Table of contents

The 2022 2^nd International Conference on Electrical Engineering and Computer Technology (ICEECT2022) was successfully held on September 23-25, 2022 in Suzhou, China (virtual conference). ICEECT2022 promoted the research and developmental activities in relevant fields as well as the scientific information exchange between researchers, engineers, and practitioners working all over the world.

In the conference, we were greatly honored to have Prof. Marco C. Campi from University of Brescia, Italy to serve as our Conference General Chairman. The conference consisted of keynote speeches, oral presentations and online Q&A discussion, attracting 150 delegates worldwide. Firstly, the keynote speakers were each allocated 30-45 minutes to address their speeches. Then in the next part, oral presentations, the excellent papers we had selected were presented by their authors one by one.

Two distinguished professors were invited to hold keynote speeches during the conference. For a start, Prof. Prof. Wei Fang from Nanjing University of Information Science & Technology, China performed a keynote speech on the title: Research on the Application of Artificial Intelligence in Meteorology. The Artificial Intelligence (AI) is playing a more and more essential role in the industrial revolution and we are seeking a lot of evolution in various machine learning methodologies. Forecast of meteorological disasters is an important and challenging worldwide problem, so in this speech he shared his experience and research results in all aspects of the application of meteorology based on artificial intelligence. Moreover, Assoc. Prof. Moh'd A. Radaideh from Jordan University of Science and Technology, Jordan made a report on The Software Project Management: from the Perspectives of the PMI, IEEE SWEBOK V3.0, and Agile-SCRUM. In this presentation, he elaborated on his own experience of designing, articulating, and delivering a reasonable senior course on the subject of Software Project Management (e.g. SE440 Software Project Management), part of the Undergraduate Software Engineering Program Curriculum at Jordan University of Science and Technology. Their dramatic speeches had triggered heated discussion. And every participant praised this conference for disseminating useful and insightful knowledge.

We are glad to share with you that we've selected a bunch of high-quality papers from the submissions and compiled them into the proceedings after rigorously reviewing them. These papers feature but are not limited to the following topics: Transmission and Distribution, Electromagnetic Compatibility, Mechatronics, Database Systems, Software Engineering, etc. All the papers have been checked through rigorous review and processes to meet the requirements of publication.

We would like to acknowledge all of those who supported ICEECT2022 and made it a great success. Particularly, we would like to thank the Journal of Physics: Conference Series, for the endeavor of all its colleagues in publishing this paper volume. We sincerely hope that ICEECT2022 turned out to be a forum for excellent discussions that enable new ideas to come about, promoting collaborative research.

The Committee of ICEECT2022

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 Publishing.

• Type of peer review: Single Anonymous

• Conference submission management system: Morressier

• Number of submissions received: 136

• Number of submissions sent for review: 117

• Number of submissions accepted: 71

• Acceptance Rate (Submissions Accepted / Submissions Received × 100): 52.2

• Average number of reviews per paper: 2

• Total number of reviewers involved: 30

• Contact person for queries:

Name: Xuexia Ye

Email: xx.ye@keoaeic.org

Affiliation: AEIC Academic Exchange Information Centre

The Fluid-structure interaction characteristics on the two drag-type horizontal axis hydraulic turbinesare investigated. The results show that the two drag-type horizontal axis hydraulic turbines are suitable for operation under low flow rate and low TSR conditions. The Tuebine 2 has a higher C_P value from TSR=0.5 to TSR=2.0, and has a lower C_T value in the whole TSR rangethan the Turbine 1. The maximum stresses are at the blade root area, and the maximum deformations are at the blade tip for both turbines. The Turbine 1 has a higher stress level and total deformation than the Turbine 2. The frequency on 1st, 2nd, 3rd, 4th and 5th order vibration modes of the Turbine 1 is higher than the Turbine 2, and the 6th one is not. The results provide a reference for the drag-type horizontal-axis hydraulic turbines.

The medium and long-term operation of the unit is an important part of the operation of the power energy system. With the development of IES, the coupling of various energy forms such as electricity, heat and natural gas has been continuously strengthened. When a CHP unit or a gas unit is shut down, its impact will involve a variety of energy systems. Therefore, It is of great significance to formulate a reasonable annual unit combination plan and a coordinated optimization plan for the maintenance plan. First of all, starting from the coupling mechanism of the power supply, heating and gas supply of the unit, consider the relationship between each system and equipment. On this basis, taking into account the system operation constraints, unit maintenance constraints and unit operation constraints, a combined dispatch model of IES unit combination and maintenance plan including power-to-gas (P2G) and electric boiler equipment are proposed. The accuracy and effectiveness of the proposed model are verified through simulation analysis of a numerical example. The calculation example results show that the overall arrangement of the unit maintenance plan and the unit combination plan in the integrated energy system can improve the operating economy of the system; the introduction of coupling equipment such as P2G and electric boilers can effectively improve the new energy consumption rate and reduce the system operating cost.

This paper proposes a bilevel optimal scheduling model considering the flexibility and peak shaving benefits of gas-steam combined cycle units, which realizes "thermoelectric decoupling" and the flexibility of the unit by configuring electric boilers, and adds the benefit of peak shaving to the upper model to maximize the profit of the unit. Among them, the upper model is the unit optimal bidding model considering flexibility and peak shaving benefits, and the lower model is the power market-clearing model considering the maximization of social welfare. The bilayer model is transformed into a mixed-integer linear programming problem through KKT conditions and other solution methods. The effectiveness and rationality of the model are verified by the analysis of examples. The results of examples show that "thermoelectric decoupling" and start-stop peak shaving can effectively improve the profit of the gas-steam combined cycle unit and the wind power consumption capacity of the system. Finally, the strategic bidding behavior of gas-steam combined cycle units in different periods of the power market is explored.

