On-line Monitoring Equipment Research Articles

Fault data expansion method of permanent magnet synchronous motor based on Wasserstein-generative adversarial network

Aiming at the characteristics of non-smooth, non-linear, multi-source heterogeneity, low density of value and unevenness of fault data collected by the online monitoring equipment of permanent magnet synchronous motor (PMSM), and the difficulty of fault mechanism analysis, this paper proposes a method of PMSM data expansion based on the improved generative adversarial network. First, use the real fault data of the motor to train the model to obtain a mature and stable generative countermeasure network. Secondly, use the generative countermeasure network model to test the remaining data and generate pseudo samples. Finally, use the two-dimensional data analysis method and the time-domain analysis method to generate validity analysis of samples. Aiming at the characteristics of unbalanced motor data, the data expansion method of inter-turn short-circuit faults is carried out based on the data expansion method of the improved generative countermeasure network, and the two-dimensional data analysis method and the time-domain analysis method are used for analysis. The experimental results show that the improved Wasserstein-Generative Adversarial Network (W-GAN) has a better ability to generate fake data, which provides a data basis for the mechanism analysis and machine fault diagnosis of PMSMs. Data analysis results show that the improved W-GAN effectively solves the problem of poor convergence of GAN.

A Novel Principal Component Analysis-Informer Model for Fault Prediction of Nuclear Valves

In this paper, a deep learning fault detection and prediction framework combining principal component analysis (PCA) and Informer is proposed to solve the problem of online monitoring of nuclear power valves which is hard to implement. More specifically, PCA plays the role of dimensionality reduction and fault feature extraction. It maps data with multi-dimensional space to low-dimensional space and extracts the main features. At the same time, the T-square and Q statistic thresholds are also provided to realize abnormal status monitoring. Meanwhile, Informer is a long-term series prediction method. It encrypts and decrypts data through the encoder and decoder to train a prediction model. Through the training of fault data, fault prediction can be realized. Experiments based on the sound waves collected from real valves can be continued, which also illustrates the effectiveness of the PCA–Informer model for fault diagnosis and fault prediction of nuclear power valves. Therefore, the online monitoring and maintenance of nuclear valves and other important equipment, without shutting down the nuclear station, can be achieved.

The design of a long-distance high-efficiency WPT for online monitoring equipment based on a new relay structure

The design of a long-distance high-efficiency WPT for online monitoring equipment based on a new relay structure

Research and Design of Remote Online Supervision System of Coal Mine Electromechanical Equipment

Increasing the supervision of coal mine electromechanical equipment is an urgent need to ensure the safe production of coal mines. This paper designs and develops a coal mine electromechanical equipment supervision system, which includes four parts: mine-side monitoring subsystem, mine-side data acquisition and transmission module, local-side data analysis and storage module, and coal mine electromechanical equipment supervision platform. It realizes coal mine ventilator, drainage pump, air compressors, hoists and other major electromechanical equipment online monitoring and equipment failure analysis. The system satisfies the remote online inspection and management of equipment by multi-level departments, reduces the possibility of enterprise accidents, innovates the supervision mode, and improves the efficiency of supervision.

Design of Distributed Substation High Voltage Electrical Equipment Online Monitoring System Based on Image Segmentation Technology

As an important support mechanism for real-time monitoring and display of system frequency, harmonic voltage, harmonic current, three-phase voltage unbalance, voltage fluctuation and flicker, true power factor and other power quality parameters specified in national standard, especially for fast and accurate data processing and improving test frequency and level, the online monitoring of substation is an important support mechanism for real-time monitoring and display of system frequency, harmonic voltage, harmonic current, three-phase voltage unbalance, voltage fluctuation and flicker, true power factor and other power quality parameters specified in national standard. The research of distributed substation high voltage electrical equipment online system based on image segmentation has important practical value. However, the design system of the online monitoring network of some substation electrical equipment can not effectively follow the development needs of society and industry, and there is an urgent need for effective reform. Based on this, this paper first analyzes the problems existing in the design system construction of online monitoring network of distributed substation electrical equipment, and then gives the construction strategy of online monitoring system of high voltage electrical equipment in view of these problems.

