Condition Monitoring Of Power Equipment Research Articles

Segmentation and Tracking Based on Equalized Memory Matching Network and Its Application in Electric Substation Inspection

With the wide application of deep learning, power inspection technology has made great progress. However, substation inspection videos often present challenges such as complex backgrounds, uneven lighting distribution, variations in the appearance of power equipment targets, and occlusions, which increase the difficulty of object segmentation and tracking, thereby adversely affecting the accuracy and reliability of power equipment condition monitoring. In this paper, a pixel-level equalized memory matching network (PEMMN) for power intelligent inspection segmentation and tracking is proposed. Firstly, an equalized memory matching network is designed to collect historical information about the target using a memory bank, in which a pixel-level equalized matching method is used to ensure that the reference frame information can be transferred to the current frame reliably, guiding the segmentation tracker to focus on the most informative region in the current frame. Then, to prevent memory explosion and the accumulation of segmentation template errors, a mask quality evaluation module is introduced to obtain the confidence level of the current segmentation result so as to selectively store the frames with high segmentation quality to ensure the reliability of the memory update. Finally, the synthetic feature map generated by the PEMMN and the mask quality assessment strategy are unified into the segmentation tracking framework to achieve accurate segmentation and robust tracking. Experimental results show that the method performs excellently on real substation inspection scenarios and three generalized datasets and has high practical value.

A method for power equipment condition monitoring and fault location based on improved ant colony algorithm

A method for power equipment condition monitoring and fault location based on improved ant colony algorithm

A method for power equipment condition monitoring and fault location based on improved ant colony algorithm

A method for power equipment condition monitoring and fault location based on improved ant colony algorithm

PD Signal Detection Model Based on Multiscale Simulation of EM Generation and Transmission

Nowadays, the digital twin method has been gradually applied to power equipment condition monitoring. Since a main fundamental part of the digital twin is a simulation process with better fitting for the real one, this paper is devoted to building up a PD detection model to simulate the generation and transmission of electromagnetic (EM) wave signals caused by PDs, especially those inside the void defects. This model combines the micro discharge process with the transmission process inside certain geometry structures. Specifically, two commonly used methods are applied to simulate the processes correspondingly. The dielectric barrier discharge (DBD) model is used for the PD simulation inside void defects, while the finite-difference time-domain (FDTD) method is for the EM signal transmission process. The pulsed discharge current can be obtained during the simulation of DBD, which can function as the excitation source of the transmission process solved by FDTD. According to the PD detection model, the influences on the simulation results are discussed, including the impact on the discharge current caused by coefficient variation and the impact on the transmission process caused by the power equipment geometry structure and materials. As mentioned above, this paper is aimed at building a direct relationship between the observed EM signal and the micro discharge process, which reveals the physical process of PD signal measurement in virtual space that can provide more features for PD pattern recognition or noise elimination, and further the mechanism and theoretical analysis support for digital twin application.

Multimodal image registration for power equipment using clifford algebraic geometric invariance

Imaging inspection and video monitoring are important means for power equipment condition monitoring and diagnosis. However, single model image cannot supply sufficient information for power equipment condition monitoring, such as infrared images and visible images. The image registration can bridge this gap. This paper develops an image registration method fused by infrared and visible images, which constructs the principal direction of corner points using neighborhood contour point geometric invariance under Clifford algebraic space. Simultaneously, we also design a law for fitting optimal spatial information in the main direction of corner points, where the Clifford rotation operator is utilized to realize the spatial transformation of the image to be registered. The experiment reveals that the average registration error is less than 3 pixels by this developed method, reaching sub-pixel registration, subsequently obtaining satisfactory fusion effect of power equipment. This algorithm enriches the multimodal image registration scheme and meets the requirements of monitoring power equipment.

A Data-Driven Approach for Electric Energy Equipment Using Wireless Sensing Technology in the Context of Carbon Neutrality

In this paper, we use wireless sensing technology to conduct an in-depth study and analysis of data-driven power energy equipment in the context of carbon neutrality. For the high-order uncertainty of renewable energy power generation and the nonlinearity of the tidal equation, a set of orthogonal bases under arbitrary probability space can be constructed by itself using the high-order information of renewable energy power generation statistics, and then, polynomials fit the state variables such as voltage in the tidal equation using the orthogonal bases and calculate the fitting parameters using the stochastic Gallatin integration method. Based on the analysis of the online monitoring project of the main power equipment in the substation, a substation power equipment condition monitoring system is designed to realize the real-time monitoring of the temperature status of power equipment, substation smoke and temperature, and humidity environment, and the feasibility and advanced of the system are verified by elaborating the analysis. Using wireless transmission to send real-time temperature information to the monitoring background, the infrared thermal imaging online monitoring system is designed around the front-end data acquisition system, transmission network, background data processing, and display module. Saving-investment equilibrium means that total investment equals total savings; government budget balance can be achieved through government savings or deficits. When both supply chain approaches enter into the same environmental competition, the added value that consumers value the product because of the carbon-neutral approach becomes smaller in the coefficient constraint of green effort investment cost due to the competition between the two supply chains, which in turn increases the green investment cost of the supply chain with the green effort carbon-neutral approach, indicating that in the case of competition, consumers, when faced with two products produced by two supply chains, are interested in the product with the carbon-neutral approach has less value-added, and some of the demand is shifted to the product not produced with the carbon-neutral approach.