With the increasing penetration of distributed photovoltaic (PV) in distribution network (DN), and the randomness of PV power output, so the DN with PV is prone to voltage fluctuation or even overvoltage. In this paper, a double-layer distributed PV voltage control strategy is proposed. This strategy takes full advantage of reactive power of PV inverter connected to DN and aims at reducing the loss of uploading power of PV, so as to achieve collaborative control of buses voltage. It adopts the double-layer control strategy of PV system in DN, as well as the three modes switching of maximum power point tracking (MPPT), reactive power regulating and power factor regulating of PV. On the premise of realizing the effective control of buses voltage, it can reduce the loss of uploading power of PV to the greatest extent. Finally, the proposed distributed PV voltage control strategy is verified through a typical DN case based on the real time digital simulation (RTDS).

The distributions of charge trap energy levels in polymer insulation material have an important influence on the charge transport characteristics and breakdown field strength of the polymer insulation, which thus has attracted considerable attention. Based on the quantum chemistry calculations, the characteristics of charge traps induced by different chemical defects and additives in silicone rubber materials are investigated. The projected density of states, and frontier molecular orbital distribution of defective silicone rubber and additive molecules are analyzed. The carbonyl defect, carboxyl defect and diethyl maleate additive all introduce deep electron traps (more than 1eV) in PDMS materials. The carbonyl defect and KH550 additive introduce shallow hole traps (less than 1eV) in PDMS materials. If chemical defects have a dominant contribution to the frontier molecular orbital of polymer is the key factor of whether they can introduce charge traps.

Silicone rubber is an insulating material widely used in DC cable accessories. Its interface is a weak area of insulation. Hydrated silica (SiO₂·H₂O) is a reinforcing agent commonly used in SR, and its main component is SiO₂. Based on the theory of nano-composite dielectric, four kinds of composite silicone rubber samples with different filler contents were prepared by taking silica as the filler and liquid silicone rubber as the matrix. The properties of composite samples were tested. It is found that nano-modification can optimize the surface morphology distribution characteristics of silicone rubber, reduce the roughness, improve the tensile strength, and deepen the trap energy distribution of silicone rubber. The results of the research provide help for the design of DC cable accessories.

The volatility and uncertainty of renewable energy sources aggravate the difficulties of power consumption balance in the new power system. One practical approach is to equip the grid with sufficient energy storage to reduce the operation risk. This paper focuses on a provincial grid with a heavy power outward delivery burden and its storage allocation problem. First, the optimization model with minimum storage allocation is established. Scenarios are constructed based on the typical set and the validation set. Second, a data-driven method is applied to build the typical set by k-means++. To describe the tolerance of wind and solar curtailed and delivery shedding, a chance-constrained approach is established and converted to linear constraints by big-M. The case study shows the power system needs considerable energy storage to ensure renewable energy consumption and delivery. With the lower tolerance, the allocated storage capacity will increase, and the lowest feasible tolerance is limited by scenarios with poor renewable output.

To solve the problem that the torque pulsation is serious due to the large current error in the period when the duty cycle is calculated by the deadbeat method in the duty cycle dual-vector model predictive current control method, a vector switching method is proposed. The control objective of the proposed method is not to equate the predicted torque current with the reference value under the dead-beat idea, but to reduce the torque current control error by changing the switching time of the voltage vector and ensuring the symmetry of the positive and negative current errors. By deducing the duty cycle, the maximum current errors in a single control cycle before and after vector switching is compared and analyzed. The vector switching method is verified on the Simulink simulation platform and compared with the duty cycle double vector method. The results show that the vector switching method can effectively reduce the intra-cycle torque current tracking error and realize the optimal process control in each cycle.

With the continuous improvement of the proportion of wind power generation, the volatility and uncertainty of wind power pose a serious threat to the stable operation of the power system. However, the traditional primary frequency regulation strategy does not fully consider the influence of wind turbine inertia. In addition, it may cause the secondary frequency drop if the instantaneous power of the wind turbine drops too much during the speed recovery. To solve this problem, this paper proposes an adaptive primary frequency regulation strategy for the mobile load shedding power tracking curve. Under this strategy, by controlling the output active power reference value, the kinetic energy stored in the rotor is fully released to support the grid frequency for a short time. Then, in the process of rotor speed recovery, the frequency secondary drop can be alleviated by moving the load-shedding power tracking curve. Finally, the simulation results in MATLAB/Simulink verify the rationality of the proposed adaptive load-shedding control strategy.

The scale and complexity of the power system are increasing, which carry out a lot of difficulty and effort to detailed simulation of the whole system, so effective dynamic equivalence is the premise of the modeling of electromagnetic transient simulation of large power systems. This paper targeting at the present situation of lack quantization standard in the dynamic equivalence, this paper took the posterior error criterion of grey forecasting model into the Dynamic Equivalence Evaluation, then proposed more detailed and exactly quantization standard appraisal index system, further perfect the error evaluation system of dynamic equivalence efficiency.

The Active power filter (APF) plays an important role in reducing the harmonic distortion rate of grid current and improving grid stability. However, the APF based on traditional methods such as deadbeat control and hysteresis control has poor tracking accuracy for the load harmonic current, and it is difficult to achieve efficient compensation. In this regard, this paper proposes a current inner loop quasi-proportional resonant (PR) control method to achieve fast-tracking of the compensation current and high control precision. Then, according to the equivalent mathematical model of the three-phase APF in the continuous domain, the parameters of the quasi-PR control method are designed. Finally, the effectiveness of the proposed method is verified by simulation.

