Abstract:The non?technical losses caused by electricity theft in the power system have always been a pressing issue for power grid companies to urgently address. With the deployment of a large number of smart meters in the power grid, the use of user?side data collected by the power metering automation system to accurately detect electricity theft has attracted widespread attention from researchers and power grid companies. Firstly, the basic classification of users' electricity stealing behavior, evaluation indicators and existing electricity theft detection data sets are introduced. Then, from the four aspects of grid state analysis, machine learning, game theory and hardware, the existing detection methods of electricity theft behavior are comprehensively sorted, analyzed and compared, and the basic ideas, advantages and disadvantages of each method are summarized. Finally, the current challenges in the field of electricity theft behavior detection are deeply analyzed, and a prospective outlook on the focus of future research work is provided.

Abstract:Virtual synchronous machine provides inertia and damping support for power systems, but at the same time, it will bring the similar problems of frequency and power oscillation as traditional synchronous generators. The oscillation of frequency is accompanied by the oscillation of active power. Most of existing virtual synchronous machine control methods focus on improving the system frequency stability, ignoring the power oscillation. Therefore, an optimal control strategy of inertia damping parameters of virtual synchronous machine is proposed. Taking the minimum sum of squares of frequency deviation and active power deviation as the objective function, the genetic algorithm is used to explore the minimum value of the newly constructed objective function. This optimization method is then verified by a specific test model. Based on this time?domain simulation model in PSCAD, the proposed method can therefore be validated. The capability to balance the frequency oscillation and output active power oscillationwill make the system frequency oscillation and power oscillation enhanced.

Abstract:The problem of over?limit short?circuit current in power grid containing renewable energy is becoming increasingly serious. Because of its faster and larger state changes, offline over?limit short?circuit current analysis may not be able to cover all the over?limit scenarios. Therefore, online analysis is quite necessary. Considering that the mainstream physical model calculation method can hardly meet the online calculation speed demand, faster calculation is of great significance. Therefore, this paper proposes a strategy of combining data?driven and model?driven method for over?limit short?circuit current evaluation for power grid with high penetration of renewable energy. Firstly, based on the analysis of the main factors affecting the short?circuit current, in order to improve the calculation speed, the original dimension is reduced to consider only the influence of load. Then, the optimal power flow and random simulation methods are combined to generate a large set of samples, and the data?driven model is obtained through machine learning algorithm training. On this basis, the error analysis and threshold setting of the model are carried out by using false positive rate and false negative rate. Then, the data?driven model is used to screen over?limit short?circuit current scenarios; Finally, the theoretical physical model proposed in the latest research is used to verify the short?circuit current scenario after preliminary screening with high accuracy. It is verified on the IEEE 39 bus model with photovoltaic power supply. The simulation results show that this strategy can effectively improve the verification speed without omitting the over?limit short?circuit current scenarios.

Abstract:At present, in view of the stable operation of the VSC?HVDC transmission system under grid voltage fluctuation, the on?load tap?changer(OLTP) is generally adjusted to ensure the constant no?load voltage on the valve side of the transformer. But the method does not take full use of the OLTP, leading to a relatively higher converter loss during normal operation. In this paper, a novel OLTP regulation strategy to effectively reduce converter losses is proposed. Firstly, the principle of the proposed OLTP regulation strategy is analyzed based on the operation characteristics of the VSC?HVDC system. The maximum modulation ratio limit and the maximum continuous operating voltage limit of transformer valve side are used to comprehensively determine the upper limit of no?load transformer valve side voltage, and then the OLTP position is calculated according to the actual grid side voltage. Secondly, for a specific example, the reactive power requirements under different AC voltages are analyzed, and then the OLTP requirements under different working conditions are calculated according to the proposed strategy. The converter losses of the novel regulation strategy and the traditional strategy under various transmission powers are calculated and compared, and the losses data show that novel regulation strategy could effectively reduce converter losses. Finally, a PSCAD simulation model of a typical offshore wind power VSC?HVDC transmission system is established, and the stability and effectiveness of the proposed novel OLTP regulation strategy are verified under two key conditions of AC voltage fluctuation and change in the number of operating transformers.

