Table of contents

With great and successful prestigious history of ICETAC, 2023 4^th International Conference on Electrical Technology and Automatic Control (ICETAC 2023) was held in Chongqing, China during 24^th–26^th, November 2023 via virtual form. It was sponsored by School of Electrical Engineering, Chongqing University and organized by National Key Laboratory of Power Transmission and Transformation Equipment Technology.

ICETAC 2023 is a great academic platform and is of great significance for disseminating cutting-edge information in disciplines and industry, promoting the transformation of scientific and technological achievements, and condensing academic achievements in the industry at this event.

The Conference consisted of three plenary keynote speeches (addressed by Associate Professor Vandana Sharma from CHRIST University, India, Professor Xiaofang Yuan from Hunan University, China, and Associate Professor Qiushi Cui from Chongqing University, China, respectively), several oral and poster presentations, in which a wide range of topics were covered and the most recent significant results were presented. They discussed such topics as Unlocking the Potentials of AI in the domain of Automatic Control, Collaborative Operation Optimization of Processing Equipment and AGVs for Green Discrete Manufacturing, New Power System Modeling, Simulation and Testing Technology, etc. Besides, the attending scholars and experts exchanged deeply on the latest research achievements and advanced experience in the industry development and shared their unique insights into development trends and research directions of the industry.

Some selected works presented at the Conference are published in this conference proceedings edition of Journal of Physics: Conference Series., which shows readers the latest research results and application cases of electrical and automatic control technology. We firmly believe that by reading these papers, readers will deepen their understanding of these fields and appreciate their enormous significance and potential impact on contemporary society.

We wish to acknowledge all the participants, and the support of our sponsors. We would also like to take this opportunity to thank the members of Technical Program Committee and Organizing Committee, as well as the expert reviewers. What's more, we hope that the conference series ICETAC shall continue to the next many sessions and deliver its purpose as the arena for presenting and exchanging the ideas of research in the fields of electrical and automatic control technology and anything related.

The Committee of ICETAC 2023

List of Committee Member is available in this pdf.

All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing.

• Type of peer review: Single Anonymous

• Conference submission management system: Morressier

• Number of submissions received: 89

• Number of submissions sent for review: 65

• Number of submissions accepted: 43

• Acceptance Rate (Submissions Accepted / Submissions Received × 100): 48.3

• Average number of reviews per paper: 2

• Total number of reviewers involved: 10

• Contact person for queries:

Name: Xuexia Ye

Email: xx.ye@keoaeic.org

Affiliation: AEIC Academic Exchange Information Centre

Diamond nitrogen-vacancy (NV) center has been widely employed in experimental magnetic field measurement using continuous wave optical detection magnetic resonance (ODMR), which makes it possible for current sensing. An optical fiber current transducer prototype using an NV center is built for electric power application. The DC measurement with an error of less than 4% within 500 A is realized, in which the error of 50 A to 450 A is better than 2%, and the low-frequency AC measurement is preliminarily realized. In this essay, the accuracy of low current measurement is primarily affected by system noise, while the accuracy of heavy current is mainly affected by the width of the ODMR resonance peak. Optimizing the optical path as well as sensing structure meanwhile updating the demodulation algorithm of the measured data is the following work to improve the measurement accuracy.

With the increase in power demand and the increasing complexity of the power grid, the operation of relay protection devices in substations is often unreasonable. Therefore, a quantitative evaluation method of operation reliability of relay protection devices in substations based on an improved neural network algorithm is proposed. The time-varying probability of misoperation of relay protection in substation is calculated based on the improved neural network algorithm, identifying misoperation behavior of relay protection device, obtaining the optimal hyperplane decision function, selecting kernel function, and construct a quantitative evaluation function of operation reliability of relay protection device. In the evaluation process, two stages of the evaluation process are carried out, namely, reliability block diagram analysis, failure mode evaluation, and verification of relay protection device operation state, so as to complete the reliability quantitative evaluation. The experimental results show that this method can minimize the error of evaluation training and improve evaluation efficiency. At the same time, the error rate is small, which can be controlled within 0.5%.