With more and more photovoltaics (PVs) integrated into distribution networks, their drastically fluctuating nature may lead to voltage limits violation. However, the existing studies only focus on capacitor bank dispatching, which cannot address the drastic fluctuation of PV outputs. To adapt to the fast and continuous variation of PVs outputs in the distribution networks, this paper proposes a joint optimization of capacitor banks (CBs) and soft open points (SOPs). A chance-constrained model is established including the nonlinear equations of SOPs. Then, the second-order conic is applied to SOPs equations for efficient and accurate solving of the model. Finally, a cluster satisfying the given confidence level is formed by a proposed heuristic method to improve calculation efficiency. The numerical simulations on distribution networks show the efficiency and accuracy of the proposed algorithm over the existing methods.

With the gradual depletion of traditional fossil energy and the increasingly prominent environmental problems caused by it, China has proposed carbon peaking and carbon neutrality goals to promote the transformation of the energy structure. Solar energy is one of the important renewable energy sources, and roof-distributed photovoltaic power generation technology has a broad market prospect. Given the efficiency analysis of the roof-distributed photovoltaic power generation system, this paper first establishes an analysis model of the roof-distributed photovoltaic power generation system, analyzes the factors affecting the power loss of each link in detail, and further clarifies the distribution characteristics of the power loss of the system through simulation. Finally, some suggestions are put forward for the efficiency promotion of the roof-distributed photovoltaic power generation system.

Ultrasonic ranging by using the time difference method generally has the problems of inaccurate measurement of echo arrival time and small range. To solve the above problems, firstly, this paper proposes a method to detect the arrival time of the echo by using the inflection point of the echo envelope with the combination of logarithmic characteristics of the logarithmic amplifier and improves the peak detection of the traditional TDOA by changing the characteristic point of the arrival time of the echo. Secondly, an ultrasonic distance measuring device is designed, in which the transmitting power and the sensitivity of the receiving end can be adjusted automatically with the echo signal strength to improve the measurement accuracy and broaden the measuring range. The experimental results show that the range of the device is expanded from 0.5-12 m to 0.3-20 m, and the distance measurement error is reduced from 1% to 0.5% when the improved time difference method is adopted and the self-tuning of the transmitting and receiving parameters is enabled. It shows that the improved time difference method can improve the measurement accuracy and can effectively expand the ultrasonic ranging range by using the self-tuning of the transmitting and receiving parameters.

With the wide application of distributed generation technology, in order to maintain the stable and safe operation of the distribution network, this paper considers the uncertainty of power generation and a load of distributed energy storage. It takes the distribution network with distributed energy storage as the research object, models and analyzes the optimization problem, and studies the problem of DG configuration by using the reinforcement learning method. In the case of distributed grid connection, the voltage distribution change of the distribution network and the impact of network loss are analyzed. The line power flow of the distribution network is calculated by using the MATLAB platform as a platform, and the power flow calculation of the distribution network is simulated and analyzed by the MATLAB platform. The system loss is significantly reduced. The experimental results show that the system loss variance is 29.19. After adding energy storage, the loss variance is 14.77, which is reduced by 49.41%. According to the scheme, the voltage stability and operating loss of the system can be effectively improved.

Photovoltaic (PV) is becoming popular in many countries. However, due to the influence of weather, the PV output power often fluctuates greatly in a short time, and its intermittence makes it difficult to meet the load demand. The Hybrid energy storage system (HESS) can smooth the PV power fluctuation and optimize the operation of the whole system. Therefore, this paper proposes a capacity configuration strategy for the HESS composed of a battery and super capacitor. The strategy aims to minimize the construction and operation costs. It contains a two-layer optimization model. The upper layer configures the capacity and output power of the HESS, and the lower model optimizes the operation of the PV-HESS system and solves the model through deep reinforcement learning. Finally, a PV demonstration project in Shandong Province, China, is taken as an example to verify the effectiveness of the HESS capacity configuration strategy.

The operation status of the transformer in the substation is changeable, and the evaluation index can not be determined, which leads to an increase in evaluation error. The evaluation method of the electronic transformer based on a high-dimensional random matrix is studied and analyzed. A comprehensive evaluation of the needs and standards and analysis of the evaluation characteristics are targeted to clear the evaluation index and calculate the threshold, build a high-dimensional random matrix state assessment model, and use the wide-area measurement processing method to achieve state assessment. The test results show that the final evaluation error of the method is only 0.82, which is well controlled below 1. It indicates that in the process of practical application, the evaluation speed is fast, the accuracy is high, and the links are simplified, which has practical application value.

This paper presents a control strategy of inter-arm voltage balance for a modular multilevel matrix converter (M3C) based on injected circulating current. The proposed method is divided into two parts, balancing the inter-arm voltage through the current control loop and the voltage control loop respectively. The strategy improves the current performance of the grid on both sides by enforcing the injected power to flow only within the nine arms, which is simple to control and suitable for various working conditions including stable operation and faults. Comparative simulation results obtained using Matlab/Simulink prove that the proposed control strategy can significantly reduce the inter-arm voltage imbalance, which demonstrates the performance and effectiveness of the strategy.

With more and more distributed generators integrated into the distribution networks, the uncertain outputs of distributed generators make the reliability evaluations of distribution networks harder to be handled. However, the existing studies such as the analytical method and simulation method cannot take into account the accuracy and calculation efficiency at the same time in a real time operation. So this paper proposes a reliability evaluation method for distribution networks considering the randomness of WT outputs. Firstly, based on the probability level of the WT outputs in different scenarios by prediction error of wind speed, the island division method under multi-scenario conditions is proposed. Then, the reliability of the distribution network with WTs is evaluated by the improved minimal path method. Finally, numerical simulations on the IEEE-RBTS 6 bus system show the superiority of the proposed algorithm over existing methods.

