Monitoring Of Operating Conditions Research Articles

An Estimation Method of High-Order LC Circuits in Power Electronic Converters

This paper proposes a parameter estimation method for high-order LC circuits conducted during the power-OFF of power electronic converters. It is based on a battery-powered compact signal injection unit and a circuit modeling method to estimate the LC circuit parameters. The proposed method aims for LC component condition monitoring in applications with routine-based maintenance or frequent ON/OFF operation (e.g., wind power, photovoltaic, traction systems). Compared to LCR meters and impedance analyzers, the proposed method has a significantly lower implementation cost and does not require disassembly of components. Moreover, it is a converter-level method in which the signal injection unit only needs to connect the converter inputs or outputs. Compared to power-ON LC parameter estimation methods, the proposed one is non-invasive without adding any hardware or software algorithm that may introduce new risks to converter operation. This method can handle the converters with high-order LC structures (e.g., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2^{nd}$</tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$4^{th}$</tex-math></inline-formula> -order) in converter power-OFF state, which has not been achieved by existing power-OFF solutions. Therefore, the proposed method is promising for the specific applications of interest mentioned above. The parameter estimation algorithm and signal injection unit design are presented. The experimental results indicate that the estimation errors of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2^{nd}$</tex-math></inline-formula> -order equivalent circuit, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$3^{rd}$</tex-math></inline-formula> -order equivalent circuit, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$4^{th}$</tex-math></inline-formula> -order equivalent circuit are lower than 1.6%, 2.5%, and 3%, respectively. The estimated LC parameters can be further used as inputs for operation optimization or condition monitoring of the converters.

Empirical analysis of sensor type importance for data preparation of real-time operational status monitoring in fused deposition modeling 3D printers

Empirical analysis of sensor type importance for data preparation of real-time operational status monitoring in fused deposition modeling 3D printers

Real-Time Digital Mapped Method for Sensorless Multitimescale Operation Condition Monitoring of Power Electronics Systems

Operation condition monitoring of power electronics systems can improve analysis visibility for device administrators and researchers. However, the current sensor-based monitoring methods have limitations in accurately monitoring variables at multi-timescales. This article presents a real-time digital mapped (RTDM) method for the sensorless multi-timescale operation condition monitoring in power electronics systems using FPGA simulators. The mathematic models of power converters are deployed in the simulator considering multi-timescale characteristics. Both the digital simulator and the physical power converter are controlled by the same control signals, allowing the simulator to map the accurate operation conditions of the power converters without additional physical sensors. The RTDM method can monitor variables on the switching period timescale to the system transient timescale due to the consideration for the ideal switch modeling of the semiconductor switch. The effectiveness of the proposed method is demonstrated using a 30kW DAB converter as an example, through the integration of an experimental prototype and a simulator to create an RTDM platform. This method offers an easy and sensorless way to monitor multi-timescale variables in power electronics systems, providing a more comprehensive and visual analysis of their operation.

Data sonification of film capacitors

AbstractFilm capacitors are playing an increasingly important role in power‐related fields, driven by the continuous development of dielectric materials and practical needs. Long‐term accumulation has also led to an increasing wealth of data related to film capacitors. Sonification opens up a new way for people to make good use of data from film capacitors. A framework for sonifying film capacitors data based on TwoTone is presented. Based on the analysis and discussion, it is clear that the sonification results can easily represent the monotonic variation pattern of film capacitors data. What's more, the sonification results increase the possibility that people pay attention to the changing trend of film capacitors data when there is no significant difference in the visual perception of the data. In addition to providing a new way of music generation of electrical equipment, the method proposed is expected to contribute to theory reference in typical scenarios, such as factory calibration of film capacitors, monitoring of film capacitor operation status, and presentation of statistical data of film capacitors' dielectric materials, which will help us to better understand the distribution characteristics of polymer films.

Monitoring method for power equipment operation condition based on data warehouse

in the process of monitoring the operation state of power equipment, it is easy to be affected by the external environment, resulting in a large amount of data of the operation state of power equipment and the monitoring effect. A monitoring method of power equipment operation condition based on data warehouse is proposed. By analyzing the constraints of power equipment operation condition monitoring, a data warehouse is established; The transition probability and stability probability are determined by Markov chain model, and the characteristic values of operation state level variables are obtained by calculating the comprehensive correlation value and sensitivity; The entropy weight coefficient method calculates the weight of each performance index of the monitoring object, constructs the operation state monitoring model of power equipment, and realizes the monitoring of the operation state of power equipment. The experimental results show that the improved method can effectively improve the monitoring efficiency and shorten the monitoring time.