Application of Optimization Technology for Overhaul Decision of Substation Equipment Based on Machine Learning

With the continuous improvement of living standards and the continuous increase of electricity load, the number of power transmission and transformation equipment also increases rapidly. The original maintenance mode is not enough to guarantee the safe operation of the huge power grid. This paper mainly studies the research and application of machine learning based maintenance decision optimization technology for substation equipment. Starting from the technical principles of online monitoring and condition maintenance of substation equipment, this paper has realized an intelligent monitoring and maintenance early warning system combined with deep learning model. The main functions of this system include monitoring device management, operation monitoring and comprehensive display, etc., which can effectively carry out online monitoring and state early warning of substation equipment. It greatly improves the intelligent degree of operation and management of substation equipment, saves the cost of traditional manual monitoring, and effectively prevents the economic loss caused by substation equipment failure, which has far-reaching significance for promoting the construction of smart power grid.

Research on Performance Optimization for Power Big Data Storage based on HBase

With the development of smart grid, the online monitoring of power equipment has become the focus. Due to the increasing of power data, data storage in power grid is faced with great challenge. HBase is widely used for storing and managing power data due to its excellent performance in distributed access to data. However, HBase is prone to write a hot spot problem when storing data. To address this problem, this paper proposes an optimization method for data storage performance based on a dynamic load balancing strategy, which is implemented by pre-partitioning processing, RowKey substitution, and WLC (Weighted Least Connection) algorithm adapted to HBase storage mechanism. Through the experimental analysis of insulator current data storage, the results show that the method improves the performance of HBase cluster parallel data storage.

Detecting pollutant sources and pathways: High-frequency automated online monitoring in a small rural French/German transborder catchment

Great temporal and spatial variability of inputs make comprehensive monitoring in small and middle sized rivers difficult. In this study, relevant inputs in a small river were recorded with suitable online monitoring equipment coupled in mobile water quality monitoring stations, the study area being a transborder catchment with French and German (Saarland federal state) subcatchments. In addition to a pronounced spatial variability necessitating a denser net of measuring points this catchment has also to be assessed in the light of different national regulations. To identify individual pollution sources and weigh their relative importance, relevant parameters were recorded over a representative monitoring period of several months: phosphorus (P) as total phosphorus (TP) and total reactive P phosphorus (TRP), nitrate (NO3–N), ammonium (NH4–N), total organic carbon (TOC), temperature, oxygen (O2), pH, turbidity, and electrical conductivity (EC). The recorded data were subjected to adapted interpretation together with other catchment-related factors. In order to retrieve maximum information from the online data sets the relationships among certain parameter pairs were also analysed for both storm events and low flow periods.Comparison of loads at the different monitoring sites could reliably verify the majority of nutrient inputs originating in the French subcatchment. Additional sampling of output channels from sewage treatment works (STWs) in the Saarland subcatchment revealed that inputs from several decentralised STWs do not result in significant loads, as opposed to inputs from one STW in France. Our holistic approach provides a basis for adopting cost-effective measures to reduce loads in small river catchments as well as cross-border harmonisation of environmental policies.

Porcelain Insulator Crack Location and Surface States Pattern Recognition Based on Hyperspectral Technology.

A porcelain insulator is an important part to ensure that the insulation requirements of power equipment can be met. Under the influence of their structure, porcelain insulators are prone to mechanical damage and cracks, which will reduce their insulation performance. After a long-term operation, crack expansion will eventually lead to breakdown and safety hazards. Therefore, it is of great significance to detect insulator cracks to ensure the safe and reliable operation of a power grid. However, most traditional methods of insulator crack detection involve offline detection or contact measurement, which is not conducive to the online monitoring of equipment. Hyperspectral imaging technology is a noncontact detection technology containing three-dimensional (3D) spatial spectral information, whereby the data provide more information and the measuring method has a higher safety than electric detection methods. Therefore, a model of positioning and state classification of porcelain insulators based on hyperspectral technology is proposed. In this model, image data were used to extract edges to locate cracks, and spectral information was used to classify the surface states of porcelain insulators with EfficientNet. Lastly, crack extraction was realized, and the recognition accuracy of cracks and normal states was 96.9%. Through an analysis of the results, it is proven that the crack detection method of a porcelain insulator based on hyperspectral technology is an effective non-contact online monitoring approach, which has broad application prospects in the era of the Internet of Things with the rapid development of electric power.