At present, there is a problem of high active line network loss in the distribution network expansion planning method for distributed power supply access, and a distributed power supply access distribution network expansion planning method that accounts for uncertainty is designed to improve this phenomenon. According to the coverage of the electricity consumers' sites, the available supply capacity of the distributed power sources is evaluated, the wind speed is discounted by the hub height of the wind turbines, the uncertainty is taken into account to build a correlation model, the number of nodes in the grid is fully considered, the grid operation characteristics are extracted, the voltage of the nodes along the line is simplified and the distribution network expansion planning model is optimized. Test results: The method designed here has the lowest average active line network loss of 112.58 kW, which is 10.83 kW, 10.3 kW and 12.85 kW less than the other three methods respectively, indicating that the method designed here performs better with full consideration of the uncertainties.

Domain walls (DWs) as waveguides of spin waves (SWs) have been used to study SWs propagation properties in ferromagnetic thin films. In this work, we present a spin-wave (SW) directional coupler based on two Bloch-type DWs for the first time with micromagnetic simulations. The distance between the DWs is 40 nm, and the magnetization orientation is is parallel at the central positions of the two magnetic DWs. Under the dipolar interaction, the lowest-width SW mode is split into symmetric and antisymmetric modes, and the SW coupling length L is characterized to be 469nm.

As the renewable energy installation incerase, self-consistent integrated energy system (IES) is becoming more and more important for power grid. However, the current optimal scheduling strategy of IES lacks of robustness. At the same time, few people have studied the influence of national policies on the optimal dispatch of IES. To solve these problems, an optimal dispatch is presented for IES with the tradable green certificate (TGC) considering PV prediction error based on distributionally robust optimization (DRO). The practicality of the proposed model is verified in the typical IES. Based on the experimental results, the proposed strategy can improve the robustness of the optimal dispatch and improve renewable energy consumption.

Reversible solid oxide fuel cells (RSOC) can realize energy storage and power generation in a compact structure, and their high-temperature operation characteristics provide the possibility for combined heat and power generation. Therefore, RSOC can be used as the node of the multi-energy flow intersection of the integrated energy system. The flexible operation mode of RSOC will greatly reduce the operation cost. However, the optimal operation of an integrated energy system including RSOC is very rare at present, so this paper establishes an integrated energy system of the electric gas network including RSOC. On this basis, this paper constructs an RSOC model, a DC power flow model and a linearized gas power flow model considering multiple operating states. Then, based on GAMS, the economic cost of a 24-20 electric gas composite network is optimized. After verifying the reliability of the model, the optimization results considering two working conditions and four working conditions are compared. It is concluded that the model considering multiple operating states of RSOC will reduce the operating cost of the system by 24%.

The characteristics of small volume, modularity, and easy expansion in solid-state transformers (SST), which makes SST widely applied in electric vehicle swapping stations. However, due to various loads and pulsed loads, voltage instability and fluctuation problems inevitably occur in electric vehicle swapping stations. Aiming at these problems, a topology structure based on a Modular Multilevel Solid State Transformer (MMC-SST) is proposed. It is the first choice for realizing the flexible power allocation of AC/DC hybrid system and the interconnection of medium and low voltage. The performance of the DC bus voltage of the charging station has been improved. Based on this topology, an energy management strategy with virtual synchronous generator (VSG) control is proposed to smoothly transition the on-grid and off-grid processes of the distributed power source. In addition, due to the frequency regulation is applied in the control system, the energy storage system has damping and inertia when the output of the new energy power generation equipment changes or load fluctuation, reducing the impact on the load energy storage equipment and improving the stability and safety of the electric vehicle charging station system. Finally, the effectiveness of the proposed topology and its control strategy in different working scenarios are verified by simulation. Simulation analysis shows that the proposed topology has good advantages in DC bus voltage stability, optimization of electric vehicle charging characteristics and load management.

This paper analyzes the frequency spectrum of the sound and vibration signals during the operation of the transformer, extracts various sound characteristics for fault classification, accurately carries out real-time early warning of the operation state of the transformer, reduces the occurrence of various power consumption faults and disasters, and improves the "intelligent" means of transformer management.

3D object detection is widely used in the field of autonomous driving. The embedded devices have few resources and low power consumption, so the existing 3D object detection algorithms are difficult to complete the detection task in the required time under some conditions. In this paper, the Complex-YOLOv4-Trans model is proposed to use point cloud data for 3D object detection. Firstly, a transformer encoder block is introduced to improve the structure of Backbone, which makes full use of its context information to improve detection accuracy. Secondly, the general convolution in Backbone is replaced with a depth-wise separable convolution, which effectively reduces the computational load of the model. Finally, Complete-IoU (CIoU) is used as the loss function, and then 8-bit model quantization is performed to speed up the inference. We select the KITTI dataset and deploy the model on NVIDIA Jetson Xavier NX by using TensorRT acceleration tools to test the methods. The mAP of Bird's Eye View for Car is 71.79%, the mAP for 3D Detection is 15.82%, and the FPS on the NX device is 42 frames. Experimental results show that our Complex-YOLOv4-Trans can perform real-time 3D object detection tasks on low-energy embedded devices.

Aiming at the problems of low efficiency, poor spatial location and incomplete attribute information in current transmission line modeling methods, a refined modeling method for transmission lines based on full area feature weighted template matching is proposed. First, collect the point cloud of the scary line, and extract the point cloud data of the tower and power line; Secondly, statistical outlier filter is used to denoise point cloud data; Finally, the GIM model of the transmission line is constructed by using the whole region feature weighted template matching method, which realizes the integrity and lightweight of the geometric, appearance and semantic information of the three-dimensional model of the transmission line, improves the efficiency of the transmission line modeling, and is verified by taking a 500kV transmission line as an example.

