used for the experimental validation are collected from the wind and solar hybrid substation located in Zhangjiakou city, China. The experimental results and concrete analysis prove that the presented method can improve fault sequence prediction accuracy and achieve fast training in real scenarios.

Analysis of fiber-optic sensors for diagnostics and monitoring of electrical equipment

The article describes the basic principles to date; one of the applications of fiber-optic technologies for measuring purposes is fiber-optic multi-sensor systems. Fiber-optic multi-sensor systems include quasi-distributed sensor networks built on the basis of fiber-optic sensors, usually based on fiber Bragg gratings. One of the main advantages of fiber Bragg gratings is the unique way of converting the measured value into a change in the wavelength of radiation passing through and/or reflected from the grating, as well as the ease of manufacturing. fiber Bragg gratings have proven themselves well and are widely used in construction, oil production, energy, aerospace engineering, etc.

Detection of decomposition products of C4F7N-CO2 gas mixture based on infrared spectroscopy

Heptafluoroisobutyronitrile (C4F7N)-carbon dioxide (CO2) gas mixture has been gradually applied as an excellent environment-friendly insulating medium. The research on the decomposition characteristics of C4F7N-CO2 gas mixture not only helps to better understand its decomposition mechanism under insulation fault, but also provides effective basis for monitoring the insulation state of electrical equipment based on decomposition products analysis. Due to infrared spectroscopy has good potential for on-line monitoring of electrical equipment, this paper studies the feasibility of the decomposition products detection by infrared spectroscopy. The infrared spectrum of C4F7N and its decomposition products were obtained by theoretical calculation. The possibility of detection by infrared spectroscopy was theoretically analyzed. The experimental platform for overheating decomposition of C4F7N-CO2 gas mixture was built, and a long path gas cell with 4 m light path was developed for the detection of trace decomposition products. The results show that C3F6, COF2 and CO can be effectively detected by infrared spectroscopy with the long path gas cell. At last, the decomposition experiments under different temperature and pressure have been employed and the decomposition products growth trend has been analyzed. The study illustrates the detection feasibility of decomposition products of C4F7N-CO2 gas mixture by infrared spectroscopy, and lays a foundation for future on-line monitoring technology of electrical equipment with the gas mixture based on infrared spectroscopy.

Development of a continuous thermal control device for high-voltage equipment

The development of devices based on IR microcameras that can operate continuously inside, for example, a distribution rack (cabinet) with high-voltage power equipment, remote transmission of information about the thermal state of electrical equipment is of interest. An autonomous device was developed for searching and monitoring an area with a malfunction in the infrared image of high-voltage electrical installations. The device is intended for continuous monitoring of the technical condition of electrical equipment in closed complete installations with no access directly in the operating condition under voltage. The construction concept and technical solutions of an experimental industrial prototype of an automated system device for continuous monitoring of electrical equipment in the infrared image and video surveillance of power facilities are presented. The device is characterized by optimal parameters for monitoring the technical condition of the equipment operating under voltage and the ability to transmit information and accumulate a data bank.

Smart embarked electrical network based on embedded system and monitoring camera

To improve the quality of life and its comfort with more security, the world of transport is moving towards the all-electric. This imposes an embarked electrical network type operation, and this network based on parallel alternators connecting, which requires more energy and needs synchronisation with identical phases between alternators. In addition, some conditions must be respected to avoid energy crises and increase the efficiency of the system. To ensure the stability and protection of this type of system, the control will be performed by a reliable controller with remote control and monitoring of all data in real time. In this paper, we realise a prototype of protection and monitoring of electrical equipment using a Raspberry Pi as an intermediate embedded system and an RPi camera. In addition, the communication between the electrical system and the web application will be done by Json file or by data stored in the database. For any change in the desired values, the electrical protection system sends a message and a musical warning to the website in real time. In addition, the monitoring will be generated by the FIFO memory for image processing and the servomotor to control the direction of the RPi camera.

Application of Transformer Online Monitoring Data in State Evaluation

The safety, stability and reliability of electrical equipment are directly related to the operation of the electrical system. Discovering various electrical equipment problems and effective treatment of the problem without delay will minimize the probability of electrical equipment failure. In electrical equipment monitoring and maintenance, Application of real-time monitoring is one of the most important elements. Real-time monitoring node can detect equipment failure promptly which will avoid the expansion of accident caused by the long test period or manual inspection. Working time of online monitoring nodes is limited by power consumption of node itself. Application of routing protocol can optimize energy consumption and extend working time of nodes.