Abstract:Aiming at the situation that some distribution networks can not collect the voltage phase angle information of all nodes, the visualization of the topology structure and line parameters of the distribution network is realized by mapping the physical system in a digital way through the analysis of the power and voltage data that can be obtained by the existing measurement equipment of the distribution network. To reach this goal, firstly, based on the measured data under multiple groups of time sections, a linear regression topology and line parameter identification model is established according to the power flow equation. The admittance parameters in the model are solved by the least square method. Considering the uncontrollable factors in the actual data acquisition process resulting in the loss of some data, the minimum variance is used to fill the missing data to ensure the integrity of the information. Secondly, a modified model is established, and the initial values of topology and line parameters are iteratively modified by the improved Newton?Raphson method to adjust the accuracy value of the topology structure and line parameters. Finally, the applicability of the approach is demonstrated by IEEE 33 node example.

Abstract:As an important part of DC microgrid system, the energy storage unit is related to reasonable powerdistribution and continuous stability of bus voltage during charge and discharge process. However, for DC microgrid systems containing multiple distributed energy storage, the imbalance of SOC will inevitably reduce the availability of energy storage systems.In order to ensure the normal and stable operation of the system and realize the fast equalization control of the SOC of the energy storage unit under different working conditions, an improved droop control strategy is proposed by introducing line impedance compensation feedforward link and dynamic speed regulation factor to eliminate the impact of inconsistent initial states of the distributed energy storage units and different line impedance. After that, Matlab/Simulink was used to build a simulation platform experiment to verify the feasibility and effectiveness of the proposed control strategy under different charging and discharging conditions. Finally, it was concluded that compared with the original control strategy, the dynamic speed regulating factor control strategy proposed in this paper can effectively accelerate the energystorage balancing speed under various working conditions, and maintain the normal and stable operation of the microgrid system.

Abstract:In order to solve the inconsistency of the traveling wave waveforms between primary and secondary sides in the power grid caused by the decay and noise interference in transmission process of the traveling wave sensor, an accurate detection method of fault voltage traveling wave combining the truncated singular value decomposition (TSVD) regularization and discrete deconvolution is proposed. This method adopts the transfer function model based on lumped parameters to construct a forward model of voltage traveling wave sensor, and the principle of deconvolution is used to establish the inversion model of traveling waves. Aiming at the ill conditions generated during the inversion of traveling wave waveforms, a waveform inversion method combined with TSVD regularization theory is proposed to realize the accurate detection of fault voltage traveling wave. The simulation shows that the detection method can realize the accurate inversion of the secondary traveling wave signals, and the waveform characteristics of the primary traveling wave signal obtained by the inversion can reflect the real fault waveform characteristics.

Abstract:Virtual synchronous generator (VSG) is a widely used grid?forming control strategy. However, due to the nonlinear power angle relationship caused by the analog of external characteristics of synchronous generators, VSG is similar to synchronous generators and is prone to lose synchronization with the power grid under severe disturbances. This paper analyzes the large signal power angle stability of grid?connected VSG, and an additional damping method to improve the power angle stability is proposed, the method is a theoretical analysis method combining the linearized model with the nonlinearized model. Firstly, the linearized model is used to qualitatively analyze the damping ratio and the rate of change of frequency (RoCoF), which shows that the additional damping method can improve the power angle stability without reducing the frequency stability. On this basis, the nonlinear model is used for quantitative analysis to accurately evaluate the power angle stability of VSG after a power grid fault. Theoretical analysis and simulation show that adding additional damping into the active power control loop can improve the power angle stability of VSG.