A multichannel high-speed data acquisition system based on improved SPI communication is designed to meet the requirements of synchronous acquisition of current and voltage parameters by master and slave control boards. The main architecture of this system is a master-slave FPGA board and AD acquisition card, and the program is developed based on the Vivado platform. Based on traditional SPI communication, this paper puts forward an improved SPI communication. By increasing the number of MISO data lines, the data transmission rate between master and slave control boards is greatly increased, and the FPGA expansion port resources are fully utilized. In addition, a data conversion module is added to the system, which can observe the acquisition results more intuitively. At the same time as high-speed data acquisition, the measurement accuracy within the specified voltage input range is high. The experimental results show that the system can realize multichannel high-speed data acquisition.

Aiming at the disconnection problem of power overhead ground wire caused by wind vibration or the action of power frequency short-circuit current, the newly revised "Eighteen major anti-accident measures of State Grid Corporation China" proposes that independent double string structure should be adopted in suspension clamp of overhead ground wire. Based on this, a research scheme of backup protective hardware for the suspension clamp of the overhead ground wire of the power transmission line is proposed. In this scheme, the backup protective hardware for the suspension clamp is designed and added on the premise that the original overhead ground wire-suspension clamp structure is not changed. It provides backup measures for the original suspension clamp and reduces suspension clamp heating by an electrical shunt. Finally, the mechanical failure load test and salt spray test are performed on the designed production. The results show that the designed production meets the standard and could be used in actual engineering. At the same time, the practical engineering application shows that the backup protective hardware can effectively reinforce the ground wire suspension clamp with double strings, avoiding the problem of dropping and breaking after the original clamp is broken.

Aiming at the needs of ionospheric detection and application development, this paper mainly introduced the current development and analyzed the advantages and disadvantages of ionospheric detection technology from ground-based and space-based instruments. Meanwhile, the hot topics of ionospheric application were introduced from the three aspects of ionospheric irregularity, earthquake-ionospheric effect, and ionospheric heating, respectively. Finally, the development trend of ionospheric detection technology and its application were discussed from five aspects, and some suggestions for the future comprehensive research fields of ionospheric detection technology in China were also given.

Due to the inability to accurately gain the changes of transformer archives, there are often problems such as confusion and loss of control of the station area archives in the electricity consumption information collection system, which seriously affects the work of line loss management and fault operation and maintenance, and it is difficult to achieve refined management. Existing manual identification methods are time-consuming and labor-intensive, while the dedicated device identification method is costly and requires a device-independent identification technology. Aiming at the above problems, a data analysis method based on temporal and spatial correlation is studied to analyze the temporal and spatial correlation of the voltage data of the power collection equipment. On this basis, use the classification method in data mining to divide collection equipment of each station area into one category, and calculate the probability that the acquisition equipment to be identified belongs to each category, to determine the belonging relationship of its station area. By simulating the user situation of the real station area, the simulation verifies the effectiveness of the intelligent station area identification method based on the spatial and temporal correlation of data, which can not only ensure the accuracy and efficiency of station area identification, but also does not depend on equipment and labor, and can meet the application requirements.

Radar-embedded communication (REC) is a low probability of intercept (LPI) communication method that enables radar and communication to share the spectrum. Covert communication is accomplished by embedding low-power communication waveforms in high-power radar backscatter echoes. This research, by optimizing the eigenvalue matrix power exponent a of the shaped dominant projection (SDP) waveform, proposes an SDP waveform with variable eigenvalue matrix power exponent, namely SDP-a waveform. Then, the reliability of waveform communication and LPI performance are theoretically analyzed by processing gain. Finally, the simulation experiments are carried out with SDP-0.25, SDP-0.5, and SDP-0.75 waveforms as examples. The experimental results are consistent with the theoretical analysis results, indicating that the optimization of the eigenvalue matrix power exponent can meet the performance requirements of different aspects.

The airborne non-contact electroscope developed in this paper does not need to touch the tower and can be remotely controlled to complete the electroscope. It can be widely used in the maintenance operation of various voltage levels, and the entire operation process is in line with the State Grid Corporation of China Electric Power Safe work regulations (power line part) regulations. The application of the device can effectively reduce the labor intensity of the operators, improve the operation efficiency, and ensure the safety of the operation.

In order to improve the correctness of the judgment of transmission line tripping and the response efficiency of monitoring, data mining technology is introduced to carry out the research of real-time monitoring platforms for transmission line tripping. The required hardware of the monitoring platform includes an electromagnetic induction sensor, a clock chip, memory and the like. A hardware framework structure of the real-time monitoring platform is constructed on the basis of the hardware. Compressed sensing is carried out on a transmission line trip monitoring signal by using a data mining technology; and finally, real-time monitoring is carried out on the operation of the transmission line, and an alarm is carried out according to the relative importance of the trip. The practical application performance of the new monitoring platform is verified by comparative experiments. The experimental test shows that the proposed platform can ensure the correctness of monitoring and has faster monitoring response efficiency.

Accurate prediction of geological subsidence is of great importance for geological hazard risk assessment. Various existing prediction models do not take into account the time correlation between geological subsidence, and the prediction effect lacks practical significance. In this paper, an LSTM-AMSGD-based land subsidence prediction method is proposed. Firstly, the high-precision time series inversion results of large-area land surface deformation are obtained by the small baseline interference technique with multiple principal image coherent targets. Secondly, a recurrent neural network (LSTM-AMSGD) is used as the network architecture. The final cumulative subsidence prediction error is within 0.3 mm, and the single-step prediction of more than 400,000 observation points can be completed in 126s. Therefore, the LSTM-AMSGD model in this paper is effective for the prediction of geological subsidence.