Research on Information Coding of Smart Substation Facing “Two Networks Convergence”

With the popularization and application of “Internet of Things” technologies such as equipment online monitoring and intelligent inspection in the field of substation operation and maintenance, the number of subsystems in smart substations has increased rapidly, and the types of information and the amount of information have shown explosive growth. However, due to disorder and irregular expression, the potential value of a large amount of information is difficult to be effectively used. This paper proposes a “system code-device code-information code” information coding technology that serves the operation and maintenance of information equipment to make information orderly and standardized. This technology lays the foundation for quickly and accurately finding the required information, fully mining and effectively using the value of information, strengthening information fusion between subsystems, and realizing digital management and control of substation equipment information.

Quasi-Static Modeling and Optimization of Two-Layer PCB Resonators in Wireless Power Transfer Systems for 110-kV Power Grid Online Monitoring Equipment

The expanding functions of the emerging smart grid have posed new technical challenges on the power supplies for online monitoring systems in the transmission and distribution networks. A recent study has suggested that magnetic energy around the transmission line can be harvested and delivered wirelessly to the monitoring equipment through printed circuit board (PCB) resonators embedded inside an insulation rod. This article presents a rigorous analysis, modeling, and optimization of such PCB resonators. A new closed-form quasi-static model of the PCB resonators is derived. A fully automated simulation-driven optimization framework is constructed to enhance the quality factor of the resonator. Practical measurements show that the optimal design improves the quality factor ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> factor) from 52 to 132 as compared to the existing trial-and-error design. The corresponding wireless power transfer efficiency in a 20-W prototype across a 1.14-m distance improves significantly from 11 to 46%.

Research on Real-time Application of Ubiquitous Power Internet of Things Information Based on Smart Sensor Technology

In view of the current situation that it is difficult for the power control centre to take into account the multi-source information to conduct effective risk assessment on the dynamic process of the power grid, the challenges and opportunities faced by the power grid risk assessment under the background of the ubiquitous power Internet of Things are pointed out. The framework of the dynamic risk assessment system for the grid of the Internet of Things. At the same time, in view of the monitoring and fault diagnosis functions of sensor technology, especially gas sensor in transformer oil chromatographic monitoring, the application of ubiquitous power Internet of Things sensor technology in online monitoring of power equipment is discussed. First, the application of online monitoring of power equipment is analysed. The inevitable trend will then discuss in detail the monitoring and fault diagnosis functions of gas sensor technology in transformer oil chromatographic monitoring from sensor technology, gas sensor, transformer oil chromatographic device, and gas analysis method.

Application of PAT-Based Feedback Control Approaches in Pharmaceutical Crystallization

Crystallization is one of the important unit operations for the separation and purification of solid products in the chemical, pharmaceutical, and pesticide industries, especially for realizing high-end, high-value solid products. The precise control of the solution crystallization process determines the polymorph, crystal shape, size, and size distribution of the crystal product, which is of great significance to improve product quality and production efficiency. In order to develop the crystallization process in a scientific method that is based on process parameters and data, process analysis technology (PAT) has become an important enabling platform. In this paper, we review the development of PAT in the field of crystallization in recent years. Based on the current research status of drug crystallization process control, the monitoring methods and control strategies of feedback control in the crystallization process were systematically summarized. The focus is on the application of model-free feedback control strategies based on the solution and solid information collected by various online monitoring equipment in product engineering, including improving particle size distribution, achieving polymorphic control, and improving purity. In this paper, the challenges of feedback control strategy in the crystallization process are also discussed, and the development trend of the feedback control strategy has been prospected.

Research on On-line Monitoring Technology of Secondary Equipment in Smart Substation

First, the overall design principles of the online monitoring of secondary equipment in smart substations are studied. Then, the overall architecture of online monitoring of secondary equipment is proposed. Finally, the advanced application of the online monitoring system of secondary equipment is analyzed. The method proposed in this paper improves the online monitoring and management level of secondary equipment, and provides technical support for the regulation and condition maintenance of secondary equipment.

Field analysis of switching transient process on the 35 kV side of a substation connecting to PV plant

To reveal the real causes of two explosion faults of potential transformer (PT) on a substation connecting to PV plant, an on-line monitoring equipment with a sampling rate of 40 MS/s for the transient voltage was installed on the secondary side of the PT. By switching the 35kV bus bar onto the main transformer, the switching transient processes on the PT were obtained. The field measured transient process indicated that the switching transient voltage caused by switch operation induced the resonance between the charging circuit and the PT and the resonance may be the direct cause of the PT explosion. The effective measures to suppress resonance in this substation are proposed.