Aiming at the problems of electric vehicle disorderly charging on grid load stability and charging cost, this study considers the grid load pressure and presents a multi-faceted optimized model for electric vehicle charging. The model is addressed by utilizing a particle swarm optimization algorithm designed for multiple objectives. The outcomes demonstrate that the charging framework built upon the multi-objective particle swarm algorithm has a fast convergence speed and can avoid the limitation of the local optimal solutions. Under the premise of reducing by managing the grid load fluctuation, the model effectively curbs the expense of vehicle charging while also minimizing peak-to-valley disparities in grid load.

New energy power prediction is an important part of the transition process from the traditional power system to the new power system. How to improve the power prediction accuracy while ensuring that data privacy is not leaked is an issue that needs to be focused on. Based on this, this paper constructs a new energy power prediction model integrating NGBoost and LSTM by screening the optimal feature sequences as model inputs, then encrypting the transmission aggregation process of model parameters and finally testing and evaluating the scheme based on a real data set. Experiments show that the scheme proposed in this paper not only improves data confidentiality to a certain extent compared with a single prediction model, but also the model is characterized by robustness and high prediction accuracy.

Influenced by the unbalanced state of particle swarm in the process of fuel combustion in thermal power plants, the fuel cost in the thermal power generation stage is relatively high. Therefore, a new particle swarm optimization model for fuel combination in thermal power plants is proposed. Combined with the combustion properties of different fuels, from the point of view of particle swarm optimization, in the process of carrying out specific particle swarm optimization simulation, the original particle swarm optimization algorithm is improved adaptively. A new particle swarm optimization algorithm is constructed by coupling it with a multiphase turbulence model. The fuel combustion performance of the power plant is analyzed by using its coupling. The optimization model of fuel combination in the thermal power plant is constructed by maximizing the energy release of fuel as the core, and the state of the fluid-particle field is taken as the constraint condition. In the test results, the design of the fuel combination optimization model can fully improve the energy release degree of fuel and reduce fuel consumption under the same power generation demand, which has a positive effect on power generation cost control.

YOLO target detection algorithm is the mainstream method for image-based defect detection of transmission line insulators. However, the existing model complexity is large. A reasonable and effective parameter compression method is urgently needed as a prerequisite to lay the foundation for solving the dilemma of UAV edge equipment deployment. At the same time, the insulator defect images taken by UAVs have complex backgrounds and small defect sizes, which are prone to misdetection and omission. It is needed to improve the channel domain of the CBAM (convolution block attention module) attention mechanism and solve its problem of missing channel information due to dimensionality reduction. The improved CBAM is added to the backbone network of YOLOv5s. The goal is to enable the model to identify and pinpoint the critical objectives with greater precision. The model is combined with Cross Entropy Loss and Lovasz-Softmax Loss weighting, which makes the network converge more stably during the training process, and the accuracy rate is also somewhat improved.

The energy flow optimization of the integrated energy system (IES) can not only decrease the operating cost of the IES but also maximize the utilization rate of renewable energy. To deal with the problem that the optimization results fall into the local optimal solution due to the over-limit of IES state variables, based on the IES energy circuit theory, this paper established an energy flow optimization model of the electric-gas-heat integrated energy system by taking the weighted sum of the operating cost, state variables over-limit penalty cost, and wind and light abandonment penalty cost as the optimization objective. The growth optimizer (GO) algorithm can avoid the local convergence of the optimization results and accelerate the solving speed of the model. Therefore, the effectiveness and feasibility of the model and the optimization method are confirmed by the simulation example.

This article focuses on the assistance of electric lifting devices for phase-to-phase live working. Taking a typical 750 kV double circuit vertical arrangement line on the same tower as the research object, a three-dimensional simulation model of a real tower conductor simulated human is established. The field strength on the human body surface is calculated and analyzed during the process of operators entering the equipotential (conductor) while riding the electric lifting device in order to determine the risk points of the operation and determine the safety protection measures for the human body. Through simulation, it is found that when simulating human entry (exit) and phase-down operations, the human body is located at different distances of 0.4 m-1.5 m directly below the conductor. When not wearing shielding clothing, the maximum field strength of the human head is much greater than the allowable value. If using shielding clothing with a shielding efficiency of 60 dB for safety protection, the surface field strength inside the worker's clothing can be reduced to 2.25 kV/m, meeting the requirements of live working. Operators should pay attention to the electric field protection at each phase wire when using an electric lifting device to enter (exit) the interphase wire. Equipotential operators must wear a complete set of shielding clothing for 750 kV live working.

