Online Fault Monitoring of On-Load Tap-Changer Based on Voiceprint Detection

The continuous operation of On-Load Tap-Changers (OLTC) is essential for maintaining stable voltage levels in power transmission and distribution systems. Timely fault detection in OLTC is essential for preventing major failures and ensuring the reliability of the electrical grid. This research paper proposes an innovative approach that combines voiceprint detection using MATLAB analysis for online fault monitoring of OLTC. By leveraging advanced signal processing techniques and machine learning algorithms in MATLAB, the proposed method accurately detects faults in OLTC, providing real-time monitoring and proactive maintenance strategies.

Online Capacitor Voltage Transformer Measurement Error State Evaluation Method Based on In-Phase Relationship and Abnormal Point Detection

The assessment of the measurement error status of online Capacitor Voltage Transformers (CVT) within the power grid is of profound significance to the equitable trade of electric energy and the secure operation of the power grid. This paper advances an online CVT error state evaluation method, anchored in the in-phase relationship and outlier detection. Initially, this method leverages the in-phase relationship to obviate the influence of primary side fluctuations in the grid on assessment accuracy. Subsequently, Principal Component Analysis (PCA) is employed to meticulously disentangle the error change information inherent in the CVT from the measured values and to compute statistics that delineate the error state. Finally, the Local Outlier Factor (LOF) is deployed to discern outliers in the statistics, with thresholds serving to appraise the CVT error state. Experimental results incontrovertibly demonstrate the efficacy of this method, showcasing its prowess in effecting online tracking of CVT error changes and conducting error state assessments. The discernible enhancements in reliability, accuracy, and sensitivity are manifest, with the assessment accuracy reaching an exemplary 0.01%.

Research on Online Monitoring Methods on SF_(6) Circuit Breakers

To promote the accuracy and application of arcing time measurement for SF_6 circuit breaker in substation,five measurement methods are investigated by two cases experimentally. First,the test results of the five methods for a circuit breaker in different stages of wear and a circuit breaker with a component failure were presented. Then,the time error is analyzed by simulation.Finally,the advantage and disadvantage of these methods are discussed.

UTM:A trajectory privacy evaluating model for online health monitoring

A huge amount of sensitive personal data is being collected by various online health monitoring applications.Although the data is anonymous,the personal trajectories(e.g.,the chronological access records of small cells)could become the anchor of linkage attacks to re-identify the users.Focusing on trajectory privacy in online health monitoring,we propose the User Trajectory Model(UTM),a generic trajectory re-identification risk predicting model to reveal the underlying relationship between trajectory uniqueness and aggregated data(e.g.,number of individuals covered by each small cell),and using the parameter combination of aggregated data to further mathematically derive the statistical characteristics of uniqueness(i.e.,the expectation and the variance).Eventually,exhaustive simulations validate the effectiveness of the UTM in privacy risk evaluation,confirm our theoretical deductions and present counter-intuitive insights.

A Monitoring Method for Transmission Tower Foots Displacement Based on Wind-Induced Vibration Response

The displacement of transmission tower feet can seriously affect the safe operation of the tower,and the accuracy of structural health monitoring methods is limited at the present stage.The application of deep learning method provides new ideas for structural health monitoring of towers,but the current amount of tower vibration fault data is restricted to provide adequate training data for Deep Learning(DL).In this paper,we propose a DT-DL based tower foot displacement monitoring method,which firstly simulates the wind-induced vibration response data of the tower under each fault condition by finite element method.Then the vibration signal visualization and Data Transfer(DT)are used to add tower fault data samples to solve the problem of insufficient actual data quantity.Subsequently,the dynamic response test is carried out under different tower fault states,and the tower fault monitoring is carried out by the DL method.Finally,the proposed method is compared with the traditional online monitoring method,and it is found that this method can significantly improve the rate of convergence and recognition accuracy in the recognition process.The results show that the method can effectively identify the tower foot displacement state,which can greatly reduce the accidents that occurred due to the tower foot displacement.