Development of Degassing Device for Transformer oil Based on Headspace Degassing Method

By analyzing the type and content of gas in oil, the latent faults in equipment can be found as soon as possible, and the faults can be monitored at any time. In this paper, a degassing device based on headspace degassing method is designed. The oil circuit adopts the way of oil pump and battery valve to complete the oil inlet and oil discharge. Through stirring, the dissolved gas in the oil can reach the dynamic balance in the gas-liquid two-phase, so as to realize the rapid and effective separation of the gas in the oil. At the same time, after degassing, the oil can be sent back to the body. In addition, the degassing verification of the device is carried out, and the effect is good. In the next step, the device can be integrated with the gas detection equipment to form a sealed cabinet detection equipment, which is of great significance for the technical realization and engineering application of online monitoring and live detection of power equipment.

Syncretic Application of IBAS-BP Algorithm for Monitoring Equipment Online in Power System

With the integration and development of sensor technology and control technology, the construction of smart grid is in the ascendant. But existing technology is still insufficient in the field of equipment monitoring. Therefore, it is difficult to accurately determine if SF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> breaker or other equipment have faults in its operational mechanisms which can cause false action and thus impact the safety of power grids. In this paper, the IBAS (Improved Beetle Antennae Search) algorithm and the BP neural network are combined and used in the monitoring system for the first time. To improve the beetle search algorithm, a single beetle is improved into a population in the iterative process of algorithm. The measured opening (closing) current data is used to verify the accuracy of different algorithms. The results show that compared with PSO-BP (particle swarm optimization, PSO) model, GA-BP (genetic algorithm, GA) model and BAS-BP model, the IBAS-BP model not only effectively avoids the possibility of local minimums but also has higher prediction accuracy and better robustness. The number of iterations in the IBAS-BP algorithm is only 38, and the average error is only 0.1%. According to this, it is possible to make real-time diagnosis of faulty states in SF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> breaker including iron core jams, low operating voltages, poor contact of auxiliary switches, jammed operating mechanisms, and barometer failures in SF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> . At the same time, the IBAS-BP algorithm can be applied to wind turbine power prediction and other occasions since model regression determination coefficient R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of the training set is up to 0.9753 and the relative average error is only 0.25%. The results prove that the IBAS-BP algorithm has obvious advantages and fairly good universality. It can be further promoted and applied to power systems to provide reference for optimizing the online monitoring of power equipment.

Study on internet of things architecture of substation online monitoring equipment

This paper introduces the structure of the existing on-line monitoring system, and analyzes the problems of poor compatibility and reliability of IED in different manufacturers. Then it gives two ways to transform the on-line monitoring system of substation equipment to the Internet of things, which is to retain IED and to remove IED. On this basis, this paper introduces the application scenarios of the on-line monitoring system of substation equipment connected to the Internet of things, including accurate perception of equipment status, accurate evaluation of equipment status and intelligent research and judgment of equipment status.

Characteristics of high-temperature equipment monitoring using dry-coupled ultrasonic waveguide transducers

Dry-coupled ultrasonic waveguide transducers enable the long-term monitoring of the critical mechanical components working in high temperature environment. In these systems, waveguide units are applied to isolate the vulnerable piezoelectric elements from the harsh measurement zones. However, the disturbance cannot be overcome, which may arise from inevitably temperature fluctuation and the indirect measurement in long term monitoring. In the present research, a high temperature experimental system using dry-coupled ultrasonic waveguide transducers is investigated. The disturbances caused by this measurement system are studied in the heating period, holding period and cooling period, respectively. Moreover, the correlations between the group velocities, dispersion curves, signal amplitudes, waveforms under temperature variations are investigated. At last, the dispersion characteristics of the shear horizontal wave in high temperature stainless steel plate are analyzed. This work strives to pave a solid foundation for the application of SH waves to online monitoring of high-temperature equipment using dry-coupled ultrasonic waveguide transducers.

Design of Equipment Online Monitoring and Fault Diagnosis Platform Based on IOT

Complex equipment in the process industry is the core productivity of automated manufacturing. Fault prediction, diagnosis, and health maintenance of such equipment are important factors to ensure continuous and reliable operation of production. However, how to adjust the fault judgment rules in time according to the changes in the operating conditions and process of the equipment is the core design content of the fault diagnosis platform. The platform of this paper presented is mainly based on stress wave condition monitoring technology, which combined with process monitoring to form an online monitoring data set of the equipment; Then use the data fusion technology to fuse the collected signals at the decision-making level, and finally combine principal component analysis and the BP neural network to predict the equipment’s health trend and fault type.