To locate increasingly frequent intermittent ground faults in medium-voltage shipboard power systems, this study proposes a fault branch selection method using wavelet analysis. By analyzing transient fault current characteristics in ship medium-voltage grids, this method utilizes wavelet analysis tools to extract features and obtain modulus maxima of branch currents. Based on a dual threshold detection strategy, the fault branch is then identified. The feasibility and validity of the proposed method are verified through simulations on the Matlab/Simulink platform. The simulation results demonstrate the capability of the wavelet analysis approach to accurately locate intermittent ground faults in ship medium-voltage power systems. This research provides an effective solution for fault branch selection of intermittent grounding faults in ship medium-voltage grids.

220 kV high-voltage submarine cables find extensive application in offshore wind power transmission systems. Ensuring the safe operation of these cables involves accurately detecting and assessing the extent of damage to the cable sheath. This study begins by constructing a finite element simulation model for the cable based on its actual model. Subsequently, a method for evaluating the cable sheath's damage state is proposed by using a BP neural network. The network takes ambient temperature, current, and six-point temperatures of the damaged section as input characteristics. Results indicate a correct rate of 94.29% for the BP neural network, demonstrating its effective discrimination of cable sheath damage levels. This approach introduces a novel evaluation method for cable sheath damage.

Subsynchronous Oscillation (SSO) is a common dynamic stability issue in power systems that integrate wind power. This paper proposes a comprehensive method for assessing the localization of SSO in wind power integrated systems. The localization method applies the transient energy flow approach to identify SSO sources in systems with wind power. Additionally, a relevant SSO index system is established where the value of each index is calculated. By using the Fuzzy Inference System (FIS) to judge the index, the SSO disturbance source contribution rating of each area is obtained. Simulation cases demonstrate that the localization method of the transient energy flow approach can effectively identify the SSO disturbance source. Moreover, the SSO disturbance source contribution rating method based on FIS can determine the SSO contribution degree of each area, thus identifying the dominant oscillation disturbance sources. The proposed method can provide valuable insights for formulating emergency control measures.

SF₆ mixed gas is widely used as an insulation and arc extinguishing medium in electrical equipment in cold regions of northern China. Due to its low liquefaction temperature, existing gas recovery technologies are unable to achieve efficient and rapid recovery of SF₆/N₂ mixed insulation gases. In response to this challenge, a two-stage membrane separation and recovery technology has been proposed, which can efficiently and quickly separate SF₆ and N₂ from the mixed gas. The volume fraction (V/V) of obtained product SF₆ gas reaches 90%, and the use of compression and refrigeration units can achieve rapid filling of 50 m³/h mixed gas. At the same time, the enrichment and recovery of trace SF₆ gas in the exhaust gas are achieved. After enrichment, the volume fraction (V/V) of SF₆ gas reaches 92%, and the volume fraction (V/V) of SF₆ gas in the emptied mixed gas is 600 μL/L, far below the national standard value, solving the problems in actual production.

This paper studies three methods of DC energy measurement for non-vehicle chargers, namely, the average value method, effective value method, and time domain integration method, introduces the definition of ripple, and analyzes the characteristics of constant voltage, constant current charging mode and pulse charging mode of electric vehicles, providing an algorithm basis for the establishment of DC energy metering error model and simulation analysis. This paper studies high-power DC energy measurement algorithms with ripple parameters, analyzes the error characteristics of various algorithms, summarizes the relevant laws, and provides a theoretical basis for the design of a high-precision DC metering module under the influence of ripple.

Due to the constant power loads (CPLs), the system damping of the DC microgrid is reduced, which will lead to the collapse of bus voltage. In addition, the errors of current sharing amplify due to different line impedances. To address these issues, a hybrid coordination control strategy is proposed for parallel-connected boost converters, which realizes stable control and maintains the accuracy of the current distribution. Firstly, a passivity-based control (PBC) with a proportional-integral (PI) regulator is developed. The virtual damping-based PBC enhances the system damping and the PI regulator eliminates errors. On this basis, a secondary voltage control (SVC) featuring simplicity and weak dependence on communication is introduced to remove the errors of current distribution. Finally, the hybrid coordination control strategy is verified by RT-LAB-based hardware in the loop (HIL) experiment.