Online Aware Synapse Weighted Autoencoder for Recovering Random Missing Data in Wastewater Treatment Process

Missing values in wastewater treatment process (WWTP) data hinder the monitoring and prediction of operational status. Therefore, various online imputation methods have been proposed to recover missing values from streaming data collected in WWTP in real time. However, existing methods tend to ignore previous learned knowledge. In this paper, an online aware synapse weighted autoencoder imputation method (OASI) is proposed to impute random missing values. First, an online stacked autoencoder (OSAE) framework is constructed to capture the nonlinear structure of the recently collected data. The framework decreases the computational and storage consumption of the model training. Second, an aware synapses weighted parameter regularization strategy is presented to guide the update of model parameters and alleviate the forgetting of historical information in an online continual setup. In this way, the learned features offer a more comprehensive representation of the overall information and help enhance imputation performance. Third, two real WWTP datasets with strong non-stationarity, high-noise level and high-dimensionality are used to validate the performance of the proposed OASI. Experimental results show that the proposed OASI achieves superior performances over the existing methods even in the presence of random missing values with different missing ratios, and only costs a short running time.

An Incorporated Power and Data Synchronous Transmission Method for SRG Integrated Wind Power Generation Systems

Reliable communication places important roles in renewable generation for operating status monitoring, fault alarming, cooperative control and so on. For existing power and data synchronous transmission strategies applied to switched reluctance generator (SRG) systems, the communication rate is limited by very low power switching frequencies. Hence, in this paper, a new PDST strategy is proposed and implemented in an SRG integrated wind generation system by incorporating power and data composite modulation method with signal injection method. For lower signal band, the data is modulated together with power by regulating the lower power switch of each phase in a fixed interval with given frequency, and for the higher signal band, the data in form of voltage ripple is injected into the bus by a coupling circuit. Therefore, specific voltage ripples with different given frequencies can be embedded into the power line for carrying signals, and the frequency band of signal bus can be well exploited. On signal receiver end, the voltage ripples are sampled and analyzed by fast Fourier transform method for demodulating the transmitted data. The feasibility of the proposed incorporated data transmission method is verified by experimental testing.

Research and application of safety monitoring technology of distribution automation based on SOM neural network

Abstract For automatic situation monitoring in power distribution safety monitoring, data features are mainly extracted by single hidden layer neural network, which makes the standard error of monitoring results larger. Therefore, the research and application of power distribution automation safety monitoring technology based on SOM neural network are proposed. Preprocess the power grid, collect distribution operation data, set multi-dimensional monitoring nodes according to the collected data, build a distribution operation status monitoring model and analyze the data fusion technology of distribution automation safety monitoring according to the model. On this basis, the distribution automation safety monitoring system is defined, the output node correction weight and the reverse output node correction weight are calculated, the SOM neural network identification model is constructed and the research and application of the distribution automation safety monitoring technology are completed under the action of the gravitational function between individuals within the target time. The experimental results show that the change curve of the number of vulnerabilities and the actual number of false positives is consistent, the number of vulnerabilities is small and the monitoring results are more accurate; The state of the safety monitoring equipment of distribution automation is normal. After applying the method in this paper, the change curve is consistent with the actual value, and the delay time is less than 15 ms.

Spatio-temporal information analytics based performance-driven industrial process monitoring framework with cloud–edge-device collaboration