Research on Radio Frequency Energy Acquisition Technology Used in Partial Discharge Sensors

To solve the energy supply problem of distributed sensor nodes for power equipment condition monitoring, the radio frequency energy acquisition technology scheme and low power consumption control method suitable for this type of sensor are studied. In the research of radio frequency energy technology, a high-gain radio frequency receiving unit is designed to convert a specific frequency high-frequency spatial electromagnetic wave into a AC small signal, and a radio frequency-voltage doubler rectifier unit is designed to convert the AC small signal into a DC signal and boost it, a supporting energy management unit is designed to control the energy interaction with the back-end sensor and provide a reliable and stable DC voltage to the partial discharge sensor. In terms of low-power control, the hardware adopts frequency-reduction detection and low-power devices, and the software proposes a work mode switching strategy, forming an ultra-low power design and application scheme for partial discharge sensors.

Classification of power quality disturbances using visual attention mechanism and feed-forward neural network

The power quality disturbances caused by large-scale grid connection of nonlinear loads and distributed generations seriously affect the safe and stable operation of precision computers and microprocessors in the power grid, and may cause serious security accidents and economic losses in some cases. Therefore, the accurate classification of power quality disturbances is of great significance for the power supply quality improvement, the power equipment condition monitoring, and the troubleshooting of power grid. For this reason, a novel method based on visual attention mechanism and feed-forward neural network is proposed to classify single and combined power quality disturbances caused by non-balanced, nonlinear loads and distributed generations in the power grid. In the first step of the proposed method, visual attention mechanism is utilized to extract the disturbance features of power quality disturbances, through performing disturbance region selection, multi-scale spatial rarity analysis, and disturbance feature fusion on the binary image converted from the original voltage signal successively. Then, four disturbance feature indexes are selected for the characterization of power quality disturbances. Finally, a classifier using feed-forward neural network is constructed to distinguish various single and combined power quality disturbances. The classification accuracy of the proposed method is compared with that of several existing methods for the classification of power quality disturbances from two types of datasources. The power quality disturbances from the simulation operating conditions include eight kinds of single and thirty-eight kinds of combined power quality disturbances. The power quality disturbances from the IEEE Work Group P1159.3 and P1159.2 Datasets include seven kinds of single and eleven kinds of combined power quality disturbances. Comparison results demonstrate that the proposed method can classify single and combined power quality disturbances more accurate than the compared classification methods, which verifies the effectiveness of the proposed method.

Research on Thermal Insulation Measures of Power Equipment Condition Monitoring Device in Cold Regions

Outdoor winter temperatures in other northern regions such as Heilongjiang Province are relatively low. As an important equipment of ensuring the safety of transmission lines, the stability of the condition monitoring devices is facing great challenges. In order to ensure that the condition monitoring device can operate reliably in winter, so as to avoid the influence of low temperature in winter on the reliability of the condition monitoring device, in this paper a device is designed that uses solar heat for heat preservation for power equipment condition monitoring device. The device uses an all-glass vacuum solar collector tube to convert solar energy into heat, and realizes instant feedback and control of temperature through a bimetallic temperature sensor. After testing, in winter, the internal temperature of the heat insulation box can be increased by about 30°C compared with the outside, which can effectively improve the winter operating environment of the device.

Monitoring Data Analysis Technology of Smart Grid Based on Cloud Computing

In the smart grid environment, the state data has the characteristics of wide area, panorama, mass and reliability. The traditional storage hardware uses disk arrays and the database management software uses the relational database system. Due to the poor system scalability, high cost and low reliability, it is difficult Adapt to requirements. Based on the above background, the purpose of this article is to study the research of cloud computing-based smart grid monitoring data analysis technology. Aiming at the problem that the power equipment condition monitoring data is getting larger and larger, and traditional storage methods cannot meet the condition monitoring big data storage problem, a cloud platform-based condition monitoring data storage system is designed. Using the distributed file system HDFS and Hbase databases to store status monitoring data, this paper designs a clustering algorithm based on the density cluster structure, DBCLustering, based on the shortcomings of traditional density clustering algorithms. This algorithm first builds the core reachability relationship of the data nodes The index structure CR-Tree is then used to extract a sorted linear table about the reachability relationship of the data. Finally, the clustering results are output according to the results of the linear table. In order to solve the problem of insufficient computing power of the stand-alone version of the algorithm, a clustering algorithm for condition monitoring data based on Spark-RDD-DBClustering algorithm is proposed. This algorithm realizes the parallel application of DBClustering algorithm under Spark platform, and improves the algorithm’s ability to process large-scale data.