In order to improve the ignition reliability of a small-thrust rocket engine, a three-dimensional sliding arc igniter discharge characteristic research experimental system was constructed. The work gas flow rate and excitation parameters were changed, the plasma discharge characteristic and spectral characteristic parameters were measured, and the characteristics of the igniter discharge modes were investigated. The influence of the excitation parameters on the discharge modes was explored, and the effect of the discharge modes on the ignition performance of the igniter was analyzed as well as the mechanism of the discharge modes. The results show that the igniter has three different discharge modes during the discharge process, i.e., breakdown with sliding mode, high-power breakdown with sliding mode, and the transition state between the two modes, the shift of the discharge mode is mainly affected by the excitation power, and the igniter is in the breakdown with sliding mode when the excitation power is low, and in the high-power breakdown with sliding mode when the power is high. High-power breakdown with sliding mode has both breakdown with sliding and stable sliding characteristics in the early stage of the sliding arc movement for the breakdown with sliding and then transformed into sliding. Compared with the breakdown with sliding mode and high-power mode, the sliding arc discharge area is wider, the concentration of O * than the breakdown with sliding mode increased by 2.86 times, the electron temperature increased by 31.76%, and it can significantly improve plasma ignition effect, reduce the activation energy and enhance the performance of the igniter.

Photovoltaic (PV) power generation, with its volatile, intermittent, and random characteristics, and large-scale PV access pose a threat to grid stability. For this reason, predicting the photovoltaic output will help keep the grid safe and stable. On the basis of the influence of cloud groups on solar radiation, a very short-term forecast of distributed PV energy will be made using satellite cloud picture information to improve the forecast accuracy of PV energy production. The paper presents a method to predict distributed PV power at very short notice based on satellite clouds and a network model with Long Short-Term Memory (LSTM). First, extract a subset of meteorological and PV power data from the forecast area as training samples., and the abnormal part of the samples is cleaned by an isolated forest algorithm. Secondly, the occlusion feature is extracted from the satellite cloud image in the same period. Finally, the measured solar irradiance, meteorological information, and obscuration features are input into the LSTM network for prediction, and the photovoltaic power prediction results in the next 4 hours are obtained. The measured PV power of Jinghai Guangfu Power Station in Hefei, Anhui province on the 5th day was the training sample for the prediction of PV power on the 6th day. The prediction results show that the prediction error is 2.73% when a satellite cloud image is added, and 16.15% when a satellite cloud image is not added, and the prediction error is reduced by 13.42%.

An improved matrix pencil (MP) algorithm for power system mode identification has been proposed in this paper. Aiming at the problem of inaccurate order determined by the singular value decomposition (SVD) step in traditional MP algorithms and the lack of Hankel structure in the reconstructed matrix, which leads to inaccurate identification under low signal-to-noise ratio (SNR) condition, singular entropy order determination is introduced. An iterative algorithm is proposed to calculate a reconstructed matrix with both Hankel structure and rank-deficient attributes. This reconstruction matrix has a better approximation effect and improves the identification accuracy of the MP algorithm under low SNR. The effectiveness of the proposed algorithm was verified by modal parameter identification of numerical signals and oscillation signals of the 4-machine and 2-area systems. The algorithm proposed in low SNR has higher accuracy than the traditional MP algorithm, and the reconstructed signal has a better fitting effect.