Abstract:In order to solve the problem of power quality deterioration of modular multilevel converter?based battery energy storage system (MMC?BESS) under the disturbance of background harmonic and dead time effect, a harmonic suppression strategy of MMC?BESS based on arm current control is proposed in this paper. To investigate the harmonic disturbance of the MMC?BESS in the medium voltage application scenario, firstly, the influence of AC background harmonic and dead time effect on the MMC?BESS is analyzed, and the mathematical model is established. Secondly, the decoupling models of AC current, DC arm circulating current and AC circulating current of the MMC?BESS are also established. On this basis, the harmonic suppression strategy of MMC?BESS based on arm current control is proposed, and the detailed controller parameter design process and performance analysis are given. Finally, the effectiveness and correction of the proposed harmonic suppression strategy are verified by simulation and experiment.

Abstract:To comprehensively investigate the intricate relationship between transformer winding deformation categories, deformation degrees and winding electrical quantities, and to extract more winding information from electrical parameters, an explicit function model employing the stochastic response surface method(SRSM) is established. This model integrates three?winding transformer electrical parameters and structural variables, facilitating a deeper understanding of their interplay, and act as objective function for the sensitivity analysis of transformer. A dual?layer global sensitivity analysis model based on the Morris?Sobol method is proposed to analyze the objective function and identify the deformation types most sensitively reflected by each electrical parameter. The Morris method excludes the impact of irrelevant structural variables, increasing the efficiency and accuracy of sensitivity analysis. The Sobol method is subsequently utilized to assess the sensitivity of electrical parameters to different deformation types. It is found that the sensitivity of capacitance parameters to winding radial deformation is much higher than that of inductance parameters. Therefore, the variation in winding capacitance is adopted as the criterion for detecting winding deformation. and the relationship between the change rate of winding test capacitance and equivalent deformation is established. This approach not only enhances understanding but also paves the way for effective detection of winding deformation based on electrical parameters.

Abstract:In order to improve the diagnostic accuracy of transformer fault, a transformer fault diagnosis method based on the multi?scale convolutional neural network model is proposed. Firstly, two multi?scale convolutional modules are designed on the basis of the 1DCNN structure, and the overall structure of the transformer fault identification model is constructed. Secondly, to handle the problem of less sample features, the feature expansion method based on the ratio method is adopted to enhance the sample features from 5 dimensions to 25 dimensions. To solve the small sample size of faults and uneven distribution of sample numbers between faults, a sample number enhancement method based on adversarial generation network is adopted, and a large number of simulated samples are generated. Finally, the modified dataset was used to train and test the designed model. The results show that the average accuracy of the model is 93.24%, and the model performs well compared with the relevant mainstream methods under different datasets.

Abstract:The high?performance permanent magnet motor system has become a key and core component of hard alloy production molding equipment. The mismatch of motor system parameters will seriously affect the overall efficiency of alloy product molding equipment. In order to mitigate the effects of model parameter mismatch and one beat delay on the current control performance of permanent magnet synchronous motor (PMSM), a robust deadbeat predictive current control (POC?DPCC) method with online parameter correction is proposed. Firstly, the parameter sensitivity of conventional deadbeat predictive current control is analyzed. Then, a multi?parameter error online identifier based on Adaline neural network is designed, and a new identification structure with parameter mismatch error as the neural network weight is proposed to improve the tracking performance of parameter changes. Finally, the POC?DPCC method is proposed to improve the robustness of the system to the motor parameters by updating the control voltage coefficient matrix, and the sampling current is replaced by the predicted current value at the next time to compensate the influence of the one?beat delay. The effectiveness and robustness of the proposed method under complex operating conditions are verified by comparing simulation and experimental results.