Research on Routing Protocol of Real-time Monitoring Nodes in Transformer Substation

The safety, stability and reliability of electrical equipment are directly related to the operation of the electrical system. Discovering various electrical equipment problems and effective treatment of the problem without delay will minimize the probability of electrical equipment failure. In electrical equipment monitoring and maintenance, Application of real-time monitoring is one of the most important elements. Real-time monitoring node can detect equipment failure promptly which will avoid the expansion of accident caused by the long test period or manual inspection. Working time of online monitoring nodes is limited by power consumption of node itself. Application of routing protocol can optimize energy consumption and extend working time of nodes.

Inductively coupled on‐line impedance measurement for condition monitoring of electrical equipment

Off-line periodic maintenance has been used to keep critical electrical equipment in good condition. However, there is an increasing demand for on-line condition monitoring of the electrical equipment for preventive maintenance purposes. An on-line impedance extraction technique based on an inductive coupling approach is explored for the detection of early signs of defects of electrical equipment. The proposed method is demonstrated experimentally using a custom-made transformer with emulated defects as equipment-under-test. To enhance the defect detection capability for impedance deviation, various statistical diagnostic indicators are also investigated. By combining the proposed on-line impedance extraction method and a suitable diagnostic indicator, early detection of the defect in electrical equipment becomes feasible.

Differential models of intellectualization of the processes of monitoring of electrical equipment of traction substations

Differential models of intellectualization of the processes of monitoring of electrical equipment of traction substations

Time Synchronization and Communication Program of Wireless Sensor Network for Online Monitoring of Electrical Equipment

<p><span style="font-size: medium;"><span style="font-family: 宋体;">In wireless sensor networks, for improving the time synchronization perfromance of online monitoring and application of ZigBee protocol, a scheme is designed. For this objective, first of all, the ZigBee protocol specification is summarized, a profound analysis of the hardware abstraction architecture of TinyOS operating system is made; the advantages of the ZigBee protocol compared with the traditional radio technology are comparatively analyzed. At the same time, the node design block diagram based on CC2430 and related development system is provided. In the TinyOS2.x operating system, we analyze CC2430 application program abstract architecture, and on this basis, give the realization process of program design. The research results showed that we achieve an on-line monitoring system based on ZigBee protocol, which has realistic significance of applying ZigBee protocol in wireless sensor network of electrical equipment online monitoring. Based on the above research, it is concluded that the online monitoring system can collect the temperature parameters of the monitored object in real time that it can be widely applied in wireless sensor networks.</span></span></p>

Design and Application of Electrical Equipment Overheating Monitoring Based on Wireless Sensor

A new scheme is proposed to solve the practical problems in th e intelligent monitoring of 220kV substation equipment . By analyzing the changes in temperature of substation electrical equipment , it applies wireless sensor network technology in the inverter station based on the running condition of the equipment . In addition, the acquisition, processing, and communication of temperature information is used as the major research object, and a kind of intelligent monitoring of substation equipment system based on wireless sensor network is designed . B y using C805IF996 chip and nRF905 chip as the main chip s, the hardware and software design of the temperature data acquisition node and sink node are realized. The result show that the e lectrical e quipment b ased on w ireless s ensor has a great impact in o verheating m onitoring . Based on the above finding, a conclusion is drawn that wireless sensor provides an effective solution for the application of intelligent monitoring of electrical equipment.

Application of Infrared Image Recognition Technology in Electrical Equipment

This paper comprehensively summarizes the application of the current infrared image recognition technology in the field of electrical equipment monitoring, and carries out the specific analysis and points out a series of problems of the infrared image recognition technology in the current application. Based on above problems, this paper proposes a brand new application strategy from following three aspects: the equipment color feature recognition, the texture feature recognition and the image recognition of the isolator status in the transformer substation, thereby indicating directions for the realization of thorough application of infrared image recognition technology in the field of electrical equipment monitoring.

Multilevel Thresholding Technique for Contrast Enhancement in Thermal Images to Facilitate Accurate Image Segmentation

The most challenging task in InfraRed Thermography based condition monitoring of electrical equipment lies in the accurate detection of the anomaly. It necessitates an efficient preprocessing technique for increasing the contrast between the background and the anomaly regions in thermographs. In this paper, an efficient multi-level thresholding based enhancement technique is proposed for highlighting the anomaly region from the background. In contrast to the two levels in binary enhancement, n levels are determined based on the histogram bins. Enhanced intensity is calculated based on the range of the level rather than selecting randomly. Strength of the proposed technique is evident in its ability to detect the true edges corresponding to the anomaly. Also the anomaly region is accurately detected to its true size which in turn resulted in the precise estimation of the real time temperature. The error in temperature is approximately 0.5 at an emissivity of 0.7. The proposed technique can be adapted irrespective of thermal profiling of thermographs. However the intensity range must be chosen appropriately. Relationship between emissivity and error in temperature is also provided at the room temperature