Dissolved Gas Analysis (DGA) of transformer insulation oil is an effective method for monitoring transformer operating conditions and diagnosing faults. Photoacoustic spectroscopy (PAS) is widely employed for online dissolved gas analysis in transformer oil. The positioning of the pressure equalization hole within the sound field significantly impacts the microphone's sensitivity when coupled with the photoacoustic cell in non-resonant photoacoustic spectroscopy. The static temperature and humidity characteristics inside the photoacoustic cell also have a notable influence on the microphone's output signal. Consequently, an analysis is performed to explore the relationship between the sensitivity of the photoacoustic spectrometer and temperature/humidity, leading to the optimization of the system configuration based on the microphone parameters. Moreover, the photoacoustic zero-point signal is influenced by the temperature and humidity levels within the photoacoustic cell. To address this problem, air mixtures at different humilities are measured, resulting in the establishment of a zero-point model for the background signal in Photoacoustic Spectroscopy (PAS). Subsequently, the experimental detection of acetylene under various humidity mixtures is conducted, successfully extracting effective signals for the measurement of 5 ppm acetylene gas. These results serve as evidence showcasing the efficacy of both the proposed structural optimization and zero-point model put forth in this study.

Differential confocal microscopy is widely used because of its ultra-high axial resolution. The surface gradient results in light loss, which decreases the slope of the differential response signal at zero crossing. At this point, when the signal-to-noise ratio is fixed, the traditional linear fitting method to determine the position of zero crossing is subject to significant error influence. To solve these issues, this paper proposes a zero crossing detection algorithm based on a multilayer perceptron (MLP) neural network. Experimental results reveal that the proposed algorithm is more robust and capable of better zero crossing extraction. When numerical aperture (NA)=0.4, the average error is 16.9 nm, which is 55.4 % higher than that of the traditional linear fitting algorithm. The proposed algorithm has a high potential for use with the differential confocal sensor to measure unknown steep surfaces.

This paper investigates the issue of stability of interconnected systems, where the systems are composed of perturbed positive subsystems and a interconnection matrix. To address the challenge posed by disturbances, a disturbance observer is formulated for disturbance estimation. The estimated disturbance is incorporated into the input of a controller, which is crafted to produce control inputs reliant on both the system state and the estimated disturbance. Using linear copositive functions, a controller design methodology based on linear programming is established. Ultimately, the efficacy of the proposed approach is substantiated through the illustration of numerical example.

Domestic residue recovery machines in China still follow the traditional towed mechanical model, lacking automation and intelligent features. Under the key R&D project of Xinjiang Autonomous Region, our team has developed integrated intelligent agricultural machinery for cotton straw crushing and residue recovery. This research covers the monitoring and control system, including PLC control and communication, working parameter monitoring and adjustment, algorithmic control of electro-hydraulic actuators, and human-machine interface display. Compared to traditional models, this machine replaces the conventional chain-driven mechanical structure and diesel tractor traction power with a fully hydraulic integrated suspension drive and power system. Sensors, PLC, and a touch screen are utilized for real-time monitoring and display of parameters. Hardware components like electromagnetic switch valves, electro-hydraulic proportional valves, hydraulic cylinders, and software elements such as PLC programs and intelligent control algorithms are employed for electro-hydraulic switching and proportional control of certain working parameters and mechanisms. All parameter information is consolidated in the PLC host and expansion modules and displayed and controlled on the human-machine interface. The system was installed on the first-generation machine for indoor testing, revealing that the monitoring and control system used on the first-generation machine achieves parameter detection and adjustment of the actuating mechanisms. It incorporates more advanced functions compared to traditional residue recovery machines. However, there is still ample room for improvement in the overall intelligence of the machine. The control system's structure requires adjustment, and certain functionalities of the overall mechanical structure and working components need optimization or upgrading. Detailed enhancements to the entire system will be made in the next-generation machine.

In the process of oil production, the submerged oil motor works 1-3 km downhole and is connected to the inverter by a long-wire cable, which generates overvoltage at the motor end due to the problem of matching the impedance of the motor with the impedance of the power cable. The downhole environment is not suitable for the installation of precision devices, such as position sensors. To solve these problems, a sensorless control method of submerged oil motors based on a high-frequency voltage injection method is proposed. A motor terminal voltage and current observer under a long-wire cable model is designed to estimate the actual values of motor terminal voltage and current by collecting the voltage and current at the inverter terminal. A three-level inverter is used to provide energy to the submersible oil electric pump through the long-wire cable, which can effectively suppress the motor terminal overvoltage compared to the two-level inverter. The control algorithm is verified by modeling the whole system using simulation software.

