On-line Monitoring Equipment Research Articles

Optimal Configuration of Single-Layer Domino-Coils With Uniform Spacing for Online Monitoring Equipment in Smart Grid

Optimal Configuration of Single-Layer Domino-Coils With Uniform Spacing for Online Monitoring Equipment in Smart Grid

Space Electromagnetic Optimization of Three-Coil WPT for Online Monitoring Equipment of High-Voltage Transmission Lines

Space Electromagnetic Optimization of Three-Coil WPT for Online Monitoring Equipment of High-Voltage Transmission Lines

Research on characteristics and influencing factors of road dust emission in a southern city in China.

One of the primary causes of urban atmospheric particulate matter, which is harmful to human health in addition to affecting air quality and atmospheric visibility, is road dust. This study used online monitoring equipment to examine the characteristics of road dust emissions, the effects of temperature, humidity, and wind speed on road dust, as well as the correlation between road and high-space particulate matter concentrations. A section of a real road in Jinhua City, South China, was chosen for the study. The findings demonstrate that the concentration of road dust particles has a very clear bimodal single-valley distribution throughout the day, peaking between 8:00 and 11:00 and 19:00 and 21:00 and troughing between 14:00 and 16:00. Throughout the year, there is a noticeable seasonal change in the concentration of road dust particles, with the highest concentration in the winter and the lowest in the summer. Simultaneously, it has been discovered that temperature and wind speed have the most effects on particle concentration. The concentration of road dust particles reduces with increasing temperature and wind speed. The particle concentrations of road particles and those from urban environmental monitoring stations have a strong correlation, although the trend in the former is not entirely consistent, and the changes in the former occur approximately 1 h after the changes in the latter.

Research and design of multi‐stage multi‐load wireless power supply system for high voltage online monitoring equipment

AbstractMany online monitoring devices in substations are placed at high potential differences from the ground, and it is not possible to supply power to these devices directly from the ground through wires. Placing magnetically coupled structures inside the substation pillar insulators for inductive power transfer is considered a feasible solution, and at this stage of research, systems that add multi‐stage repeating coil structures are mostly used to achieve stable power supply for individual online monitoring devices. However, the monitoring point generally requires multiple devices to monitor together, and the power requirements of different devices are different, so adding multiple repeating coils at the same time will increase the complexity of the system design, which is not conducive to engineering applications. Therefore, this paper designs and optimizes a multi‐stage multi‐load wireless power supply system magnetic coupling structure, using a three‐dimensional hollow solenoid combined with a ferrite core as the system repeating coil structure and an embedded magnetic coupling structure as the receiver end structure to reduce the number of system repeating coils while supplying power to devices with different power requirements. A prototype with a transmission distance of 1.02 m was built in the experiment, and the experimental results showed that the highest transmission efficiency of the system was 44.3% and 39.2% under single load and double load conditions, respectively. As the load resistance increases, the system can achieve constant voltage output under both single‐load and dual‐load operating conditions. Adjusting the number of turns of the receiving coil can change the output characteristics of the system and achieve constant voltage output of different voltage levels or constant current output of different current levels, which can meet the power supply needs of different loads.

Gas-Sensing Performance of Metal Oxide Heterojunction Materials for SF6 Decomposition Gases: A DFT Study.

The online monitoring of GIS equipment can be realized through detecting SF6 decomposition gasses. Metal oxide heterojunctions are widely used as gas-sensing materials. In this study, the structural and electrical properties of In2O3-ZnO and TiO2-ZnO heterojunctions were analyzed based on density functional theory calculations. After heterojunction structural optimization, the electrical conductivity of these two heterojunctions was enhanced compared to each intrinsic model, and the electrical conductivity is ranked as follows: In2O3-ZnO heterojunction > TiO2-ZnO heterojunction. The gas-sensing response of these two heterojunctions to four SF6 decomposition gasses, H2S, SO2, SOF2, and SO2F2, was investigated. For gas adsorption systems, the adsorption energy, charge transfer, density of states, charge difference density, and frontier molecular orbitals were calculated to analyze the adsorption and gas-sensing performance. For gas adsorption on the In2O3-ZnO heterojunction surface, the induced conductivity changes are in the following order: H2S > SO2F2 > SOF2 > SO2. For gas adsorption on the TiO2-ZnO heterojunction surface, H2S and SOF2 increase conductivity, and SO2 and SO2F2 decrease conductivity.