The grid communication data gateway filtering system has the problem of excessive memory overhead, so a grid communication data gateway filtering system based on micro-service architecture is designed. For the hardware part, the system can support mounting a U disk with a capacity of 16 G and above and use NAND FLASH to realize the expansion of solidified code and storage space. For the software part, the system can get the gateway communication parameters, calculate the bit rate of grid communication data transmission, and match a specific duty cycle. Besides, it can set the existing network port authentication mode according to the instructions, start the gateway filtering program, design the power grid communication data reliability judgment rule base, and optimize the filtering mode based on the micro-service architecture. The test results are that under the conditions of a different number of sensitive words, the average memory overhead of the designed system is 75.93 MB, indicating that the system has better overhead performance under the influence of the microservice architecture.

Under the background of accelerating the process of power grid construction, the unified spatial-temporal data model of power grid resources has become a necessary means to describe the relationship between spatial objects and power grid data. Affected by the defect of the information island, some unified spatiotemporal data models of power grid resources have poor updating performance. Therefore, a unified spatiotemporal data model of power grid resources based on microservice architecture is designed. The architecture can obtain the spatial structure elements of the power grid area, identify the spatial correlation characteristics of the modeling object through the distribution of power energy supply lines, eliminate the dimension of meteorological data variables, design a unified resource scheduling scheme based on the microservice architecture, calculate the space-time weight matrix, and build a space-time data model. Test results are that under the two update task scenarios, the average update performance of the unified spatiotemporal data model of power grid resources based on the Internet of things is 11363 times/second. The average update performance of the unified spatiotemporal data model of power grid resources based on the Internet of things is 9958 times/second). And the average update performance of the unified spatiotemporal data model of power grid resources based on the genetic algorithm is 9771 times/second. It shows that the designed unified spatiotemporal data model of power grid resources is perfect after combining the microservice architecture.

In the face of increasingly complex application conditions, some distributed adaptive control models for microgrids suffer from large differences between voltage magnitudes and rated values. To address such shortcomings, a distributed adaptive control model for microgrids is designed for a microservice architecture. The mathematical expressions of the state integral control variables and control input variables under steady-state conditions are derived by extracting the integral control variables of the distributed generation units of the microgrid, and the current at the end of the line short circuit is used as the database to design the current quick-break protection mechanism for the microservice architecture. Experimental results is that the voltage magnitude of the distributed adaptive control model of the microgrid in the paper is 317.71V, which is closer to the rated output voltage value of 320V, indicating that the distributed adaptive control model of the microgrid is better when the microservice architecture is fully utilized.

In recent years, the traditional ICP algorithm has high requirements for the initial position of point clouds, and low registration ability for point clouds with low overlap. The Microsoft Kinect depth sensor was used to obtain the point cloud data of the target object from the real scene. Then, pre-processing such as point cloud segmentation, filtering and down sampling. In the coarse registration, the feature point sampling consistency algorithm was used to make the point cloud obtain a better initial position. Finally, a point-to-surface ICP algorithm optimized by linear least squares was proposed in the fine registration. The experimental results show that the root mean square error of the improved algorithm is 0.761mm and the time is 52.32ms. Compared with the ICP algorithm based on SIFT feature points and the improved ICP algorithm based on feature point sampling consistency, the registration accuracy of the improved algorithm is increased by 21.0% and 43.3%, and the speed is increased by 18.9% and 30.2%.

The normal operation of transmission lines is one of the basic guarantees for industrial development. The foreign object on the transmission line is one of the main reasons for the failure of the transmission line, which brings huge economic losses to the society. The traditional method has the problem of low accuracy and slow speed. The detection and identification of foreign objects on transmission lines are beneficial to the maintenance of huge economic benefits. Therefore, cloud-edge technology is introduced into this study, and a monitoring model for foreign objects on transmission lines in complex scenarios based on an improved YOLOv4 network is proposed. The feature extraction module of the YOLOv4 model is improved based on the GhostNet network to speed up the detection of foreign objects. The SPP module in the YOLOv4 network is improved to enhance the accuracy of detection and identification of foreign objects on transmission lines. The accuracy and speed of the detection of foreign objects tested are 99.2% and 218ms, respectively. The experimental results can meet the needs of real-time detection.

Fine-grained vehicle classification is a hot research topic in the field of computer vision, which aims to recognize different models of vehicles belonging to the same brand. Limited by the large appearance differences of the same vehicle (due to different attitudes and perspectives) and small appearance differences between different vehicles, the vehicle's reorganization performance is not high and can't meet the real application requirements. In this paper, a Fine-grained Vehicle Classification is proposed based on Feature Augmentation (FAFCC), which focuses on mining the key discriminative parts of the object. Specifically, the FAFCC first improves the weakly supervised data augmentation network (WSDAN) by using multi-layer feature extraction and fusion instead of single-layer features. Further, the FAFCC assigns local feature weights to select key features, which can solve the problem that the discriminant features in the channel are not prominent enough and realize the high-quality compact fine-grained vehicle recognition task. The performance of the FAFCC on the fine-grained image classification tasks for vehicles has been evaluated through algorithm comparison experiments and ablation experiments. Through visual analysis, it can be seen that the introduction of each new module has optimized the loss function value and convergence speed of the model. In general, our FAFCC algorithm has a certain improvement effect compared with the original algorithm.