This study aims to optimize the speed control effect of fuzzy PID controller in brushless DC motor control, and improve the traditional genetic optimization algorithm to improve fuzzy PID algorithm, mainly for genetic algorithm coding, crossover, and mutation process. In the three processes of the genetic algorithm, real number field coding is used, Taguchi orthogonal table is introduced into the cross-process, and Gaussian variation is applied to the mutation process. In the part of model construction and verification, this study uses Simulink software to build a brushless DC motor model and sets the same conventional parameters for simulation. The simulation results show that the algorithm overshoot and stability time of the fuzzy PID controller optimized by the improved genetic algorithm are obviously improved.

The performance state evaluation method of circuit breaker energy storage spring mainly judges its performance state indirectly by measuring the pre-tightening force or pre-pressure of the spring. However, there may be some errors in this indirect measurement method, which will affect the accuracy of the evaluation results. Therefore, the performance state evaluation based on intelligent algorithm is proposed. Select the evaluation characteristic quantity of performance state, calculate the energy storage spring impulse according to the momentum theorem, and obtain the pressure value of the closing energy storage spring through the pressure sensor as the evaluation quantity reflecting the energy storage spring performance state. The BP neural network is established, and the fireworks algorithm is applied to the BP neural network to optimize the initial weight and threshold, so as to realize the performance state evaluation of energy storage spring based on BP neural network. The experimental results show that the spring energy release speed of the proposed method is in the range of 0 - 1.0m/s, and the estimated spring pressure value is basically consistent with the actual value.

In the perpendicular simple sinusoidal excitation, the damping characteristics of the soft-wall particle damper with a damping rod were experimentally investigated by controlling the acceleration amplitude and vibration frequency of the damper motion and measuring the force and acceleration signals. The experimental results show that the loss power of the damper increases monotonically with the rise of the excitation acceleration amplitude, and the effective mass shows the change of "gentle decrease-critical point-decrease". As the driving frequency rises, the loss power progressively decreases, and the effective mass does not change significantly. By analyzing the damping characteristics of different dampers, it shows that the damping effectiveness of the soft inside wall particle damper with a damping rod is obviously better.

Unmanned Underwater Vehicles (UUVs) are essential equipment for Marine development, widely used in Marine scientific research, Marine resource survey, and Marine security. The autonomous navigation planning ability in unknown environments is a critical indicator for UUV intelligence. This paper focuses on the particularity of UUV motion and the complexity of the underwater environment and proposes an adaptive Dynamic Window Approach (DWA) for UUV obstacle avoidance planning. The adaptive DWA introduces novel heading angle evaluation and adaptive dynamic strategies to solve the problems of poor adaptability of traditional DWA to complex environments and unreasonable path selection in encountering dynamic obstacles and approaching targets. Simulations verify the effectiveness and superiority of the proposed method.

Due to the way line drive, the continuous endoscope robot will have a certain lag in the process of movement, which will affect the accuracy and flexibility of the operation. In addition, the hysteresis and return difference caused by wire rope transmission will also increase the hysteresis effect of continuous robots. In this paper, the motion characteristics of the continuous endoscopic robot are analyzed, the hysteresis of the robot is modeled theoretically based on the Preisach model, and the effectiveness of the model is verified. Preisach hysteretic nonlinear hyperbola model predicts the bending changes under different positive and negative drives. The experimental results show that the maximum error between the model and the theoretical prediction is 3.18 degrees. The hysteresis model can predict the hysteresis characteristics of the continuous endoscope robot well.

In modern wars and regional conflicts, in order to reduce casualties, more and more unmanned equipment and weapons are utilized. Among them, the UAV is the new favorite in the sky. Application fields of the military UAV are wide and varied. It is found to be used for intelligence reconnaissance, battlefield surveillance, as well as strikes. As for the scout UAV, long endurance is of utmost importance. There is no denying that less weight can result in longer endurance. A departure device for the assistant landing gear of a long-endurance UAV is designed in this paper. The system constitution and working principles are introduced in the beginning. Then, detailed system designs are presented, including the hardware design, software design, and other parts of the departure device. Several static and dynamic experiments are conducted to prove its feasibility. At last, the conclusion part summarizes its applications.