Abstract:In order to solve the problem of insufficient calculation accuracy in existing high?voltage XLPE cable thermal evaluation models under continuously fluctuating loads, this paper explores the traditional methods (including IEC 60853?2 and thermoelectric equivalent method) using constant insulation layer heat capacity distribution coefficient, and the accumulated calculation errors are investigated when subjected to continuously fluctuating loads. An optimized approach for the lumped thermal model of cable insulation is proposed to mitigate potential error accumulation. The proposed improved thermoelectric equivalent(TEE) method is validated through the dynamic cable temperature rise experiments. The analysis reveals that the improved TEE method can better predict the real thermal performance of cables under continuously fluctuating loads, with conductor temperature calculation errors being maintained within 2.5 ℃. The methodology developed in this paper paves a way for electricity utilities to enhance cable utilization while ensuring cable reliability.

Abstract:As wind power output is highly unstable due to the uncertain environmental disturbance such as wind speed, both the wind power supplier and the power retailer in the wind power supply chain face the risks of uncertain profits. In light of this, firstly, profit?sharing contracts are introduced under decentralized decision?making to optimize the decision?makings of wind power suppliers and electricity retailers. Subsequently, conditional value at risk (CVaR) is introduced as a measure of revenue level for the electricity retailer. A wind power supply chain model is built under revenue?sharing contracts based on CVaR. Finally, under the CVaR criterion, the uncertainty of the optimal contract quantity between wind power suppliers and electricity retailers towards wind power suppliers as well as the sensitivity of the optimal towards risk aversion coefficient of electricity retailers are analyzed. The case study analysis results demonstrate that the introduction of revenue?sharing contracts and risk aversion mechanisms can enhance the optimal contract quantity for both the wind power suppliers and electricity retailers, achieving optimal performance for the wind power supply chain under uncertain generation conditions.

Abstract:As one of the important components of heterogeneous energy, the distributed photovoltaic power will affect the safety and stability of the traditional distribution network, due to the randomness and the fluctuation of its output. With the increase of power generation capacity, the ability of distribution network to incorporate the distributed photovoltaic power becomes the main constraint. Therefore, under the premise of considering multiple operation targets, a multi?model learning method is constructed in this paper, which consists of a PV force change prediction model, a load distribution prediction model, and a distributed PV maximum capacity evaluation model. The impacts of photovoltaic access on the distribution networks during the whole process can be analyzed by the proposed model. The Elman neural network model is proposed to ensure the prediction accuracy of the photovoltaic power generation variations. The BP neural network model is established to consider the prediction accuracy as well as the prediction efficiency. The PSO model for the maximum capacity of distributed photovoltaic power generation is built, so as to realize the accurate evaluation of the limit of distribution network acceptance of distributed photovoltaic power. The empirical results show that the proposed method can ensure the safe and stable operation of the distribution network, and is also beneficial for the planning of the maximal capacity of distributed photovoltaic power generation in the distribution networks.

Abstract:The multi?circuit line configuration can effectively solve the problems in line reconstruction and construction amidst the increasing shortage of transmission corridors. The conductor spatial arrangement and phase sequence determination for long?distance transmission lines pose great challenges in the design and maintenance of overhead transmission systems. This paper utilizes the ATP?EMTP simulation software to build a model of 500 kV four?circuit transmission line on the same tower, and simulates the induced voltage and current values with different line lengths, tower spacings, vertical and horizontal inter?circuit gaps, phase sequence arrangements, and tower nominal heights. Employing a BP neural network optimized by genetic algorithm, this paper achieve to predict the induced voltage and current values under unknown conductor spatial arrangements and phase sequences. Subsequently, according to the relevant electromagnetic environment control criteria, the multi?objective particle swarm optimization algorithm is used to optimize the conductor layout and phase sequence arrangement for overhead transmission lines. This process yields a four?circuit conductor arrangement meeting the electromagnetic environment requirements, thus providing a reference for the selection of substation grounding switches.