Smart manufacturing in the context of Industry 4.0 presents new characteristics, such as large-scale, complex sub-processes cooperation, and integrated information management, which makes comprehensive performance-driven process monitoring difficult to be achieved in practice. Cloud–edge-device collaboration brings new perspectives for modern industries. This paper presents a performance-driven process monitoring framework in cloud–edge-device collaborative architecture, including a spatio-temporal information analytics based method and its application to a hot strip mill process (HSMP). Firstly, an integrated performance-driven process monitoring framework is proposed, where different monitoring tasks are deployed in the device, edge, and cloud layers, respectively. Secondly, a new performance-related spatio-temporal information analytics based fault detection and localization method is developed. In detail, temporal kernel canonical correlation analysis (CCA) is employed to extract the canonical correlation features with nonlinearities in the temporal domains, and spatial CCA is designed to form the spatial features. Based on the spatio-temporal features, the equipment operation condition monitoring model is developed in the device layer for real-time fault localization, and the process monitoring model is deployed in the edge for fault detection. Furthermore, to give a high-level evaluation of the operating status of the process, a qualitative trend analysis based operation risks evaluation model is deployed in the cloud. Different from the traditional architectures, the data processing capability of each layer is fully utilized in the proposed one, and the function is relevant to the enterprise management system. It provides a topological basis with clear priorities for subsequent process regulation and optimization. Finally, an actual HSMP is introduced to testify the performance of the framework. From the results, the fault detection rates are improved in comparison with traditional methods, and the fault localization results are consistent with the actual situations.

Analysis of the Slanted-Edge Measurement Method for the Modulation Transfer Function of Remote Sensing Cameras

The modulation transfer function (MTF) serves as a crucial technical index for assessing the imaging quality of remote sensing cameras, which is integral throughout their entire operational cycle. Currently, the MTF evaluation of remote sensing cameras primarily relies on the slanted-edge method. The factors influencing the slanted-edge method’s effectiveness are broadly classified into two categories: algorithmic factors and image factors. This paper innovatively comprehensively analyzes the influencing factors of the slanted-edge method and proposes an improved slanted-edge method to calculate the MTF testing method of remote sensing cameras, which is applied to the MTF testing of remote sensing cameras. Since the traditional algorithm can only be applied in the small angle situation, this paper proposes a new method of slanted-edge method test calculation based on the optimal oversampling rate (OSR) adaptive model of the slanted edge and uses simulation experiments to verify the reliability of the algorithm model through the deviation of the slanted-edge angle calculation and MTF measurement, and the results show that the algorithm improves the accuracy of the MTF measurement compared with the ISO-cos and OMINI-sine methods. Then, the effects of the slanted-edge angle, image region of interest (ROI), as well as image contrast and signal-to-noise ratio (SNR) on the accuracy of the MTF calculation by the slanted-edge method were quantitatively analyzed as the constraints of the slanted-edge method test. Based on the laboratory target experiment, the algorithm flow and various influencing factors obtained in the simulation stage are verified, and the experimental results are more consistent with the various test results obtained in the simulation stage. Consequently, the slanted-edge method introduced in this paper is applicable for future remote sensing camera MTF testing. This approach offers a valuable reference for on-orbit focusing, satellite operational condition monitoring, lifespan estimation, and image restoration.

Research on Operational Status Monitoring Technology of the Secondary Circuit of Intelligent Substation Based on Virtual Circuit Visualization

With the promotion and construction of intelligent substations, how to reliably monitor the operation status of the intelligent components and related circuits of the secondary circuit equipment has become a difficult problem to which needs to be paid attention in the optimization operation and relay protection of the power system. This article proposes an intelligent substation secondary circuit operation status monitoring technology, which visualizes the design of SCD files. At the same time, the entropy weight method and grey correlation analysis method are introduced into the monitoring system to improve the work efficiency of on-site operation and maintenance personnel and promote the development of substation intelligence.

스마트축사 활용 가상센서 기술 설계 및 구현

Innovation and change are occurring rapidly in the agriculture and livestock industry, and new technologies such as smart barns are being introduced, and data that can be used to control equipment is being collected by utilizing various sensors. However, there are various challenges in the operation of barns, and virtual sensor technology is needed to solve these challenges. In this paper, we define various data items and sensor data types used in livestock farms, study cases that utilize virtual sensors in other fields, and implement and design a virtual sensor system for the final smart livestock farm. MBE and EVRMSE were used to evaluate the finalized system and analyze performance indicators. As a result of collecting and managing data using virtual sensors, there was no obvious difference in data values from physical sensors, showing satisfactory results. By utilizing the virtual sensor system in smart livestock farms, innovation and efficiency improvement can be expected in various areas such as livestock operation and livestock health status monitoring. This paper proposes an innovative method of data collection and management by utilizing virtual sensor technology in the field of smart livestock, and has obtained important results in verifying its performance. As a future research task, we would like to explore the connection of digital livestock using virtual sensors.