Online Fault Detection Configuration on Equipment Side of a Variable-Air-Volume Air Handling Unit

With the development of the technology of the Internet of Things,more and more operational data can be collected from air conditioning systems.Unfortunately,the most of existing air conditioning controllers mainly provide controlling functions more than storing,processing or computing the measured data.This study develops an online fault detection configuration on the equipment side of air conditioning systems to realize these functions.Modbus communication is served to collect real-time operational data.The calculating programs are embedded to identify whether the measured signals exceed their limits or not,and to detect if sensor reading is frozen and other faults in relation to the operational performance are generated or not.The online fault detection configuration is tested on an actual variable-air-volume(VAV)air handling unit(AHU).The results show that the time ratio of fault detection exceeds 95.00%,which means that the configuration exhibits an acceptable fault detection effect.

In-situ analysis of laser-induced breakdown spectra for online monitoring of femtosecond laser machining of sapphire

Laser-induced breakdown spectroscopy(LIBS)is widely used for elemental analysis.However,its application for monitoring and analyzing a laser machining process by examining the changes in spectral information warrants further investigation.In this study,we investigate the effect of laser parameters on the spectra,variations in the time-resolved plasma emission spectra,and the relationship between the morphology of craters and plasma plume evolution during the femtosecond(fs)laser ablation of sapphire.The Boltzmann plot method and Stark’s broadening model are employed to estimate the temporal temperature and electron density of the plasma plume,revealing the process of plasma evolution.This study aims to demonstrate the feasibility of LIBS for online monitoring of laser processing through experimental data and theoretical explanations.

Non-contact and full-field online monitoring of curing temperature during the in-situ heating process based on deep learning

Online monitoring of the curing temperature field is essential to improving the quality and efficiency of the manufacturing process of composite parts.Traditional embedded sensor-based technologies have difficulty monitoring the full temperature field or have to introduce heterogeneous items that could have an undesired impact on the part.In this paper,a non-contact,full-field monitoring method based on deep learning that predicts the internal temperature field of composite parts in real time using surface temperature measurements of auxiliary materials is proposed.Using the proposed method,an average temperature monitoring accuracy of 97%is achieved in various heating patterns.In addition,this method also demonstrates satisfying feasibility when a stronger thermal barrier covers the part.This method was experimentally validated during the self-resistance electric heating process,in which the monitoring accuracy reached 93.1%.This method can potentially be applied to automated manufacturing and process control in the composites industry.

Development of a monitoring system for Huangjiu storage based on electrical conductivity

In order to quickly detect the rancidification of Huangjiu in pottery jars,this study developed a fast detection method based on the principle of electrical conductivity changes caused by microbial contamination.The change in total acid in Huangjiu was positively correlated with the increase of electrical conductivity.This method was applied to an online monitoring system for Huangjiu storage in stainless steel tanks.When the electrical conductivity exceeds the normal fluctuation range(mean+3 standard deviations)of previous data,the monitoring system recognizes microbial contamination.By optimizing the conductivity-temperature compensation coefficient and conductivity statistical method,the standard deviation of the method was reduced and the sensitivity of microbial pollution monitoring was improved.The ranges of conductivity and compensation coefficient of common types of Huangjiu were estimated.Interference in conductivity measurements due to environmental factors was minimised through the synchronous comparison of conductivity data for multiple tanks of Huangjiu.The standard deviation,which indicates the fluctuation range of the system,decreased from 143 to 2μS/cm.The monitoring system was then applied in Huangjiu storage tanks with capacities of 60 t and 300 t.Through the comparison of conductivity data change,the abnormal signals caused by microbial contamination during the storage of Huangjiu were found over time.Meanwhile,through offline detection of total acid in Huangjiu,the effectiveness of microbial contamination online detection was verified.