This paper propses an infrared small target detection method in complex dynamic backgrounds in sequence images by using image registration and pipeline filter. For dynamic scenes caused by camera movements, registration of image frames and reference frame images is conducted by using the feature point matching algorithm. Next, the frame difference method is used to detect small mobile target in video image sequences. Finally, the impact of ground clutter noise and random noise are eliminated by using a pipeline filter to precisely track the trajectory of small moving targets. The experiment results show that the proposed method is superior to the classic infrared small target detection algorithm in terms of accuracy and stability in complex ground backgrounds.

The rapid economic growth has promoted the development of China's electric power industry, which has gradually expanded the demand scale of electric power materials. Only by accurately forecasting the demand situation of electric power resources can the electric power field develop in a better direction. Based on this, this paper expounds the current situation of power material demand forecasting, then introduces the basic introduction of Prophet algorithm, and then puts forward the power material demand forecasting strategy based on Prophet algorithm, and verifies the application effect of this forecasting strategy through experimental analysis. Through the experimental analysis, it can be seen that the prediction strategy can get very accurate results, and has high practicability, so it can be applied to practical work.

The grid structure of the modern power system is complex and the space span is large. When the power system is disturbed to generate unbalanced power, the transient distribution process among the various synchronous units leads to the spatiotemporal distribution characteristics of the frequency response of the system. By measuring the generator Frequency and Inertia, the inertia center frequency can be calculated. Based on the two-generator system, the relationship between generator frequency and bus frequency is analyzed. A method for selecting the frequency inertia center node of the local power grid in the power system is proposed, and the inertia center frequency of the system is estimated based on the measured frequency at this point. Taking 10 machines and 39 nodes as an example, the simulation calculation is carried out to verify the effectiveness of the proposed method.

The permanent magnet motor will inevitably produce cogging torque because of its inherent structure, which will cause vibration and noise and affect its application in certain high-precision applications. Firstly, considering the actual cogging structure of the stator, a general analytical model of cogging torque of the permanent magnet motor is obtained by the energy-based approaches. On the basis of the further derivation of cogging torque analytical expressions with different segmented pole structures, a cogging torque combination reduction method is presented based on the particle swarm optimization algorithm, by comprehensively optimizing three different combinations of the opening width of stator slot, the calculating pole arc coefficient and the number of PM pole segments, the method can reduce the "peak-to-peak value" of cogging torque. The finite element analysis (FEA) results prove the validity of the proposed method.

Load-balancing is a key component of highly available network infrastructure. Weighted round-robin is one of the important strategies to achieve load-balancing, which is a classic research subject. In recent years, the weighted round-robin algorithm has developed from static weight to dynamic. The Dynamic weighted round-robin algorithm needs further research in time-sensitive of dynamic weights and the rationality of results. Based on Nginx, this paper improves the dynamic weighting algorithm. Firstly, it proposes a method of dynamically calculating weights. The method determines the weight of the server in the cluster according to its theoretical performance and runtime state. Secondly, making the request allocation result more balanced and avoiding continuous centralized scheduling by an improved weighted algorithm. Experiments show that the algorithm proposed in this paper is effective and can improve system performance and load-balancing efficiency.

Large-scale integration of variable renewable energy and power electronic device has raised great changes in the physical characteristics of power systems. It is urgent to strengthen system weaknesses in system evolution study. To fill the gap, a generation expansion planning model considering weakness identification and reinforcement is proposed. An efficient outer-inner loop algorithm is designed based on the structure of the model. An IEEE 6-bus test case is carried out to show the effectiveness of the proposed model and methodology. The test case reveals that weakness reinforcement schemes are robust against the worst-case contingency and the unserved demand can be reduced to 20.6% after the weakness reinforcement process.

Traditional centralized Internet of Things (IoT) management cannot effectively defend against internal attacks from compromised devices due to its own limitations, while distributed IoT node management has certain requirements for the communication environment and resource consumption of the nodes themselves. And the trust authentication of the nodes cannot be easily resolved in a distributed manner. This paper presents a blockchain-based trust management solution. Trust management of nodes based on blockchain also greatly reduces resource consumption. Specifically, the trust value is derived by weighting the historical trust value and the direct trust value, so that the evaluation node does not rely solely on historical trust data to derive its own independent trust evaluation. A separate system node level mechanism exists in the scheme, and the system increases the system node level trust value for the node when this evaluation reaches a trusted level. If this evaluation is malicious, the system node-level trust value for the evaluation is initialized. Security analysis and experiments have shown that this scheme can effectively detect malicious nodes and takes less time.

Transformer oil is an indispensable part of the transformer. The performance of transformer oil largely determines the working condition of the transformer. Therefore, it is necessary to realize accurate and rapid detection of the physical and chemical properties of transformer oil. To realize the detection of transformer oil, firstly, the ultrasonic wave with multiple frequencies is transmitted through the transformer oil, and the physical data such as penetration velocity and attenuation coefficient in the transformer oil are detected. The parameters of the wavelet neural network were optimized using the sparrow search algorithm, and the feasibility of the model was detected using the transformer oil experimental data.

With the development of new energy technology, the installation of new energy is becoming more and more. After a large number of new energy sources are connected to the power system, the capacity of peak regulation and frequency modulation is gradually insufficient, resulting in poor stability of the power system. Aiming at the problem of poor operation stability after new energy is connected to power system, a power system optimization scheduling method based on ant colony algorithm is proposed. In the model, the objective function is to minimize the total operating cost. The improved ant colony algorithm is used to solve the optimal scheduling model, and the effectiveness of the method is verified by simulation. The results show that this method has better ability of peak regulation and frequency modulation after new energy is added to the power system network, the frequency value and active power value of the power system are reduced, the load fluctuation value of the grid is reduced obviously, and the operation of the power system is more stable.