Operational status monitoring of smart grid and power communication network coupling and collaboration based on multi-head attention mechanism

Smart grid is an organic combination of traditional power grid and advanced digital communication technology, and power communication network plays a vital role in ensuring its steady operation. However, as the coupling correlation between the smart grid and the power communication network increases, the propagation of coupled network failures may constitute a great threat to the safe and stable operation of the power system. To address this phenomenon, based on the algorithm of the multi-head attention mechanism (MHA) and convolutional neural network (CNN), this paper proposes a framework that integrates the multi-head attention mechanism with the convolutional neural network (MHA-CNN) for the monitoring of the coupling network’s operation status. In the proposed algorithm, Hilbert–Huang transform (HHT) is used to extract the intrinsic features of the operational data, and MHA employs multi-subspace analysis and extracts the key feature information of the fault data to maximize the fault information extraction and help the system to discover the fault as soon as possible. Simulation results demonstrate that the proposed algorithm can effectively realize a better detection speed and higher detection probability compared with CNN and ResNet-50, and maintain certain robustness under poor transmission conditions, ensuring the steady operation of the coupled network.

Operation and maintenance strategy of radio transmission equipment based on big data

AbstractIn recent years, the operation and maintenance of radio transmission equipment have faced challenges such as equipment aging, high maintenance costs, long response time, and technological updates. It is necessary to adapt to new technologies and improve maintenance efficiency. The operation and maintenance strategy of radio transmitting equipment based on big data (BD) aims to optimize the reliability and operating efficiency of equipment through data analysis and intelligent algorithms, and reduce failure risk and maintenance costs. By analyzing the operation data and maintenance records of a large number of radio transmission equipment, it revealed the patterns and trends of equipment failures, in order to timely identify and predict possible faults, and take corresponding maintenance measures. This article adopted methods such as data collection and preprocessing, fault prediction and diagnosis, and maintenance optimization to achieve accurate monitoring of equipment operation status, fault prediction and diagnosis, optimized maintenance, and decision support. This can improve the operational efficiency and reliability of radio transmission equipment, which is used for fault diagnosis of radio transmission equipment through SVM (Support Vector Machine) algorithms. By training SVM classifiers, this article associated different types of faults with their feature data, thereby quickly and accurately determining the type of equipment fault based on real‐time collected device data, providing a basis for maintenance decision‐making. The experimental results show that the failure frequency of the radio transmitting equipment in the control group was between 7% and 11%, while the failure frequency of the radio transmitting equipment in the experimental group was between 3% and 7%. Experiments show that BD technology can effectively reduce the failure frequency of radio transmitting equipment. The empirical results indicate that this strategy can improve the reliability of equipment and reduce operation and maintenance costs, which has important guiding significance for the operation and maintenance management of radio equipment. This would bring about more efficient and reliable operation and maintenance management of radio equipment, making important contributions to the smooth progress of broadcasting business and the improvement of user experience.

Vehicle Operation Status Monitoring Based on Distributed Acoustic Sensor

To develop implementation research on distributed optical fiber sensing technology, field tests were conducted on municipal roads and railways using a distributed acoustic sensor (DAS). Data were collected by the DAS during a field test for a long time period (more than 20 min), and we conducted short-term (&lt;10 s) and long-term (≥10 s) analyses on these data separately. In the short-term data analysis, the vehicle type, vehicle length, and working status of the vehicle engine or the compressor were identified. In the long-term data analysis, the traffic flow was monitored, and the running distance, acceleration, speed, and braking distance of the vehicle were obtained. The characteristics of the vehicle operation data obtained in these field tests are important in developing the data processing method of DASs, which will help to promote the implementation of DASs.

Energy consumption analysis of power grid distribution transformers based on an improved genetic algorithm

With the promotion of energy transformation, the utilization ratio of electrical power is progressively rising. Since electrical power is challenging to store, real-time production and consumption become imperative, imposing significant demands on the dependability and operational efficiency of electrical power apparatus. Suppose the load distribution among multiple transformers within a transformer network exhibits inequality. In such instances, it will amplify the total energy consumption during the voltage conversion process, and local, long-term high-load transformer networks become more susceptible to failures. In this article, we scrutinize the matter of transformer energy utilization in the context of electricity transmission within grid systems. We propose a methodology grounded on genetic algorithms to optimize transformer energy usage by dynamically redistributing loads among diverse transformers based on their operational status monitoring. In our experimentation, we employed three distinct approaches to enhance energy efficiency. The experimental findings evince that this approach facilitates swifter attainment of the optimal power level and diminishes the overall energy consumption during transformer operation. Moreover, it exhibits a heightened responsiveness to fluctuations in power demand from the electrical grid. Experimental results manifest that this technique can truncate monitoring time by 27% and curtail the overall energy consumption of the distribution transformer network by 11.81%. Lastly, we deliberate upon the potential applications of genetic algorithms in the realm of power equipment management and energy optimization issues.