Smart component monitoring system increases the efficiency of photovoltaic plants

Due to the increasing expansion of renewable energy,especially the widespread installation of solar power plants worldwide,to better exploit and increase the efficiency and quality of power generation,we need to closely monitor the performance of important components of the plant.In this study,using an innovative smart monitoring system and electronic sensors,we monitored compo-nents such as power in photovoltaic(PV)arrays in real time,including the phenomenon of hot spots as an example of power loss in PV panels.Detection of hot spots is very difficult to deal with in a large power plant.The results also demonstrated smart monitoring that increased detection speed and increased the efficiency rate per supervisory technician by≤36% compared with the previous ef-ficiency rate,which was 10% per technical staff,and increased the quality of the operation of each solar power plant.In this paper,meteorological data were coordinated with research data to validate the research.Furthermore,to compare the results of using the smart monitoring method with the conventional observation method,a complete diagram of a 5-kW solar system in MATLAB■2018 was simulated and output diagrams were presented.Finally,to provide a comprehensive validation,our research results were com-pared with technical data obtained from a local 5-kW solar power plant located in Sari,Iran(36°33ʹ48″N,53°03ʹ36″E)with an average annual irradiation of 1490 kWh/m^(2).When the simulation results and research are analysed,it is clear that the smart and real-time monitoring approach brings various benefits to solar power plants.

Online Monitoring Method for Insulator Self-explosion Based on Edge Computing and Deep Learning

Aiming at the problems of traditional centralized cloud computing which occupies large computing resources and creates high latency,this paper proposes a fault detection scheme for insulator self-explosion based on edge computing and DL(deep learning).In order to solve the high amount of computation brought by the deep neural network and meet the limited computing resources at the edge,a lightweight SSD(Single Shot MultiBox Detector)target recognition network is designed at the edge,which adopts the MobileNets network to replace VGG16 network in the original model to reduce redundant computing.In the cloud,three detection algorithms(Faster-RCNN,Retinanet,YOLOv3)with obvious differences in detection performance are selected to obtain the coordinates and confidence of the insulator self-explosion area,and then the self-explosion fault detection of the overhead transmission line is realized by a novel multimodel fusion algorithm.The experimental results show that the proposed scheme can effectively reduce the amount of uploaded data,and the average recognition accuracy of the cloud is 95.75%.In addition,it only increases the power consumption of edge devices by about 25.6W/h in their working state.Compared with the existing online monitoring technology of insulator selfexplosion at home and abroad,the proposed scheme has the advantages of low transmission delay,low communication cost and high diagnostic accuracy,which provides a new idea for online monitoring research of power internet of things equipment.

Online Monitoring Method Based on Locus-analysis for High-voltage Cable Faults

Online diagnosis methods for high-voltage(HV)cable faults have been extensively studied.Power cable fault monitoring is not accurate,and the degree of data fusion analysis of current methods is insufficient.To address these problems,an online monitoring method based on the locus-analysis for HV cable faults is developed.By simultaneously measuring two circulating currents in a coaxial cable,a two-dimensional locus diagram is drawn.The fault criteria and database are established to detect the fault by analyzing the changes of the locus characteristic parameters.A cross-connected grounding simulation model of a high-voltage cable is developed,and several faults at different locations are simulated.Fault identification is established based on the changes in the long axis,short axis,eccentricity,and tilt angle of the track.The simulation and field experiments show that this method can provide an increased amount of fault-monitoring reference information,which can improve the monitoring accuracy and provide a new approach to cable fault monitoring.

Instantly Investigating the Adsorption of Polymeric Corrosion Inhibitors on Magnesium Alloys by Surface Analysis under Ambient Conditions