Abstract:To ensure the global of the non?fossil energy proportion target, the renewable energy power absorptive safeguard mecha?nism of China has been officially implemented. In this case, the reasonable formulation of weight indicators for absorptive responsibility is of great significance in guiding the healthy and orderly development of renewable energy. To effectively evaluate the current energy absorptive capacity of the system and provide support for the formulation of absorptive respon?sibility weight indicators, a method for assessing the absorptive capacity of renewable energy power generation is proposed. This method constructs a system day?ahead scheduling model that includes wind and photovoltaic power, comprehensively considers the constraints of secure operation of the power system and the impact of seasonal factors, and separately calcu?lates the renewable energy absorptive capacity of the system during the transition season, summer, and winter. The calcula?tion examples validate that the proposed method can determine the maximum renewable energy absorptive ratio of the power system in different seasons and use this to estimate the maximum annual renewable energy absorptive level of the system.

Abstract:With the large?scale grid connection of new energy and participating in system frequency modulation through virtual synchronous generator (VSG), virtual inertia has become an important component of system inertia. Therefore, an equivalent inertia evaluation method of power system considering the virtual inertia of new energy is proposed. Firstly, a unified analytical model of new energy VSG equivalent virtual inertia and synchronous generator moment of inertia is established. Secondly, the controlled autoregressive model is used for dynamic modeling and the problem of inertia solution is transformed into the problem of parameter identification in the model. Then, using the active power frequency time series data, the parameters to be identified in the model are solved by adopting the adaptive recursive least square algorithm. The adaptive forgetting factor is introduced and dynamically adjusted to realize the continuous tracking of the system equivalent inertia under different working conditions. Finally, the simulation analysis is carried out on the IEEE?39 bus system. The experimental results verify the correctness of the theoretical analysis and the effectiveness of the identification method.

Abstract:In recent years, the wide?band harmonic resonance has become one of the most important issues that endanger the stability of the power system. In order to build wide?band harmonic resonance oscillation monitoring system, an optimal placement method of monitor device is proposed based on the power grid topology. Firstly, this method defines the criticality of comprehensive nodes from the perspective of the harmonic resonance observability. Then, under the premise of ensuring the overall observability of the power system in the event of N?1 contingencies on critical transmission lines, a 0?1 integer programming mathematical model is constructed for the goal of monitoring device configurations number minimization and the criticality maximization. The optimal placement scheme is obtained by using the improved binary particle swarm algorithm to solve the model. Finally, the reliability and economy of the proposed algorithm are verified through the simulation tests of the IEEE 14?bus system and the IEEE 30?bus system.

Abstract:The high frequency of extreme natural disasters may cause long?term power outages in the island distribution network. The flexible deployment of batteries at the required sites through trucks and other vehicles can help ensure the power supply of the island distribution network and reduce power outage losses. During the recovery process, extreme weather may affect the transfer speed of batteries between sites, resulting in non?integer hours of scheduling time between sites. In order to solve this problem, this paper proposes a new optimization strategy for load restoration of island distribution network. First, the impact of environmental factors on renewable energy generation and battery transfer time is analyzed. On this basis, a dual?state decision?making method for coordinating scheduling steps and non?integer?hour battery transfer time is proposed. In addition, based on the 1?hour scheduling step length, an optimal island distribution network load recovery model that integrates battery scheduling decisions and network reconfiguration decisions is proposed. Finally, the effectiveness of the proposed strategy is verified by a simulation example.

Abstract:Accurately predicting the internal hot spot temperature can improve the damp fault inspection effect of arrester effectively. An inversion detection method of hot spot temperature inside 500 kV arrester based on surface temperature and SVM (support vector machine) is proposed. Taking the surface temperature of arrester and wind speed as inputs, the inversion of hot spot temperature inside arrester is realized. In order to improve the prediction accuracy of the proposed model, the influences of the grid search (GS) and particle swarm optimization (PSO) algorithms on inversion accuracy are compared. The results show that the GS?SVM model is of good inversion performance. The maximum and minimum errors between the internal hot spot temperature obtained by the inversion method and the actual values are 4.00 ℃ and 0.01 ℃ respectively, which proves the effectiveness of the inversion model.