Research on the Pre-Control of Side Scrap Blockage Failure during Trimming with the Side Trimmer

In the coupled pickling line and tandem cold mill (PL-TCM), the hot-rolled strip needs to be trimmed on both sides of the strip before the tandem cold rolling process to ensure stable operation of the rolling process. The equipment for trimming the strip is the side trimmer, and during trimming a failure of the side scrap blockage is often caused by the variation in the width of the hot-rolled strip and the deviation during the actual operation. For this problem, this paper established a finite element model of strip trimming by side trimmer, analyzed the influence of side scrap width, structure parameters, and strip specifications on the equivalent plastic strain at the trimmed position of the strip, and obtained the influence law of each factor during the trimming process. On this basis, by collecting the historical data of side scrap blockage failure of the side trimmer, the threshold value of the side scrap width setting is obtained. Combining the width data of the hot-rolled strip and the actual operation status monitoring of the strip in the inlet section of the PL-TCM, the side scrap blockage risk identification function and the speed reduction strategy of the side trimmer are designed to form the side scrap blockage pre-control model together. After applying the side scrap blockage pre-control model to a PL-TCM, a 40.5% reduction in side scrap blockage failures was achieved compared with the same length of time before the application, which achieved satisfactory results.

SAPM: Self-Adaptive Parallel Manipulator with Pose and Force Adjustment for Robotic Ultrasonography.

Robotic ultrasonography potentially acts as an essential aid to medical diagnosis. To overcome the limitations in robotic ultrasonography, in this paper, we proposed a novel self-adaptive parallel manipulator (SAPM) that can automatically adjust the ultrasound (US) probe pose to adapt to various contours of scanned areas, provide approximate constant operating forces/torques, achieve mechanical measurement, and cushion undesired produced forces. A novel parallel adjustment mechanism is proposed to attain automatic pose adjustment with 3 degrees of freedom (DOFs). This mechanism enables the US probe to adapt to different scanned areas and to perform the scanning with approximate constant forces and torques. Besides, we present a mechanical measurement and safety protection method that can be integrated into the SAPM and used as operation status monitoring and early warning during scanning procedures by capturing operating forces and torques. Experiments were carried out to calibrate the measurement and buffer units and evaluate the performance of the SAPM. Experimental results show the ability of the SAPM to provide 3-DoFs motion and operating force/torque measurement and automatically adjust the US probe pose to capture US images of equally good quality compared to a manual sonographer scan. Moreover, it has characteristics similar to soft robots that could significantly improve operation safety, and could be extended to some other engineering or medical applications.

Non-Contact Monitoring of Operating Conditions for Solar Cells in a Photovoltaic Module Using a Surface Potential Meter for Detecting the Risk of Fire

Fires in photovoltaic modules are caused by hot spots, which are typically monitored by thermal images. This method helps visualize the hot spot, but it is affected by the environment (solar irradiance, wind, ambient temperature) and is not reproducible. Assessing the heat dissipation of the hot cell can be used for alternative assessment of the fire risk. This method was validated by comparing the value measured by the surface potential meter and the module potential measured directly by adding a bypass measurement circuit. The substantial reverse-bias voltage caused by mismatching or partial shading (depending on the operating conditions) leads to local heat consumption of the partially shaded solar cells and potentially causes fire. The fire risk can be assessed in the worst-case conditions (ex. 1380 W/m2 solar irradiance) by non-contact measurement of the reverse-bias voltage and calculating the heat dissipation and temperature rise. This work suggested that −13 V is the criterion and was close to the known value of reverse voltage for Si cells. The current technology inspects solar cells before assembly to the module, and there is no way of inspecting in the product test or detecting after degradation that can be covered by the proposed method in this work.

Retracted: Operational Status Monitoring and Fault Diagnosis System of Transformer Equipment

Retracted: Operational Status Monitoring and Fault Diagnosis System of Transformer Equipment