Optimal deployment of the online monitoring equipment at the edges of substations considering spatial constraint

Optimal deployment of the online monitoring equipment at the edges of substations considering spatial constraint

BiLSTM-FCN based vibration signal diagnosis of smart grid cables

Abstract Cable faults threaten the safe and stable operation of smart grids, and vibration signal diagnosis research on cables based on artificial intelligence technology can effectively enhance the reliability of smart grids. In order to improve the speed and accuracy of cable defect identification, this paper proposes a partial discharge identification method for cables based on a fully convolutional bidirectional long short-term memory neural network (BiLSTM-FCN). The time-domain characteristics of different working conditions under industrial frequency AC voltage are collected by building a vibration signal-based partial discharge test platform for cables. The Overlap strategy enhances the data set. The BiLSTM layer is introduced to process the one-dimensional time domain waveform signal, and combined with the local detail features extracted by the FCN layer, the data features are more abundant. Thus, a more accurate diagnosis of localized discharge in high voltage cables under different operating conditions. The results show that the diagnostic accuracy based on the BiLSTM-FCN model reaches 92.2%, which is 1.2% and 1.4% higher compared to the FCN model and the BiLSTM model. BiLSTM-FCN model possesses a better recognition effect and faster recognition speed in the identification of partial discharge defects type, which can effectively achieve the automatic detection of abnormal fault nodes of smart grid cables. It is significant for realizing online dynamic evaluation of small portable online monitoring equipment and provides a reference for future related research.

Research on laser online monitoring equipment for high-temperature corrosive gas in coal-fired boilers

Research on laser online monitoring equipment for high-temperature corrosive gas in coal-fired boilers

Power plant turbine power trend prediction based on continuous prediction and online oil monitoring data of deep learning

Power output is an important property of steam turbines and more accurate trend prediction is essential for understanding the operation of power plants and anomaly detection in equipment. Equipment power is strongly influenced by human factors and achieving accurate trend prediction is often impossible. In the current research, a continuous prediction model was developed on the basis of deep learning to establish the relationships among oil online monitoring parameters and equipment power. The model used long short term memory (LSTM) method to develop a trend prediction model for oil wear state parameters. Trend prediction results were applied as a test set to establish a deep belief networks (DBN) prediction model for predicting device power. In modeling process, continuous prediction model was optimized via feature selection method on the basis of ridge regression with L2 regularization, recursive feature elimination (RFE) and differential evolution (DE) algorithms for the elimination of subjective factor to decrease cumulative error of forecasting. Comparative experimental results showed that LSTM-RFE-DE-DBN continuous prediction model outperformed LSTM-RFE-DE-BPNN and LSTM-DBN. The developed model realized continuous prediction and applied lubricating oil wear status with objective factors to perform the power prediction of power plant turbines with subjective factors.

Self-Powered Wireless Temperature and Vibration Monitoring System by Weak Vibrational Energy for Industrial Internet of Things.

Developing self-powered smart wireless sensor networks by harvesting industrial environmental weak vibration energy remains a challenge and an impending need for enabling the widespread rollout of the industrial internet of things (IIoT). This work reports a self-powered wireless temperature and vibration monitoring system (WTVMS) based on a vibrational triboelectric nanogenerator (V-TENG) and a piezoelectric nanogenerator (PENG) for weak vibration energy collection and information sensing. Therein, the V-TENG can scavenge weak vibration energy down to 80 μm to power the system through a power management module, while the PENG is able to supply the frequency signal to the system by a comparison circuit. In an industrial vibration environment where the vibration frequency and amplitude are 20 Hz and 100 μm, respectively, the WTVMS can upload temperature and frequency information on the equipment to the cloud in combination with the narrowband IoT technology to realize real-time information monitoring. Furthermore, the WTVMS can work continuously for more than 2 months, during which the V-TENG can operate up to 100 million cycles, achieving ultrahigh stability and durability. By integrating weak vibration energy harvesting and active sensing technology, the WTVMS can be used for real-time online monitoring and early fault diagnosis of vibration equipment, which has great application prospects in industrial production, machinery manufacturing, traffic transportation, and intelligent IIoT.

Partial Discharge Wideband Full-Band High-Gain Resonant Cavity UHF Sensor Research.

To meet the real demand for broadband full-band high-gain antenna sensors in the process of partial discharge (PD) Ultra-High frequency (UHF) detection test and online monitoring of power equipment, this paper builds a resonant cavity monopole UHF antenna sensor based on Fabry-Perot resonant cavity antenna technology, conducts the sensor Voltage Standing Wave Ratio (VSWR) optimization study using curved flow technology, conducts the sensor gain optimization study using slot dual resonant structure, and, finally, tests the sensor performance using the built PD detection test platform. The resonant cavity monopole antenna exhibits outstanding VSWR performance in the frequency range of 0.37 GHz-3 GHz, according to simulation and test data: the average gain in the frequency range of 0.3 GHz-3 GHz is 4.92 dBi, and the highest gain at the primary resonant frequency of 1.0 GHz is 7.16 dBi, with good radiation performance over the whole frequency spectrum. The electromagnetic pulse signal sensed by the UHF sensor developed in this paper can demonstrate the energy spectrum distribution characteristics of PD radiation electromagnetic wave signal more comprehensively, laying a firm technical foundation for thoroughly understanding the electromagnetic wave radiation characteristics of various types of PD insulation defects of various power equipment and the selection of a specific direction for its supporting optimization.