Abstract:Distributed energy individuals generally cannot directly participate in electricity market transactions or provide grid auxiliary services. This issue can be solved through distributed energy aggregation. This paper proposes a new distributed energy polymer optimization method based on multiple energy types, multiple trading markets, and multiple services. The optimization method can be divided into three stages: high?level, middle?level, and low?level optimization. First, in the high?level optimization, linearization techniques are used to overcome the non?convexity of the cross spatiotemporal scheduling problem of aggregates, and the problem is transformed into a mixed integer linear programming problem to obtain the day?ahead unit combination scheme. Based on the unit combination scheme, the middle?level optimization solves the economic dispatch problem by the second?order cone?convex relaxation of the optimal power flow equation to obtain the node voltage and power flow. Deviations from the middle?level optimization set point are then penalized in the low?level optimization, making the results of the low?level optimization get close to the system optimal operating point while having a shorter planning horizon. Finally, in a case study, the optimization method is applied to a multi?energy polymer model including electrolytic hydrogen production, wind power generation, photovoltaic power generation, etc. The results show the advantages of the proposed method in terms of grid ancillary service and aggregate flexibility enhancement. The operational benefits are maximized, which validate the effectiveness of the proposed method.

Abstract:A higher?precision air conditioning load model serves as a crucial foundation for developing and implementing effective air conditioning control strategies, which is conducive to reducing electricity consumption and saving power costs. Firstly, by analyzing the impact of building structure, indoor and outdoor environment, and meteorological factors, a grey?box model is constructed for predicting air conditioning loads.. This model consists of a third?order equivalent thermal parameter model and a second?order equivalent moisture resistance model. Subsequently, the optimization objective function is established by minimizing the error between the indoor temperature and humidity output from the model and the measured temperature and humidity. Then, a parameter identification method based on the particle swarm optimization (PSO) algorithm is proposed and employed to obtain the crucial parameters of the grey?box model. Experimental studies demonstrate that the identified equivalent thermal resistance and moisture resistance models accurately reflect the indoor temperature and humidity distribution and variation characteristics, thus possessing practical application value in predicting air conditioning loads.

Abstract:Aiming at the instability of low?voltage power line carrier communication(PLC) problem caused by many factors including line attenuation, load characteristics and so on, a neutral line coupling method of low?voltage PLC is proposed in this paper. Firstly, the topological structure of low?voltage distribution area is analyzed. Secondly, the principle of neutral line coupling method of low?voltage PLC is described, and the PLC channel models of neutral line coupling in this paper are established. Then the performance of neutral line coupling of PLC is simulated using Matlab software. Finally, the accuracy and application effect of neutral line coupling is evaluated with practical cases. The results show that the neutral line coupling method in this paper reduces the attenuation of PLC channel by more than 24.9 dB under the typical five branch heavy load line conditions; after on?site construction, the success rate of data acquisition is significantly improved. It has many advantages such as high safety and easy construction, and can be used as an effective operation and maintenance means for PLC in low?voltage distribution area.

Abstract:Aiming at the subjectivity and blindness of different device feature selection in current non?intrusive load identification, a non?intrusive load identification algorithm based on Fisher?SVM feature selection is proposed. Firstly, the original data of household?side current and voltage are extracted based on the high frequency sampling device. Fourier transform is used to decompose the original signal into active power, reactive power and harmonic time series. Secondly, the load waveform is divided into four stages and the transient characteristics of the load waveform are calculated. Then, by utilizing the Fisher?SVM algorithm for feature selection among different classifiers, the optimal subset of classification features is obtained. Additionally, the results are calibrated using the Sigmoid function for probability calibration. Finally, different classifiers are integrated based on Bayesian theory to achieve identification of different loads. The algorithm is tested on a dataset consisting of 831 actual users from three different distribution areas. The results show that the algorithm effectively exploits the uniqueness of different electrical load imprints, overcomes the blindness in feature selection, and increases the load identification ability.