Sensitivity Improvement of Acoustic Partial Discharge Detection Measurements through Wavelet Analysis

Condition monitoring of power equipment is a vital stepin extending the lifetime of existing equipment andreducing costs for utilities while minimizing the risk ofunscheduled outages. Partial discharge (PD) monitoringhas evolved as a reliable mean of determiningdeterioration in insulation systems. Acoustic emissiondetection techniques are usually utilized for PDdetection mainly in oil-filled transformers offering theadvantage of being immune to electrical noise and amethod to localize PDs. In this work it was attempted toimprove the sensitivity of acoustic measurementsthrough wavelet analysis and estimation of the thresholdvalue from actual measurements of the noise, whichproved to be more effective compared to otherestimation values. The analysis was performed onlaboratory measurements from a 36 kV condenserbushing known to exhibit PD activity acquired with alow cost PD acoustic sensor developed at KTH. As anext step the results have to be verified by onlinemeasurements, which can result in the addition of anCondition monitoring of power equipment is a vital step in extending the lifetime of existing equipment and reducing costs for utilities while minimizing the risk of unscheduled outages. Partial discharge (PD) monitoring has evolved as a reliable mean of determining deterioration in insulation systems. Acoustic emission detection techniques are usually utilized for PD detection mainly in oil-filled transformers offering the advantage of being immune to electrical noise and a method to localize PDs. In this work it was attempted to improve the sensitivity of acoustic measurements through wavelet analysis and estimation of the threshold value from actual measurements of the noise, which proved to be more effective compared to other estimation values. The analysis was performed on laboratory measurements from a 36 kV condenser bushing known to exhibit PD activity acquired with a low cost PD acoustic sensor developed at KTH. As a next step the results have to be verified by online measurements, which can result in the addition of an onboard signal-processing box for improved sensitivity.

Energy Estimation of Partial Discharge Pulse Signals Based on Noise Parameters

Partial discharge (PD) detection has been proved as an effective tool for insulation condition monitoring of power equipment. The energy of PD pulses is valuable for studying the characteristics of PD activities. This paper proposes a method for estimating the energy of PD pulses in the presence of white noise and narrowband noise. First, a maximum likelihood (ML) estimator of the pulse energy is derived from the probability distribution of the energy spectral coefficients. To implement the ML method, the sampled data are divided into signal frames and noise frames. The noise frames are then utilized for extracting noise parameters using the 3F-C method. Eventually, these noise parameters are applied to the signal frames to find the ML estimate of the pulse energy. To verify the effectiveness of the proposed method, both simulated data and measured data have been processed using the proposed method and the conventional wavelet packet (WP) denoising method. Compared with the WP denoising method, the proposed method has a higher accuracy and is less susceptible to the lengths of the sampling time windows. The advantage of the method is more significant in unfavorable conditions where the signal-to-noise ratio is low and the accurate lengths of the PD pulses are difficult to determine.

Framework of Random Matrix Theory for Power System Data Mining in a Non-Gaussian Environment

A novel empirical data analysis methodology based on the random matrix theory (RMT) and time series analysis is proposed for the power systems. Among the ongoing research studies of big data in the power system applications, there is a strong necessity for new mathematical tools that describe and analyze big data. This paper used RMT to model the empirical data which also treated as a time series. The proposed method extends traditional RMT for applications in a non-Gaussian distribution environment. Three case studies, i.e., power equipment condition monitoring, voltage stability analysis and low-frequency oscillation detection, illustrate the potential application value of our proposed method for multi-source heterogeneous data analysis, sensitive spot awareness and fast signal detection under an unknown noise pattern. The results showed that the empirical data from a power system modeled following RMT and in a time series have high sensitivity to dynamically characterized system states as well as observability and efficiency in system analysis compared with conventional equation-based methods.

Using IEC 61850 for Power Equipment Condition Monitoring Diagnosis System

Power equipment condition monitoring diagnosis (CMD) system includes 4 parts, master stations in control center master station system, integrated processing unit in subsation, monitoring device, and communication network. To achieve a seamless communication with the condition monitoring device and master station, IEC 61850 shall be used. Based on IEC 61850, the configure file, information model, information exchange model are standardized.

Research of Intelligent Substation Condition Monitoring Based on the Neural Network

The substation electric power equipment condition monitoring is the basis of intelligent substation. This paper analyzes the composition of the substation electric power equipment condition monitoring system and monitoring parameters, and with the transformer condition monitoring as an example, this paper proposes fault diagnosis methods of electric power equipment using artificial neural network(ANN).