Due to the coupling effect between the DC/AC system as well as between different LCC-HVDC, the first commutation failure can cause a series of chain reactions, including successive commutation failure, overvoltage of the rectifier station, and subsequent commutation failure. The existing control methods for suppressing overvoltage or commutation failure cannot simultaneously meet the needs of the sending and receiving end grid of MIDC. It may expand the impact and scope of a single fault. Therefore, a commutation failure chain reaction suppression method is proposed based on the characterization of chain reaction boundary conditions. The effectiveness is verified by using the CIGRE standard test model.

The lifespan of transformers is closely related to their operating temperature. Among the existing temperature acquisition methods, the measurement method cannot obtain the complete temperature field temperature, while the numerical calculation method presents the challenge of significant computational expenses. This study theoretically derived how to determine the real-time temperature distribution of transformer windings based on sparse measurement data and reduced order models and solved sensor layout issues, including the number and placement position of sensors. In this study, the maximum calculation error of the temperature distribution is 1.8 K, and the calculation time is 0.38 seconds. The research findings suggest that multiple modes can contribute to the temperature distribution of a transformer. Under a constant load condition, the calculation of transformer temperature can be achieved by utilizing a temperature sensor positioned at the hot spot in conjunction with the first mode.

A distributed UAV automatic airport inspection system, with the advantages of unmanned autonomous inspection operation, is widely used in power patrol, highway inspection, forest fire monitoring, and other fields. The disadvantage is that the operating radius distance is limited, with the multi-fixed site layout and high cost. In this paper, a vehicle-mounted rotor UAV automatic airport inspection system is designed, which can flexibly move the operation site and increase the operation radius. This inspection system is based on 5G wireless communication control technology and can remotely control the UAV. After the operation is completed, the UAV is automatically stored, charged, and maintained, and other functions have a good application prospect.

This article introduces a treatment method, component concentration calculation model, and device for preparing standard oil using transformer oil. The base oil is prepared by deeply purifying the transformer oil and measuring its background value. Based on the target component concentration, the appropriate volume of standard gas is determined, and the background value of the combined oil is used in the component concentration calculation model to determine the theoretical value of the standard oil. The standard oil value that has been prepared is in good agreement with the anticipated target value, thereby simplifying the purification process and cutting down on the processing cost of used transformer oil. Simultaneously, a fully automatic integrated device has been developed for the advanced purification of transformer oil and the production of standard oil. This device comprises a vacuum pump, an oil storage tank, an electromagnetic valve, an oil cylinder, and various sensors, all managed by sophisticated software. The standard oil for transformers can be prepared and stored stably for an extended period. The standard oil output is processed under sealed and unchanging pressure conditions to ensure a consistent concentration of components in the standard oil.

Tunable Diode Laser Absorption Spectroscopy (TDLAS) technology combines the advantages of wavelength modulation technology and direct absorption spectroscopy technology, and the system achieves highly sensitive and full-range detection of methane gas concentrations. In the system, only the pure optical methane probe is positioned under the mine, which establishes connectivity with the host computer on the surface through the optical fiber. This setup enables the simultaneous detection of 16 channels of methane gas through time-sharing multiplexing technology. Experimental results demonstrate a measurement error of less than ±6.0×10⁻⁴ at low concentrations (0∼1%) and within ±6% of the true value at high concentrations under normal temperature and pressure conditions. The full-range linear fit attains 0.9998. According to the one-fold standard deviation, the minimum detection lower limit of the system stands at 68.36 ppm and the measured gas concentrations across different channels of the methane probes remain consistent inhe same environment. This technology provides a new solution for continuous, accurate, and multi-point monitoring of mine gas.

In order to protect the health of older people, a wearable lower extremity motion data acquisition system was designed. With STM32F103C8T6 as the core, the system uses an inertial attitude sensor to collect lower limb motion attitude information. In view of the large difference in plantar pressure data of different fall types, the distributed flexible film plantar pressure sensor is used to collect plantar pressure information as an auxiliary discrimination means, which can accurately distinguish the fall behavior from other behaviors, and carry out data transmission through the wireless module. The device is small in size, light in weight, low in power consumption, and wearable, which is easy to apply in the field of health monitoring, and can also be used as a reference for the implementation of corresponding protection strategies for active lower limb prosthetics.