Research on Substation Monitoring and Fault Diagnosis Based on Distributed Computing and Artificial Neural Network

In the Energy Conversion for Next-Generation Smart Cities, intelligent substation plays an important role in the power conversion. As an important guarantee for the stable operation of intelligent substation, the research on fault diagnosis technology is particularly important. In this paper, the acoustic characteristic diagnosis of substation equipment (take transformers for example) is researched and the application of “Voice Recognition + artificial neural network (ANN)” technology in substation fault diagnosis is analyzed. At the same time, the continuous online monitoring of the intelligent substation equipment will produce a large amount of monitoring data, which needs to be analyzed timely and effectively to understand the operating status of the equipment accurately. Because of this, this paper adopts distributed computing by establishing a real-time distributed computing platform, using open source technology to store the online monitoring of sound data into the computing platform for data processing to achieve the purpose of automatic fault detection and analysis. The results show that distributed computing can realize the intelligent analysis, storage, and visualization of equipment data in the substation, which provides data support for fault diagnosis. Besides, the fitting accuracy rates of ANN model are 95.123% for training process and the fitting accuracy rates of ANN model are 99.353% for training process and the overall fitting accuracy rates of ANN model are 95.478% and the error between the predicted value and the actual value of the 5 sound signals is within 5% in the fault diagnosis process. Consequently, the ANN model can accurately identify each fault sound of substation and achieve the purpose of fault diagnosis.

A Multirelay Wireless Power Transfer System With Double-Sided LCC Compensation Network for Online Monitoring Equipment

A multirelay wireless power transfer (WPT) system with the double-sided inductor-capacitor-capacitor (LCC) compensation network for the online monitoring equipment on the high-voltage transmission line is proposed. Constant-voltage (CV) or constant-current (CC) output characteristics is achieved. The proposed topology does not use ferrite materials, and the LCC compensation networks are only utilized at the transmitter and the receiver. Each relay coil is only connected in series with one compensation capacitor. Three working modes are proposed according to the resonance conditions of Tx compensation network, Rx compensation network, and compensating capacitances of relay coils. CC and CV output characteristics in each mode are analyzed and compared by simulation in terms of system output attenuation with load changes and frequency sensitivity. The parameter design process is given. A six-coil experimental prototype is built to validate the correctness and feasibility of the theoretical analysis.

Study on Selective Insulation On-line Monitoring Technology for Mine Power Network

In this paper, by studying the change rule of zero sequence voltage when single-phase leakage occurs, the detection method of insulation decline of mine cables is studied, especially the additional low-frequency signal sampling technology of power cables. Combined with the conventional leakage protection technology, the online monitoring equipment and supporting software for the insulation parameters of power cables are developed to realize the online monitoring of the insulation of each branch cable of the mine low-voltage power supply system and achieve the purpose of selective leakage protection.

On-line Monitoring of Instantaneous Over-voltage Intelligent Monitoring of EMUs

Since high-speed EMUs have high requirements for the reliability of the traction power supply system and the safety of train operation, the online monitoring and analysis of transient over-voltage are of great significance. By analyzing and comparing the characteristics of the existing power grid over-voltage online monitoring devices at home and abroad, and due to the difficulty in detecting the power grid voltage on the high-speed train, a new type of over-voltage online monitoring using the broadband transmission characteristics of the equipment is proposed for the high-speed train. The principle of the scheme is to reconstruct the real-time voltage waveform of the primary side of the transformer by convolving the secondary side voltage waveform of the vehicle-mounted voltage transformer through the fast Fourier transform with the transformer-specific transmission parameter model and to correlate the relevant data, such as power, voltage fluctuation, harmonic distortion, etc. Simultaneously, simulation and experiments are carried out to verify the validity and stability of the scheme algorithm. The experimental results show that the system has good response characteristics, high measurement accuracy, and stable and reliable working conditions. The system achieves the expected design goals, and it has been successfully applied to the online monitoring equipment of EMU power quality. While monitoring the over-voltage of the power grid, this system provides data support for the research on the aging and life of the onboard high-voltage electrical equipment.