The conventional icing detection method is mainly based on the manual detection of icing status. The texture correlation characteristics of icing are variables, which lead to large errors in the icing edge detection and affect the subsequent maintenance of transmission lines. Therefore, the transmission line icing detection method based on machine vision is designed in this paper. The method comprises the following steps of extracting the icing texture feature of the transmission line, namely extracting the correlation feature of an icing image, identifying an icing area by using machine vision technology, carrying out edge detection on the transmission line and finally obtaining the icing thickness of the transmission line to realize the accurate detection on the icing of the power transmission line. The comparative experimental results show that the detection error of this method is small, the detection accuracy is high, and it can be applied to real life.

Wheeled mobile robots have a wide range of applications in aerospace, military, industry, medical services, and even entertainment. It is simple in design and easy in fabrication and is widely studied by scholars at home and abroad. However, the path-tracking problem of wheeled mobile robots has also been a key problem inhibiting their development. In this design, the kinematic model of the robot is constructed by using P and feedforward, and a double closed-loop P and feedforward control system is designed. After a trial-and-error method, the appropriate control parameters are obtained. The simul ink function in MATLAB is used to simulate and verify the path-following ability of the method.

Electricity-hydrogen coupled DC microgrid is of great significance for energy structure adjustment and efficient consumption of renewable energy. However, at present, such systems lack high-precision simulation support, and the transient simulation technology for large-scale microgrid systems is not perfect. In order to solve these problems, this paper form an electricity-hydrogen coupled DC microgrid simulation platform, and studies the function realization and the efficiency improvement of transient simulation. Firstly, the Euler algorithm is adopted to realize the transient simulation of the microgrid system for the functional requirements of the electricity-hydrogen coupled DC microgrid. Secondly, for different system sizes and computing platforms, an adaptive parallel computing optimal mode selection algorithm is proposed to improve the simulation efficiency. The experimental results show that the algorithm improves the computing efficiency of the large-scale electricity-hydrogen coupled DC microgrid system by about 40%.

Aiming at the problem that the current power cloud resource scheduling is inefficient and time-consuming, a large-scale power cloud resource scheduling algorithm in an edge computing environment is proposed. Firstly, the resource management method of large-scale power clouds is optimized. Considering the resource sharing and task scheduling security requirements between secondary nodes, and combined with edge computing technology, the resource distribution management and scheduling algorithm of the power cloud are optimized. Finally, the experiment proves that the resource scheduling time of a large-scale power cloud resource scheduling algorithm under an edge computing environment is reduced by more than 15 minutes in the actual application process, And the scheduling effect is also better than the traditional method.

The relational database uses distributed storage for grid over-voltage anomaly data, which lacks the division of the anomaly data, resulting in a long query time for anomaly data management. For this reason, the research of grid over-voltage anomaly data management based on the clustering algorithm is proposed. The clustering algorithm is combined with the outlier detection to divide the anomaly data and improve the query efficiency. The data are classified according to their characteristics. Row storage is selected as the main storage method for grid over-voltage anomaly data, and a three-dimensional model library is used to build out the management framework of the anomaly data to realize the efficient management of the anomaly data. In the experiment, the query time consumption of the proposed method is tested, and the analysis of the experimental results shows that the proposed method has a high query efficiency in managing the grid over-voltage anomaly data.

Human pose estimation has always been a popular research topic in the discipline of computer vision, which aims to predict the spatial position of the human body's key points (parts/joints) from a given image or video. Benefiting from the powerful feature representation ability of the convolutional neural networks, the pose estimation methods based on the deep learning have become the mainstream framework of this task, which obtain the human pose estimation results by extracting the human pose feature information at different scales with specific neural networks and using the corresponding methods to process it. This paper, based on the depth study of human body posture estimation can be divided into single-task and multi-person missions. The time and method of correlation are classified and summarized, respectively from the network architecture, algorithm and feature extraction of performance that are analyzed. The current problems existing in the research field and methods are summarized, and the research prospect for the future has been prospected.

When the particle swarm is active, the robustness of the system is quite great, which is very useful for solving ill-conditioned problems like picture reconstruction. In the reconstructed image, however, the large number of pixels results in the large dimension of particles, making it harder for particles to attain the ideal solution during the optimization process. A constraint condition is applied to the position of particles to solve this problem. The image reconstruction algorithm regularized by Tikhonov is used as the particle position reference to constrain the particles to search within a certain range of the algorithm, and the penalty function is used to solve the problem to improve the particle search speed. For the inertia weight of particle swarm, this paper uses linear decreasing weight to realize its adaptive dynamic adjustment, which improves the flexibility of the algorithm. When chaos operator is added to the particle swarm location search process, if the particle is trapped in a local optimum, the chaos variable will fluctuate within a certain range, thus reducing the error rate of the optimal solution. The experimental comparison results illustrate that the modified particle swarm optimization technique for the image reconstruction of Electrical Capacitance Tomography outperforms the classic LBP and Tikhonov algorithms in terms of accuracy and efficiency.

The refinement of existing CNN-based Super-resolution Reconstruction (SR) networks mainly focuses on deeper network architecture, which hinders the transmission of information in the networks. A deeper network is unable to make full use of intermediate correlation features and makes the training of the network difficult. To solve these problems, we propose a multi-scale channel attention residual network (MCAR). Specifically, we propose a multi-scale channel attention fusion module (MCAF) to learn local and global channels feature and capture the long-range dependencies. Furthermore, the multi-scale block is adopted to get the different scale feature representations. The experimental results on four benchmark datasets demonstrate that our models can effectively improve the visual effect of images, and outperform most of the advanced SISR methods in PSNR and SSIM.