Surface engineering of magnesium alloys requires adequate strategies, processes and materials permitting corrosion protection. Liquid formulations containing corrosion inhibitors often are to be optimized according to the demands of the respective substrate and following the service conditions during its application. As an interdisciplinary approach, a combination of several techniques for instantly monitoring or elaborately analyzing the surface state of magnesium was accomplished in order to characterize the performance of new adsorbing sustainable amphiphilic polymers which recently were developed to facilitate a multi-metal corrosion protection approach. The application of established techniques like Contact Angle measurements and X-ray Photoelectron Spectroscopy investigations was supplemented by introducing related and yet faster online-capable and larger-scale techniques like Aerosol Wetting Test and Optically Stimulated Electron Emission. Moreover, an inexpensive setup was configured for scaling the inset and the extent of degradation processes which occur at local electrochemical circuits and lead to hydrogen bubble formation. Using these analytical tools, changes of the surface state of emeried AM50 samples were investigated. Even in contact with water, being a moderate corrosive medium, the online techniques facilitated detecting surface degradation of the unprotected magnesium alloy within some seconds. In contrast, following contact with a 1 weight% formulation of a polymeric corrosion inhibitor, surface monitoring indicated a delay of the onset of degradation processes by approximately two orders of magnitude in time. Mainly based on the spectroscopic investigations, the corrosion inhibiting effects of the investigated polymer are attributed to the adsorption of a primary polymer layer with a thickness of a few nanometers which occurs within some seconds. Immersion of magnesium for several hours brings up a protective film with around ten nanometers thickness.

Electromechanical coupling properties of a self-powered vibration sensing device for near-surface observation tower monitoring

The wind-induced vibration of a remote sensing tower is the key factor affecting the stability of image sensing and structural reliability. Monitoring the vibration of a long-time unattended tower is critical to its proper operation. Currently, most monitoring devices are supplied with wired power or battery, significantly limiting their practical applications in remote areas. In this paper,a self-powered vibration sensing device based on hybrid electromechanical conversion mechanisms is proposed. The device depends on a cylindrical magnetic levitation structure sensitive to ambient vibration for transferring mechanical energy and is taken as a dual-functional heterogeneous integrated system comprising electromagnetic, piezoelectric, and triboelectric generators. When the device vibrates under environmental force driving, the suspension magnet reciprocates vertically and generates induced electromagnetic energy, which is used to power the device. Moreover, the triboelectric and piezoelectric voltages,respectively originating from magnet impact on two separation friction materials and magnetic field repulsion-induced strain deformation of a piezoelectric sheet, are used as the synergistic sensing signals. To improve the output energy, a set of dualsegmented annular coils is designed in an electromagnetic generator, which greatly avoids the obstructive effect of the suspended magnet on the magnetic flux change at its end. Compared with a whole isochoric coil, it increases the output voltage by 78.3%.For the triboelectric sensing module, a silicone film with a large specific surface area is fabricated via 3D modification, which improves the output voltage by 29.4%. Furthermore, a pair of piezoelectric sensing modules is set to improve the accuracy of comparative sensing data. The experimental measurement shows that the device maintains a high sensitivity of 6.711 V(m s;);and excellent linearity of 0.991 in the range of 0–14 m s;. This work provides a practical strategy for the vibration monitoring of remote sensing towers and exhibits attractive potential in early warning and data analysis.

Deep-learning-assisted online surface roughness monitoring in ultraprecision fly cutting

Surface roughness is one of the most critical attributes of machined components,especially those used in high-performance systems.Online surface roughness monitoring offers advancements comparable to post-process inspection methods,reducing inspection time and costs and concurrently reducing the likelihood of defects.Currently,online monitoring approaches for surface roughness are constrained by several limitations,including the reliance on handcrafted feature extraction,which necessitates the involvement of human experts and entails time-consuming processes.Moreover,the prediction models trained under one set of cutting conditions exhibit poor performance when applied to different experimental settings.To address these challenges,this work presents a novel deep-learning-assisted online surface roughness monitoring method for ultraprecision fly cutting of copper workpieces under different cutting conditions.Tooltip acceleration signals were acquired during each cutting experiment to develop two datasets,and no handcrafted features were extracted.Five deep learning models were developed and evaluated using standard performance metrics.A convolutional neural network stacked on a long short-term memory network outperformed all other network models,yielding exceptional results,including a mean absolute percentage error as low as 1.51%and an R2value of 96.6%.Furthermore,the robustness of the proposed model was assessed via a validation cohort analysis using experimental data obtained using cutting parameters different from those previously employed.The performance of the model remained consistent and commendable under varied conditions,asserting its applicability in real-world scenarios.