Design of Inductive Power Taking System for Smart Grid Monitoring Equipment

Due to the continuous growth of urban electricity load and the increasing development of high-voltage transmission technology, the effective operation of the online monitoring equipment of the transmission line is particularly important, which is also a necessary condition for the development of the smart grid. However, the power supply conditions for the usage scenarios of smart grid monitoring equipment are limited. Conventional power supply methods such as battery power supply and new energy power generation have disadvantages such as cumbersome battery replacement, and the power supply stability is greatly affected by the environment, so they are difficult to meet the development trend of a rapid capacity increase of power grid and stable power supply of power equipment. Aiming at the technical bottleneck of smart grid monitoring equipment in power supply, this paper proposes a current transformer inductive power taking method based on capacitive reactance matching shunt, using the open air gap current transformer secondary winding set on the bus to connect the capacitor and its excitation inductance in series carry out capacitive impedance matching, and then shunt through another branch with load and connect it at both ends of the open air-gap current transformer to obtain electric energy. Through the circuit model experiment of the inductive power-taking system, it is verified that the current transformer inductive power-taking method based on capacitive reactance matching shunt can obtain more power.

A Dual-Frequency Dual-Load Multirelay Magnetic Coupling Wireless Power Transfer System Using Shared Power Channel

Aiming at the power supply solution of online monitoring equipment for high-voltage transmission lines, a dual-frequency dual-load multirelay magnetic coupling wireless power transfer (MC-WPT) system using a shared power channel has been proposed. The proposed MC-WPT system provides the selective power distribution for different loads and has the constant current output characteristic. First, a dual-frequency inverter is introduced to provide the ac voltage of two frequencies. The compensation network that includes the band-stop filter can reduce the cross interference of the nontarget frequency from another power channel. Second, the system is modeled and analyzed based on the quadratic eigenvalue problem. Considering the cross-coupling mutual inductance, operating frequencies that make the system have constant current output characteristics are obtained. Finally, three evaluation indices are proposed: average current gain, load-independent index, and average sensitivity index of operating frequencies. Optimized operating frequency is selected according to these evaluation indices. The experimental results verify constant current output characteristics and the selective power distribution of two power channels.

Unmanned stationary online monitoring system based on buoy for marine gamma radioactivity

Research on unmanned online monitoring equipment for marine radioactivity surrounding nuclear power plants is of great significance. In this work, a small radioactivity monitoring system based on buoy was designed and manufactured for the emergency situation of nuclear accidents. The core of the radioactivity monitoring system is the underwater gamma spectrometer. The spectrometer can respond to gamma rays from 60 keV to 3 MeV, and can identify the nuclides whose characteristic rays belong to this energy range. The detection efficiency curve was calculated through Monte Carlo simulation and verified in a standard liquid source. A data acquisition processor was also designed to coordinate the detectors in the system and wirelessly transmit online monitoring data. Three experiments were carried out in the seawater around the Tianwan Nuclear Power Plant in Lianyungang, China using this online marine radioactivity monitoring system based on buoys. The stability and radioactivity monitoring capabilities of the system have been verified.

Adaptive neuro-fuzzy interface system based performance monitoring technique for hydropower plants

ABSTRACT Energy has played a significant role in developing civilization, but the continuous use of fossil fuels has hampered the environment. Hydropower is the alternative to fossil fuels. But most of the hydropower plants in hilly areas suffer from silt erosion problems. Erosion of underwater parts creates vibration and noise and reduces machine efficiency. Therefore, online monitoring of turbines and other equipment is necessary to minimize losses due to erosion and part-load operation. Various studies are reported in the literature and found that correlation-based machine efficiency monitoring is one of the popular techniques. ANN method is useful for system modeling with a wide range of applications. However, despite the excellent classification capacities, its development can be time-consuming, computer-intensive, and prone to overfitting. In this paper, an Adaptive Neuro-Fuzzy Interface System (ANFIS) has been utilized to develop a correlation that removes the drawbacks of ANN and can predict the efficiency of the machine with an R2-value of 0. 99,976 having a Mean Absolute Percentage Error (MAPE) of 0.0108% at 0.06482% Root Mean Square Percentage Error (RMSPE).

A Real-Time Monitoring and Warning System for Power Grids Based on Edge Computing

It is very important for the safe operation of the power grid to be able to achieve real-time online monitoring, but along with the expansion of the smart grid, powerful computing power is required to achieve online monitoring, which cannot be loaded by ordinary servers. Due to the limited computing and storage resources of the server, it is difficult to fully meet the real-time requirements of online power line monitoring. Therefore, this research proposes a smart grid real-time monitoring and early warning system based on edge computing, which can reasonably allocate computing tasks according to the effective use of edge node resources. It can also collect and process information in real time, and can monitor the power grid online, which improves the fault identification efficiency of the power grid, effectively reduces the economic cost, and relieves the computing pressure of the online monitoring equipment on the cloud computing server. Meanwhile, considering the burst problem in allocation queue optimization, a multipriority allocation queue algorithm is proposed, and an improved greedy algorithm allocation model is used to solve the optimal scheduling problem between bursts. Simulation analysis shows that the scheduling scheme proposed in this paper can effectively reduce the monitoring delay of the power line monitoring system, improve the overall adaptability and compatibility of the system, meet the market demand, and facilitate promotion.