Abstract:With the rapid development of renewable energy represented by distributed photovoltaics (PV), massive flexible regulation resources are urgently needed to reduce the adverse effects of its volatility and uncertainty on the secure and stable operation of power systems. Due to the advantages of high energy density and flexible configuration, lithium batteries have been used in PV consumption. However, because of the expensive cost and unsafety of lithium batteries, it is difficult to implement them widely within a few years. A virtual energy storage (VES) modeling method and control strategy for distributed PV consumption are proposed by utilizing the inverter air conditioner with flexible power regulation ability. Firstly, based on the established equivalent thermal parameter model of the room and the operating model of the inverter air conditioner, a modeling method of the VES charging and discharging processes is proposed. Subsequently, in order to realize the statues perception and regulation capacity evaluation of the VES, several evaluation indexes of VES are proposed. On this basis, aiming at distributed PV consumption, a VES control strategy considering charge and discharge rate constraints is proposed. Finally, using the actual PV output data, the process of VES participating in PV fluctuation stabilization is simulated, and the effect of VES with different capacities is compared. The results show that the VES constructed by the inverter air conditioner can effectively damping the photovoltaic fluctuation and promote the grid friendly integration of renewable energies.

Abstract:Since the reform and opening up, Sino?foreign cooperation in running schools has become an important part of China's education system. International curriculum is the carrier and the specific form of cooperative running schools. The quality of curriculum teaching directly affects the quality of talent training, which is the top priority in Sino?foreign cooperative running schools. Online course teaching is one of the important teaching methods for Chinese?foreign cooperative schools. Due to the differences between Chinese and foreign teaching modes, there are some problems to be solved for course teaching in video teaching, language communication, teacher team, ideological and political courses, and assessment methods. For this purpose, taking the course "Power System Foundation" as an example, it puts forward the specific countermeasures of teaching team construction, teaching resource optimization, curriculum structure optimization, teaching mode innovation, curriculum integration of ideology and politics, and the application of modern science and technology. The implementation of these countermeasures is conducive to the improvement of course teaching quality and achievement of good teaching performance. Through teaching reformation, 74% of the students in the international class of Electrical Engineering in Grade 2019 have achieved excellent results in the comprehensive evaluation mechanism, which proves that the students have a good grasp of the course "Power System Foundation", and the course teaching reform has achieved preliminary progress.

Abstract:In power systems, the high?voltage circuit breakers are routinely deployed to switch capacitor banks, thus optimizing reactive power compensation, enhancing power factor, minimizing power losses, stabilizing voltage, and elevating power supply quality. Consequently, the capacitive current switching experiment plays a pivotal role in assessing the effectiveness of high?voltage circuit breakers. In laboratory settings, synthetic circuits are commonly employed to evaluate the capacitive current switching performance of these circuit breakers. This article introduces a novel synthesis loop for capacitive current switching, along with its corresponding operation sequence control system. This synthetic loop capitalizes on the phase angle characteristics inherent in three?phase AC power supplies, enabling direct synchronization of voltage and current sources without the complexity of additional synchronization devices. This simplifies the operations and enhances the reliability. The timing control system for the loop comprises an upper computer software operating platform based on LabVIEW and a lower computer control unit based on Arduino. This setup achieves intelligent and visual control. The system design prioritizes resistance against electromagnetic interference and employs photoelectric technology for signal transmission, mitigating potential disruptions caused by high?voltage and high?current signals on control signals. Following rigorous testing, the entire control system demonstrated remarkable performance under high?pressure conditions. This experimental teaching approach not only equips students with an good understanding of capacitive switching synthesis loop control schemes but also instills a comprehensive grasp of timing control principles. Moreover, it fosters practical skills in hands?on operation and proficiency in engineering software applications, providing a solid foundation for future roles in related positions.